bank indonesia, financial stability review no.9 september 2007

The Financial Stability Review (FSR) is one of the avenues through which

Bank Indonesia achieves its mission ≈to safeguard the stability of the Indonesian Rupiah by

maintaining monetary and financial system stability for sustainable national economic

developmentΔ.

Published by:

Bank Indonesia

Jl. MH Thamrin No.2, Jakarta

Indonesia

This edition was launched in September 2007 and is based on data and information available as of June 2007, except where

stated otherwise.

The complete Financial Stability Review is available for download in PDF format from Bank Indonesia»s website : http://www.bi.go.id

Any inquiries, comments and feedback please contact:

Bank Indonesia

Directorate of Banking Research and Regulation

Financial System Stability Bureau

Jl.MH Thamrin No.2, Jakarta, Indonesia

Phone : (+62-21) 381 8902, 381 8336

Fax : (+62-21) 351 8629

Email : [email protected]

FSR is published biannually with the objectives:

To foster public awareness regarding domestic and global financial system stability issues;

To analyze potential risks confronting the domestic financial system;

To evaluate progress and issues related to financial system stability; and

To recommend policies to relevant authorities for promoting a stable financial system.

Financial Stability ReviewI - 2007( No. 9, September 2007 )

iii

Foreword vi

Overview 3

Chapter 1 Macroeconomic Conditions and

the Real Sector 9

Macroeconomic Conditions 9

Conditions in the Real Sector 12

Box 1.1. Potential Impacts of the US Subprime

Mortgage Crisis on the Domestic Financial

Market 15

Chapter 2 The Financial Sector 19

Financial Sector Structure 19

Banks 20

Funding and Liquidity Risk 20

Credit Growth and Credit Risk 22

Market Risk 27

Profitability and Capital 29

Non-bank Financial Institutions and

the Capital Market 31

Finance Companies 31

Capital Markets 33

Box 2.1. Financial Deepening In Indonesia 38

Box 2.2. Capital Inflows and Sudden Reversal:

Are We Ready to Face a Crisis? 40

Table of Contents

Chapter 3 Prospects of the Indonesian Financial

System 45

Economic Prospects and Risk Perception 45

Bank Risk Profile: Level and Direction 46

Prospects of the Indonesian Financial System 47

Potential Vulnerabilities 48

Box 3.1. Financial Stability Index and Probability

of Default 50

Chapter 4 Financial Infrastructure and Risk

Mitigation 55

Payment System 55

Payment System Policy and Risk Mitigation 57

Financial Sector Safety Net (FSSN) 58

Financial System Stability Forum (FSSF) 59

Articles

Article 1 The Dynamics of Banking Industrial Structure,

Strategic Risk, and Their Implications on

Financial Systems Stability 63

Article 2 Credit Risk Modelling:

Rating Transition Matrices 77

iv

List of Tables and Figures

Table

1.1 World Economic Indicator (Volume) 9

2.1 Selected Regional Price Index Performance 33

2.2 Sectoral Price Index Performance 34

2.3 Performance of Stocks Market Efficiency 35

3.1 Concencus Forecast of Selected Economic

Indicators 45

3.2 Indonesian Risk Perception 45

3.3 Impacts of Exchange Rate to Conglomeration

Equity 48

4.1 BI-RTGS Settlement Performance (in Value and

Volume) 55

4.2 Card Based Payment Transaction 56

4.3 Structure and Membership of Financial System

Stability Forum (FSSF) 59

Table Box:Table Box:Table Box:Table Box:Table Box:

2.1.1 Indonesia Financial Deepening Performance 38

2.1.2 Indonesia Financial Deepening Performance 38

2.1.3 Indonesia Real Rates of Return 39

1.1 Interest Rate Performance 9

1.2 GDP Performance 10

1.3 Non Oil and Gas Exports 10

1.4 Non Oil and Gas Imports 10

1.5 Composite Index 10

1.6 Indonesia and US Real Interest Rate 10

1.7 Exchange Rate of Selected World Currencies 11

1.8 Rupiah Exchange Rate against USD 11

1.9 World Oil Price 11

1.10 World Commodities Price 12

1.11 Interest Rate and Inflation 12

1.12 Consumer Loans 12

1.13 Consumer Expectation 12

1.14 Financial Performance of Non Financial Public

Listed Companies 13

1.15 NPL of Working Capital and Investment Loans 13

1.16 Corporates Financing and Expansion 13

1.17 Unemployment Rate 14

1.18 Output Gap Estimation 14

1.19 DER and Debt/TA Performance 14

1.20 Net Foreign Transaction: Stocks and Government

Bonds 14

2.1 Assets of Financial Institutions 19

2.2 Structure of Funding and Bank Placements 20

2.3 Bank Liquid Asset Ratio 20

2.4 Average Interbank Money Market Interest Rate 20

2.5 Deposits Performance 21

2.6 Deposits Performance Based on Exchange Rate 21

2.7 Foreign Exchange Deposits Performance 21

2.8 Time Deposits Growth 21

2.9 Deposits Structure 22

2.10 Deposits Performance (Related to Guarantee) 22

2.11 Loans Growth 22

2.12 Share of Earning Assets 23

Graph

v

2.13 Loans Growth by Type 23

2.14 Share of Loans by Type 23

2.15 Share of Loans by Economic Sector 24

2.16 Non Performing Loans (NPL) 24

2.17 NPL Value Performance 24

2.18 Gross NPL Performance by Bank Group 24

2.19 Gross NPL by Economic Sector 25

2.20 Share of NPL by Economic Sector 25

2.21 Share of NPL by Loans Type 25

2.22 Performance of Consumer Loans NPL 26

2.23 NPL Gross Performance 26

2.24 NPL Value of Corporate and MSME 26

2.25 NPL Gross of Corporate and MSME Loans 26

2.26 Foreign Exchange Rate and Foreign Exchange

NPL 27

2.27 Performance of Foreign Exchange Gross NPL 27

2.28 Loans, NPL and APLL 27

2.29 Interest Rate and Exchange Rate Performance 28

2.30 Loans Interest Rate by Bank Group 28

2.31 Rupiah Maturity Profile 28

2.32 Foreign Exchange Maturity Profile 28

2.33 NOP Performance (Overall) 29

2.34 Government Bonds in Bank Portfolio 29

2.35 NII Performance 30

2.36 Profit and Assets Performance 30

2.37 Structure of Bank Interest Income 30

2.38 Risk Weighted Assets, Capital, and CAR 31

2.39 Tier 1 Capital to Risk Weighted Assets Ratio

and CAR 31

2.40 Operational Activities of Finance Companies 31

2.41 Finance Companies 32

2.42 Source of Fund of Domestic Private Finance

Companies 32

2.43 Source of Fund of Joint Venture Finance

Companies 32

2.44 Net Cash Flow of Finance Companies 32

2.45 Capital Inflows in Government Bonds, SBI

and Stocks 33

2.46 Regional Index Performance 34

2.47 Sectoral Index Performance 34

2.48 Stocks Transaction of Domestic and Foreign

Investor 34

2.49 Value of Capitalization and IPO 35

2.50 Performance of Selected Government Bonds

Price 35

2.51 Distribution of Government Bonds by Maturity 36

2.52 Ownership of Government Bonds 36

2.53 Yield of 20-year Government Bonds of Selected

Countries 36

2.54 Comparison of Financial Asset Price Volatility 36

2.55 IPO and Position of Corporate Bonds 55

2.56 Mutual Funds by Type 55

3.1 Yield Curve 45

3.2 Risk Profile of Banking Industry and Its Direction 46

3.3 Financial Stability Index 47

3.4 Non Financial Public Listed Companies Probability

of Default 48

4.1 Activities of Payment System Transaction

Semester I 2007 55

Graph Box :

1.1.1 Residential Price Index of Selected Countries 15

1.1.2 Delinquency Rate of SPM 15

1.1.3 Foreclosure Rate of SPM 16

2.2.1. Pre and Post Crisis of NPL & CAR 41

2.2.2. Foreign Exchange Loans Performance 41

3.1.1. Probability of Default - Barrier Option Methods 51

3.1.2. Probability of Default - Common Option

Methods 51

vi

By expressing thanks and praise to God Almighty, we are pleased to publish the Financial Stability Review (FSR)

No. 9, September 2007. This review is deemed timely and pertinent considering the recent dynamism of global financial

markets experiencing high volatility and uncertainty, suggesting that the subsequent effects on domestic financial

stability need to be well understood. This review will help reinforce the importance of preserving financial stability

amidst closer correlation between the global market and the domestic financial market.

As with previous reviews, this edition will detail Indonesian financial system stability, sources of volatility and the

risks faced, steps to mitigate the risks, as well as an outlook for the financial system. In addition, this review will draw

focus on numerous recent developments such as the subprime mortgage crisis and the possibility of a sudden reversal,

which could trigger a crisis similar to that which took place exactly 10 years ago. In the context of maintaining financial

system stability, this review will also outline the importance of prioritizing financial deepening in Indonesia.

By elaborating on the above issues, this review is expected to become an invaluable input to business players in

the financial market, government officials, academics and economic analysts. This review is also expected to attract the

attention of all related parties to proactively collaborate in preserving financial system stability. Without a stable and

well maintained financial system, it would be difficult to bolster economic growth, reduce unemployment and alleviate

poverty.

Finally, on behalf of the Board of Governors, I would like to extend my gratitude and appreciation to all parties

who have directly and indirectly contributed to this review. May the fruits of this hard work prove useful in maintaining

sustainable financial system stability.

DEPUTY GOVERNOR

BANK INDONESIA

Muliaman D. Hadad Muliaman D. Hadad Muliaman D. Hadad Muliaman D. Hadad Muliaman D. Hadad

Foreword

1

Overview

Overview

2

Overview

This page is intentionally blank

3

Overview

Indonesian financial system stability in the first semester of 2007 was well

maintained and future prospects remain positive. A possible sudden reversal

of short term capital flows, contagion stemming from the subprime

mortgage crisis that recently affected several other countries and a potential

surge in capital outflows did not undermine Indonesian financial system

resilience. Monetary stability and an improving domestic economy, as well

as well-controlled pressures originating from the international economy,

all served to maintain financial system stability. Taken holistically, such

conditions proved favorable for improvements in financial sector

performance. Banks continued to dominate the financial sector and

remained liquid with maintained productive asset quality, rising profitability

and strong capital. However, continuous anticipative measures as well as

comprehensive risk mitigation are imperative due to the prevailing sources

of instability.

Overview

1. SOURCES OF INSTABILITY

1.1. Fluctuations in the external environment

Greater integration between the domestic and global

economies has left Indonesia prone to fluctuations in the

external environment; both economic and non economic.

The fluctuations stemmed from the presence of global

imbalances, the soaring global oil price and excess global

liquidity, which encouraged short term capital flows.

Fluctuations also arose from the contagion effects of

specific problems in a particular business sector, namely

the subprime mortgage crisis affecting several countries

recently.

1.2. High dependence on banks

Banks continue to dominate Indonesia»s financial

sector, which resulted in high dependency on banks as

the funding source for development and the economy.

Such high reliance on banks can trigger volatility because

fluctuations can quickly spread and subsequently disrupt

financial system stability. Consequently, all types of risk

that can jeopardize banks must be accurately mitigated to

prevent any escalation and therefore become a source of

instability. One source of instability that requires close

monitoring is the possibility of higher credit risk due to

partially unresolved credit restructuring, the as yet sub-

4

Overview

optimal implementation of credit risk management and

some weaknesses in the bank credit management

information system.

1.3. Constraints in the real sector

The real sector remains beset by several constraints,

namely workforce issues and infrastructure limitations. In

addition, the costs borne by the business community also

remains high undermining further expansion. If such

conditions persist, many companies may decide to

discontinue conducting business in Indonesia and relocate

abroad. As a result, banks would continue to experience

excess liquidity in line with the limited demand for credit

from the business sector, resulting in the ineffective

intermediary function of banks. Therefore, overcoming

constraints in the real sector is a crucial step in establishing

financial system stability.

1.4. Credit concentration on consumer financing

Another important source of instability is the

concentration of bank credit on consumer financing.

Among others, this is indicated by the rise in loans for

credit cards, motor vehicles and housing. Credit risk may

arise should household income be insufficient to repay

outstanding liabilities to the banks.

2. RISK MITIGATION

To minimize instability and mitigate the high risk

faced by the Indonesian financial sector, the following

measures have been taken:

2.1. Strengthen Bank Risk Management

Bank risk management is continually strengthened

due to the more complex and dynamic nature of business

activity in line with advancements in information

technology and a more competitive business environment.

The ongoing risk management certification program and

the implementation of Basel II commencing in 2008 are

expected to improve the quality of bank risk management.

Additionally, the bank credit management information

system is continuously upgraded to improve risk

management effectiveness. From a supervisory side, the

risk based supervision approach continues to be developed

and implemented. This is supported by advances in

supervisor capacity and skill level through the bank

supervisory certification program.

2.2 Bolster Financial Infrastructure

Financial infrastructure can be strengthened in two

ways. First is through the development of a secure and

reliable payment system by Bank Indonesia. Second is

through a sound financial sector safety net (FSSN) coupled

with crisis management based on joint efforts between

Bank Indonesia and the Government. Risk of instability in

the financial sector can be minimized through a robust

financial infrastructure.

2.3. Improving the Effectiveness of Financial

System Surveillance

Risk mitigation in the financial sector is only effective

if financial system performance is continuously monitored.

Improvements in surveillance effectiveness are ongoing and

utilize various methodologies and stress testing to gauge

the risks and improve resilience in the financial system.

Discussions with market players, academicians and

observers are held regularly to further sharpen financial

sector analysis and oversight.

2.4. Financial Deepening

Financial deepening has become a dominant issue

in order to reduce, among others, dependence on the

banking sector. Financial deepening can spur growth in

5

Overview

non bank financial business activities, provide more

options for financial instruments as well as improve the

accessibility of financial products and services to the

underprivileged.

3. OUTLOOK FOR FINANCIAL SYSTEM STABILITY

In general, financial system risk in the first semester

of 2007 remained relatively steady and under control in

line with monetary stability and improving economic

conditions. A possible sudden reversal of short term capital

flows, contagion stemming from the subprime mortgage

crisis that recently affected several other countries and a

potential surge in capital outflows did not undermine

Indonesian financial system resilience. Looking ahead,

sluggish global economic growth, the soaring global oil

price and short term capital inflows may weaken

Indonesian financial system resilience. Subsequent

measures taken by the relevant monetary authorities and

banks within the affected countries to resolve the subprime

mortgage crisis may also influence Indonesian financial

system stability.

Internally, self awareness is essential regarding the

possible effects of the upcoming general election on

business activities and risk in the financial sector. In

particular, unfavorable security conditions could trigger

capital outflows. Vigilance is also necessary because banks,

which dominate the financial sector, will shortly face

numerous challenges, among others, the resolution credit

restructuring, improving bank risk management and credit

management information system, as well as

synchronization between efforts to improve intermediation

and reduce credit risk. In addition, banks will continue to

develop contingency plans to decrease operational risk,

improve internal control effectiveness and corporate

governance, as well as meet the minimum core capital

requirement of Rp80 billion by the end of 2007 and Rp100

billion by the end of 2010.

Meanwhile, stress testing to measure credit risk,

liquidity risk and market risk has indicated that banks are

adequately resilient to numerous shocks stemming from

fluctuations in macroeconomic variables. The results of

stress tests on a sample of conglomerates/large

corporations that borrowed foreign currency demonstrate

resilience towards fluctuations in exchange rate risk.

Despite the estimation results indicating that the probability

of default (PD) for a sample of non financial public listed

companies with a PD of 0.5 will increase slightly, however,

given the existing provisions and strong bank capital, this

is not expected to trigger instability. Overall, financial sector

prospects remain steady and controllable.

6

Overview


7

Chapter 1 Macroeconomic Conditions and the Real Sector

Chapter 1Macroeconomic Conditionsand the Real Sector

8



9


Macroeconomic Conditionsand the Real Sector

Chapter 1

1.1. MACROECONOMIC CONDITIONS

The global economy was beset by pressures

stemming from a slowdown in economic growth and a

subprime mortgage crisis in the property sector throughout

the first semester of 2007. In order to recover economic

growth in the United States (US), and in particular to

alleviate concerns of high inflation, the Fed decided to

postpone plans to raise the Fed Fund rate; maintaining it

at a level of 5.25%1Ω. This severely affected growth in the

global financial market since the Fed Fund rate was

previously expected to fall.

In line with US economic recovery, the global

economy has shown signs of the adjustments to global

imbalances leading to a soft landing scenario. This is

partially due to an increase in Asian exchange rate flexibility,

greater expenditure by oil producing countries as well as

structural reform in Europe and Japan. Efforts undertaken

to correct the global imbalances have encouraged

economic expansion in emerging countries, including

Indonesia.

Economic expansion continued with maintained macroeconomic stability

throughout the first semester of 2007. External vulnerabilities had no

significant effects on domestic economic stability. Meanwhile, the lower

domestic interest rate began to gradually catalyze activity in the real sector.

However, various business constraints and financing issues restricted real

sector growth.

Graph 1.1Interest Rate Performance

%

0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

20072004 2005 2006200320022001

SIBOR LIBOR

ECB FFR

Jun Dec Jun Dec Jun Dec Jun Dec Jun Dec Jun Dec Jun

World Output 4.9 5.5 5.2 5.2Advanced Economies 2.6 3.1 2.6 2.8United States 3.2 3.3 2.0 2.8Emerging & Developing Countries 7.5 8.1 8.0 7.6

Consumer PriceAdvanced Economies 2.3 2.3 2.0 2.1Emerging & Developing Countries 5.4 5.3 5.7 5.0LIBORUS Dollar Deposit 3.8 5.3 5.4 5.3Euro Deposit 2.2 3.1 3.8 3.7Yen Deposit 0.1 0.4 0.8 1.2

Oil Price (USD) - average 41.3 20.5 (0.8) 7.8

Table 1.1World Economic Indicator (Volume)

Category 2005 2006

%%%%%Projection

2007 2008

Source: World Economic Outlook - IMF July 2007

1 In the latest development, on 18th September 2007 the Fed cut its Fed Fund rate by50 bps to 4.75%.

10


Throughout semester I 2007, the Indonesian

economy continued to grow aided by a managed inflation

rate. In the first quarter of 2007, economic growth

reached 6.0% (y-o-y), rising to 6.1% (y-o-y) in quarter II.

This surpassed growth in the same quarter of the previous

year, which reached just 5.0% (y-o-y). Increases in private

consumption and exports, particularly exports from the

manufacturing sector, coupled with improvements in

Graph 1.4Non Oil and Gas Imports

Millions of USD

Agriculture, Hunting and Fishing Mining and Quarrying

Manufacturing Total

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2006 2007Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

public purchasing power and high global demand

supported economic growth through quarter II 2007. The

current account is estimated to run a surplus of USD1.2

billion, which will bolster the Indonesian balance of

payments (BoP) with a surplus totaling USD3.7 billion.

With such favorable developments, Indonesia»s foreign

exchange reserves reached USD50.9 billion at the end of

June 2007.

Meanwhile, unstable US economic conditions and

the decision to postpone plans to raise the Fed Fund rate

have diverted investment towards emerging markets where

economic growth has improved offering higher returns.

Hikes in the price indices of domestic, regional and

international capital markets indicated rapid foreign capital

inflows. In semester I 2007, the regional South East Asian

financial market was more bullish than the previous

semester.

Graph 1.3Non Oil and Gas Exports

Agriculture, Hunting and Fishing Mining and Quarrying

Manufacturing Total

Millions of USD

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

2006 2007Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May JunJan

Graph 1.6Indonesia and US Real Interest Rate

%

2003 20072004 2005 2006

(8.00)

(6.00)

(4.00)

(2.00)

-

2.00

4.00

6.00

Indonesia U S

Jun Dec Jun Dec Jun Dec Jun Dec Jun

Graph 1.2GDP Performance

%

2.00

4.00

6.00

8.00

-

4.12

5.22

4.39

5.87

6.30

4.96

Q-I Q-II Q-III Q-IV Q-I Q-II Q-III Q-IVQ-I Q-II Q-III Q-IV Q-I Q-II Q-III Q-IVQ-I Q-II Q-III Q-IV Q-I Q-II

2002 2003 2004 2005 2006 2007

Graph 1.5Composite Index

0

500

1,000

1,500

2,000

2,500

2003 20072004 2005 2006Jun Dec Jun Dec Jun Dec Jun Dec Jun

11


A surplus of funds in several countries has

encouraged growth in hedge fund activities of greater

volume. Meanwhile, the low interest rate in Japan triggered

carry trades by global market players utilizing low-yielding

fund sources in Japan to invest in other high-yielding

currencies in search of profit spread. Carry trades are

generally short term and, therefore, price volatility

remained high in global and regional financial markets,

including Indonesia. The rise in capital inflows positively

affected the exchange rates of emerging market countries,

including Indonesia. On the contrary, carry trades

weakened the Japanese yen. In May 2007, the rupiah was

strong against the US dollar, reaching Rp8,670.-.

Since the end of July 2007, however, the exchange

rates of several Asian currencies have tended to fluctuate

and weaken, despite remaining manageable. Unfavorable

developments in the global and regional financial markets

that precipitated a drop in the domestic financial market

spurred slight rupiah depreciation. The slump in subprime

mortgages in the US that stemmed from subprime loans

triggered negative sentiment and provoked foreign

investors to reduce their high risk financial instrument

portfolio in emerging markets like Indonesia and switch

to dollar denominated assets (see Box 1.1). Such behavior

sparked subsequent capital outflows that contributed to

rupiah depreciation against the US dollar. The rising global

oil price further weakened the rupiah exchange rate.

Nevertheless, an expanding BoP surplus, burgeoning

foreign exchange reserves, maintained risk exposure from

the exchange rate and strong resilience from the fiscal

side have indicated robust economic fundamentals.

Furthermore, in semester I 2007, an improvement in

Indonesia»s debt rating outlook, from steady to positive,

awarded by the international ratings agencies (Fitch Ratings

and Moody»s) together with a decline in premi swap and

the stable yield spread showed effective management of

the domestic risk factors. Taken as a whole, this drove

positive sentiment towards Indonesia shielding the

domestic economy and financial sector from pressures

emanating from escalating global financial market volatility.

Upcoming market risk pressure is expected to be

significant, particularly that stemming from global and

Graph 1.8Rupiah Exchange Rate against USD

Rp/USD

FFR 5% ( May 10, 2006)BI-Rate 12.50% ( May 9, 2006)

2005Jan Apr Jul Aug DecOct Feb May Mar Jun

2006 2007

7,000

7,500

8,000

8,500

9,000

9,500

10,000

10,500

11,000

11,500

12,000

The implementation ofnew fuel price &2nd Bali bomb (Oct 1, 2005)

- Katrina Hurricane in New Orleans (Aug 29, 2005)- World oil price USD69.81/barrel (Aug 30, 2005)

Graph 1.9World Oil Price

USD/barrel

20.00

30.00

40.00

50.00

60.00

70.00

80.00

2003 20072004 2005 2006

6-month Future

WTI Spot Price

3-month Future


Graph 1.7Exchange Rate of Selected World Currencies

96

98

100

102

104

106

108

110

Jan Feb Mar Apr May Jun

SGD THBPHP KRWEUR JPY

IDR

12


regional financial market volatility. The potential for further

global oil price hikes and inflationary pressures due to

soaring global commodity prices remains. Vigilance is

therefore required to avoid pressures on financial system

stability.

1.2. CONDITIONS IN THE REAL SECTOR

Improving macroeconomic indicators have instilled

confidence in the achievement of future inflation targets

and enabled Bank Indonesia to reduce the BI-Rate to a

level of 8.25% at the end of semester I 2007. The reduction

in the BI-Rate met the expectations of market players and,

therefore, encouraged positive sentiment regarding the

rupiah. Amidst a steady and gradually improving rupiah

exchange rate, the lower BI-Rate was subsequently

followed by other domestic interest rates, in particular the

1-month term deposit interest rate, which at the end of

June 2007 was at 7.46%; representing a decline of 150

bps compared to its position at the end of December 2006.

Meanwhile, during the same period, the interest rates for

working capital, investment and consumption loans fell,

albeit with slightly less momentum, namely 119 bps, 111

bps and 67 bps respectively, to 13.88%, 13.99% and

16.91%.

Despite the limited transmission of the BI-Rate decline

to lending rates, the prevailing trend of lower interest rates

and improving macroeconomic indicators drove customer

demand and restored optimism concerning supply.

From the demand side, although limited, private

consumption followed an increasing trend and continues

in the expansion phase. As a result of the reduction in

interest rates, stronger public purchasing power has

boosted private consumption. This is apparent from the

Graph 1.10World Commodities Price

USD

0

50

100

150

200

250

300

20072004 2005 20062003

Plywood

Crude Palm Oil

Rice


Graph 1.11Interest Rate and Inflation

2003 20072004 2005 2006

1-month Time Deposits

Interest Rate of Working Capital Loans

Interest Rate of Investment Loans

Interest Rate of Consumer Loans Inflation

0

5

10

15

20

25%

BI-Rate

1-month SBI


Graph 1.12Consumer Loans

%

Growth of Consumer Loans

NPL (right axis)

7

12

17

22

27

32

37

42

47

52

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

20072004 2005 2006Jun Dec Jun Dec Jun Dec May

%

Graph 1.13Consumer Expectation

0

20

40

60

80

100

120

140

160

180

2003 20072004 2005 2006

IncomeEconomyEmployment Rate

MayJun Dec Jun Dec Jun Dec Jun Dec

13


acceleration in consumption credit growth in the wake of

sustaining heavy pressures in October 2005 due to the

hikes in fuel prices. In addition, several economic indicators

also point to an increase in private consumption along

with higher expectations for income, the economy and

job availability.

From the supply side, in line with favorable

macroeconomic conditions the improving financial

performance of non financial public listed companies,

which began in December 2006, is expected to continue.

This is, amongst others, marked by increasing business

profitability (ROE) and maintained leverage.

Better corporate performance imbued positive

impacts on the financial sector as it strengthened credit

repayment ability. Therefore, it was not surprising that the

value and percentage of non-performing loans (NPL) for

Current Ratio 1.27 1.21

ROA (Return on Assets) 0.06 0.06

ROE (Return on Equity) 0.15 0.17

ITO (Inventory Turn Over) 0.18 0.16

Sales to Total Assets 0.87 0.78

DER (Debt Equity Ratio) 1.53 1.52

Graph 1.14Financial Performance of

Non Financial Public Listed Companies

Current Ratio

ROA

ROE

I T O

Sales toTotal Assets

DER

2005

2006

-

5.0

10.0

15.0

20.0

Note:Nearer to the centre, the lower the risk

2005

%%%%%

2006

Graph 1.15NPL of Working Capital and Investment Loans

%

0

2

4

6

8

10

12

14

16

18

2003 2004 2005 2006 2007

Investment Loans

Working Capital Loans


working capital credit and investment credit drop in

semester I 2007.

However, robust corporate sector performance failed

to spur adequate business expansion. In general, the

corporate sector remains beset by numerous constraints

to business expansion, such as labor issues and poor

infrastructure. Meanwhile, persistently high business costs

forced companies to utilize internal fund sources rather

than credit from banks to finance business expansion. This

also explains why bank credit expansion stayed below

expectations.

The tendency of utilizing internal fund sources was

also evident from the relatively high capital to total assets

ratio for public listed companies. On one hand such a

tendency illustrates an improvement in corporate financial

conditions since the company no longer depends on debt

financing. However, on the other hand such an inclination

Graph 1.16Corporates Financing and Expansion

-0.5

0.0

0.5

1.0

1.5

2.0

0.0

0.1

0.2

0.3

0.4

0.5

0.6

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Sales GrowthGrowth of Go Public Corporation Assets

Self Financing (right axis)

14


potentially restricts a company from fully expanding due

to limited internal funds. Consequently, new job creation

can be limited, thus undermining prevailing efforts to

reduce unemployment.

Graph 1.19DER and Debt/TA Performance

%

0

10

20

30

40

50

60

70

80

90

-

1

2

3

4

5

6

7

8

9

2003 2004 2005 2006

Total Liabilities/Total Assets

Debt to Equity Ratio (right axis)

Graph 1.20Net Foreign Transaction: Stocks and Government Bonds

-4.00

-2.00

0.00

2.00

4.00

6.00

8.00

10.00

2005 2006Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Trillions of Rp Rp/USD

8,200

8,400

8,600

8,800

9,000

9,200

9,400

9,600

9,800

10,000

Stocks Government Bonds Exchange Rate

particularly the corporate sector. Furthermore, the debt-

to-equity ratio of non financial public listed companies has

tended to slide, despite the strong capital inflows. This is

evidence that capital inflows have not been absorbed by

the corporate sector, especially those that have listed in

the capital market. The capital inflows are short term in

nature and are predominantly in the form of financial

instruments such as shares, government bonds (SUN) and

Bank Indonesia»s Certificates (SBI).

In the future, support from various relevant parties

to overcome the constraints in the real sector is required

to enable more dynamic economic growth. As a result,

improving macroeconomic indicators would truly reflect

developments in the real sector. This will boost the resilience

of the economy and domestic financial sector against

external vulnerabilities.

Graph 1.18Output Gap Estimation

1996

Output gap periode iscloser to zero

Output gap accelerationto zero

became slower down

Output GapAccelerated Output Gap

0.1

0.05

0

-0.05

-0.1

-0.10

-0.5

-0.0

-0.01

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Q- I I Q- IV Q- I I Q- IV Q- I I Q- IVQ- I I Q- IV Q- I I Q- IV Q- I I Q- IV Q- I I Q- IV Q- I I Q- IV Q- I I Q- IV Q- I I Q- IVQ- I I Q- IV Q- I I Q- IV Q- I I Q- IV

Graph 1.17Unemployment Rate

%

0

2

4

6

8

10

12

Feb Aug Feb2001 2002 2003 2004 2005 2006 2007

Source : BPS-Statistic Indonesia

In addition, despite the lower interest rate, bank

credit is yet to be fully utilized by business players.

Indications remain that business players await the

investment climate improvement package, through

ratification of the Draft Capital Investment Act (RUU-PM),

and the acceleration of infrastructure development. This

suggests that Indonesia»s resurgent economy is actually

growing below potential.

Strong capital inflows to Indonesia in semester I 2007

are yet to significantly impact real sector funding,

15


Potential Impacts of the US Subprime Mortgage Crisis on theDomestic Financial Market

Box 1.1

Subprime mortgage loans (SPM) is the practice

of extending credits to borrowers who do not meet

acceptable standards for granting loans because of their

deficient credit history; or are considered high risk.

Applicable schemes include Fixed Rate Mortgages (FRM)

and Adjustable Rate Mortgages (ARM). However, the

majority of SPM are adjustable rate, namely mortgage

loans with an adjustable interest rate after a given

period according to the level of risk in the market.

SPM have boomed in the United States since

2003. The demand for subprime mortgages soared in

line with the rapid development of the US real estate

sector at the time. Financial institutions utilized this

business opportunity and developed securitization for

SPM. Rapid securitization supported development of

the SPM secondary market, both in developed countries

and in emerging countries such as Latin America,

Eastern Europe, and Asia (excluding Indonesia). In

addition, numerous SPM-based derivative instruments

also became popular among investors. Rapid growth

in the SPM secondary market drove demand for SPM

from property investors in order to purchase property

for resale to get high returns from the property boom.

Furthermore, the ongoing property boom in the US at

the time obscured creditor perception of SPM credit

risk. Consequently, creditors tended to perceive SPM

credit risk as relatively low due to strong expectations

of uninterrupted soaring property prices, which would

cover the loans.

However, in mid 2006 the US property market

peaked, followed by a drop in property prices.

Circumstances then became less desirable as pressures

stemming from US economic downturn weakened the

repayment ability of SPM debtors, in turn, triggering

non-performing loans. In addition, other countries that

also adopted the SPM business model experienced

similar problems.

Conversely, the lackluster property market swung

SPM creditor expectations of risk. To mitigate the

higher risk, creditors adjusted the interest rate of SPM

and set a far higher margin. This subsequently

exacerbated the repayment ability of debtors pushing

the ARM delinquency rate from 14.5% (December

2006) to 16% (March 2007), despite the FRM

delinquency rate remained relatively steady at 10%.

A rise in the foreclosure rate for both SPM owed

for up to 90 days (starting delinquency) and SPM owed

for more than 90 days (serious delinquency) also

indicated a decline in SPM performance. The

Graph Box 1.1.1Residential Price Index of Selected Countries

US UK Australia South Korea Hongkong Thailand

Q-IV Q-I I I Q-I I Q-I Q-IV Q-I I I Q-I I Q-I Q-IV Q-I I I Q-I I Q-I Q-IV Q-I I I Q-I I Q-I Q-IV Q-I I I Q-I I Q-I

92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07

%

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

Graph Box 1.1.2Delinquency Rate of SPM

0

2

4

6

8

10

12

14

16

18Total Sub-prime FRM ARM

2002 2003 2004 2005 2006 2007

%

Q - I Q - III Q - I Q - III Q - I Q - III Q - I Q - III Q - I Q - III Q - I

16


foreclosure rate for starting delinquency and serious

delinquency rose respectively from 2.0% and 7.8%

(December 2006) to 2.4% and 8.3% (March 2007).

Declining SPM credit quality had widespread

impacts, with the biggest losses reported primarily

by investors in the secondary market for SPM-based

securities and their derivatives. Massive and

simultaneous redemptions were the result of negative

sentiment attributable to the rising delinquency rate

and soaring SPM foreclosure rate. Accordingly, a

number of hedge funds were liquidated, triggering

a rise in the requirement for liquidity in the global

market.

Losses are predicted to be relatively smaller and

insignificant in emerging market countries due to

the comparatively low exposure to financial

institutions that have SPM-based instruments. The

relatively robust capital position of financial

institutions in several emerging market countries also

alleviated prevailing concerns. However, the larger

requirement for liquidity in the global market due to

pressures emanating from the SPM crisis has

undermined financial market performance in emerging

markets that are predominantly supported by capital

inflows.

Global market liquidity pressures have triggered

capital outflows in the Indonesian financial market,

as reflected by a drop in the number of foreign

investors, particularly in SUN and SBI. The already high

yields received from SUN and SBI investment coupled

with the declining profit potential of SUN investment

has encouraged foreign investors to realize the profits

and divest to the global market, especially dollar-

denominated assets. Such conditions have recently

weakened the rupiah against the US dollar.

Meanwhile, available data show that Indonesian

banks do not directly invest in SPM, hence, the direct

impacts of the SPM crisis has been avoided. This is

specifically attributable to prevailing bank regulations

that mandate banks to categorize investment in non-

investment grade securities as non-performing.

However, losses could arise from the sale of SUN by

foreign investors, leading to a drop in the SUN price.

Nevertheless, any subsequent losses affecting banks

with a SUN portfolio will be minor because the

expected drop in SUN price stemming from the SPM

crisis is small (around 2% throughout July-August

2007). In addition, based on the results of stress

testing, a fall of 20% or more in the SUN price would

be necessary to affect bank capital (CAR). As such,

the SPM crisis did not disrupt financial system stability

in Indonesia.

Graph Box 1.1.3Foreclosure Rate of SPM

Serious Delinquency

0

2

4

6

8

10

12

14

%

2002 2003 2004 2005 2006 2007

Total Sub-prime Starting Delinquency

Q - I Q - III Q - I Q - III Q - I Q - III Q - I Q - III Q - I Q - III Q - I

17

Chapter 2 The Financial Sector

Chapter 2The Financial Sector

18



19


The Financial SectorChapter 2

2.1 FINANCIAL SECTOR STRUCTURE

The Indonesian financial sector comprises of

commercial banks and rural banks, as well as non-bank

financial institutions such as insurance, superannuations,

finance companies, securities and pawnshops. With a

market share of 80% of total assets of the financial system,

banks remain at the forefront of the financial sector. This

reflects the high dependency on banks as a source of

funding for development and the economy. In addition,

the major banks continued to dominate the banking

industry, with a market share of about 69% of total bank

assets. As a result, financial system stability in Indonesia is

strongly affected by the risk behavior of the major banks.

Securities companies have witnessed a significant

surge in their market share of total financial system assets;

from 1.0% in 2005 to 3.7% in 2006. Such growth was

balanced by a slight decline in market share for banks and

finance companies. On one hand, the rise can be viewed

as positive since it reduces the dependency on banks.

Conversely, this suggests that better risk-management

capacity should be applied by securities companies, as well

as more stringent oversight by the related supervisory

authority. Therefore, such positive developments would

benefit the economy rather than jeopardize financial system

stability.

Financial system stability was maintained throughout semester I 2007.

Banks, which continued to dominate the financial sector, performed

favorably despite the need to extend more credit. Banks remained liquid

with maintained asset quality. Furthermore, market risk was anticipated

along with high profitability and strong capital. Meanwhile, regardless of

the numerous risks and challenges emanating from external vulnerabilities,

non-bank financial institutions and the capital market showed significant

progress.

Graph 2.1Assets of Financial Institutions

Share of Total Assets of Financial Institution

Sources: BI and Others

0

20

40

60

80

100

2005 2006

80.6 80.1

1.0 3.7

Pawn ShopsSecuritiesCompanies

LeasingCompanies

Pension FundsInsurance Companies

Rural Banks

Commercial Banks

High dependence on banks is one indication that

the financial sector is facing problems with regards to

financial deepening (see Box 2.1). Consequently, financial

20


deepening needs to be prioritized to improve the role of

the financial market in supporting economic development

in Indonesia.

2.2. BANKS

2.2.1. Funding and Liquidity Risk

Deposits remain the largest source of funds for banks

despite a downturn in growth in line with the downward

trend in the interest rate. Deposits, primarily short term,

accounted for 90% of bank funding. Prior experience has

shown that banks are able to manage liquidity. However,

banks should continue to effectively manage the mismatch

in maturity profile to prevent negative effects on bank

liquidity.

Market (PUAB) remained relatively stable suggesting that

banks can maintain sufficient liquidity resilience with low

liquidity risk.

Graph 2.2Structure of Funding and Bank Placements

%

0

20

40

60

80

100

Placement of Funds Source of Funds

Securities

Inter Bank

Borrowing

Deposits

904.0

224.1

342.0

165.086.0

1353.75

13.98118.67

16.81EquityParticipation

Inter Bank

Securities

SBI/Fasbi

Loans

2 Liquid assets comprise of cash and bank placements at BI (i.e. demand deposits at BI, SBIand Fasbi).

3 For Non-Core Deposits (NCD), the assumption is 30% demand deposits and savings plus10% of 3-month term deposits.

Graph 2.4Average Interbank Money Market Interest Rate

%

Afternoon

FX-Onshore

Morning

FX-Offshore

3

6

9

12

2007Jan Mar MayFeb Apr Jun

Liquidity Adequacy

Bank liquidity was relatively well controlled through

semester I 2007. This is evident from the ratio of liquid

assets2 to non-core deposits (NCD)3 , which remained

above 100%, although bank liquidity tended to decline

slightly as shown by the drop in the ratio to 138.9% at

the end of June 2007. The decline was attributable to

the rise in short-term liabilities (6.6%) exceeding the rise

in liquid assets (1.2%). Meanwhile, the Inter-Bank Money

Graph 2.3Bank Liquid Asset Ratio

Liquid Assets NCD Liquid Assets/NCD

Trillions of Rp

0

80

160

240

320

400

60

120

180

Dec Feb MayMar Apr JunJanDec2005 20072006

%

Inter-Bank Money Market (PUAB)

The rupiah PUAB throughout semester I 2007 was

controllable and the interest rate for overnight transactions

(O/N) averaged between 4% and 10%. PUAB tightened

on several occasions, particularly when confronted by the

rather strong appetite for liquidity such as during the

annual tax payments around the end of March. As a result,

the O/N interest rate peaked at 29% on 22nd March 2007.

However, the banks» ability to manage liquidity through

SBI Repo and Fine Tune Expansion (FTE) ensured that PUAB

remained robust.

21


Graph 2.5Deposits Performance

Demand DepositsSaving Deposits

Time Deposits (right axis)

Trillions of Rp

320

340

360

380

Dec Feb Apr JunMayMarJan2006 2007

610

615

620

625

630

Graph 2.6Deposits Performance Based on Exchange Rate

Trillions of Rp

1,050

1,075

1,100

1,125

1,150

Deposits of Rupiah

Deposits of Foreign Exchange(right axis)

180

190

200

210

220

230

2006Des Feb Apr JunJan Mar May

2007

Graph 2.7Foreign Exchange Deposits Performance

in USD

in Rupiah (right axis)

Billions of Rp Trillions of Rp

20

21

22

23

24

25

180

200

220

240

2006Dec Feb Apr JunJan Mar May

2007

Graph 2.8Time Deposits Growth

(m-t-m)

-10

-5

0

5

10

Rupiah Foreign Exchange

2006Jul-Jun Nov-Oct Mar-Feb Jul-Jun

2007

Structure of Deposits

Bank deposits continued to grow throughout

semester I 2007, despite a slowdown, and reached

Rp1,353.7 trillion at the end of June; or 76.4% of total

bank assets. This represents an increase of Rp66.8 trillion

or 5.2% compared to the position at end of the previous

semester. Meanwhile, growth of foreign currency

denominated deposits surpassed rupiah denominated

deposits, leading to their market share of total bank

deposits increasing from 15.0% to 16.5%.

by term deposits, whereas savings accounts reported the

highest growth. Foreign currency deposits grew because

some depositors conjectured that placements in foreign

currency, especially term deposits, were more profitable.

In particular, the rise in savings in foreign currency was

indirectly precipitated by PBI No 9/4/2007 dated 26th March

2007 that rescinded the ban on foreign currency savings.

Although bank deposits have grown, concerns

remain regarding a potential rise in liquidity risk, specifically

stemming from the imbalance in deposit structure, namely

the concentration on short-term funds, the large depositors

and ownership by several individuals. By the end of

semester I 2007, short-term deposits (demand deposit

accounts, savings and 3-month term deposits) reached

93.2% of total deposits. The large depositors with account

values of over Rp100 million covered 78% of total deposits

During semester I 2007, deposits denominated in

foreign currency increased uniformly for all types of

deposits (demand deposits, term deposits and savings) by

USD3.16 billion. In terms of value, demand deposit

accounts recorded the most significant increase followed

22


confidence in banks. Furthermore, the reduction of the

deposit insurance scheme has not resulted in fund

migration or flight to safety.

Meanwhile, the hypothesis that a decline in the

deposit insurance scheme would encourage customers to

split their account into smaller value accounts did not occur.

Since implementation, the number of customer accounts

held at banks has even tended to decline. Moreover, the

percentage of accounts valued below Rp100 million has

also not increased. Taken holistically, there is strong

evidence that the reduction in the deposit insurance

scheme has not triggered any significant negative impacts

on banks, whilst public confidence in the Indonesian

banking system has grown, which is important for financial

system stability.

2.2.2. Credit Growth and Credit Risk

Credit Growth

Despite a relatively low growth rate of 8.5%, the

nominal value of bank loans continued to grow in semester

I 2007; up Rp71.1 trillion compared to the 2006 year-end

position. Nevertheless, the low growth rate exceeded that

in the same period of the previous year (3.7%). Credit

growth (y-o-y) as of June 2007 recorded 19.4%, surpassing

the previous year at 14%. With a relatively small deviation

of Rp0.3 trillion, however, the accomplishment of creditGraph 2.10

Deposits Performance (Related to Guarantee)

Trillions of Rp

0

100

200

300

400

500

600

March 22, 2007 468.8 124.9 99.5 39.4 20.3 84.311 449.840

June 26, 2007 479.7 138.2 103.8 46.9 20.9 86.518 457.632

State-OwnedRegional Dev.

ForeignJoint Venture

SmallPrivate

MiddlePrivate

BigPrivate

Graph 2.11Loans Growth

%

y-o-y

y-t-d

-5

0

5

10

15

20

25

2006 2007

Jan Jun Jan JunFeb Mar Apr May Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jul

Individual(54.8%)

< 3-month(93.2%)

> 100 million(78.0%)

Others(42.5%)

> 3-month(6.8%)

< 100 million(22.0%)

Ownership

Maturity

Nominal

Graph 2.9Deposits Structure

despite constituting just 2.5% of total customer accounts.

In addition, deposits from several individuals still

dominated; accounting for 54.8% of total deposits. This

structure of deposits is vulnerable to sudden withdrawals,

especially by the large depositors and individuals. To reduce

liquidity risk, banks have invested in low-risk liquid assets.

As a result, SBI ownership by banks increased by 12.9% in

semester I 2007.

Impact of the Reduction in the Limited Deposit

Insurance Scheme

Reducing the limited deposit insurance scheme up

to a maximum of Rp100 million per customer per bank on

22nd March 2007 has, hitherto, not significantly affected

bank deposits. In fact, growth in all categories of bank

deposits is evidence and reflects the public»s strong

23


responded by granting more of such loans. The greatest

drop recorded for any lending rate (229 bps) was for

working capital loans; however, it did not spark an increase

in their credit extension. Up to May 2007 working capital

loans followed a decelerating trend but had grown

significantly by June 2007.

Consumption loans continued to dominate credit

growth. In terms of nominal value, working capital loans

remained foremost, totaling Rp31.3 trillion (7.5% growth),

however, consumption loans recorded the highest growth,

namely 10.2% (an increase of Rp23.1 trillion) y-t-d. Based

on the economic sector, consumption loans sought by the

others sector experienced the largest rise, more specifically

by Rp23.1 trillion. Credit for the mining sector experienced

the most impressive growth at 44.9%.

targets by the end of semester I 2007 remained below

those stipulated in the bank business plan. Furthermore,

bank loan to deposit ratio (LDR) rose from 64.7% in

December 2006 to 66.8% in June 2007 due to credit

growth eclipsing deposit growth. Meanwhile, excess

liquidity at banks was generally invested in SBI/Fasbi and

government bonds.

Banks» preference for placements in SBI/Fasbi tended

to decline, demonstrated by a reduction in market share

against total productive assets from 14.0% to 13.7%,

despite the tranche climbing by Rp6.5 trillion in nominal

value. Meanwhile, bank ownership on corporate bonds

increased, albeit remaining relatively low, whereas

government bonds tended to subside. Consequently, this

led to a drop in the share of bonds from 22.0% to 20.8%.

A stronger rupiah in the second quarter of 2007

also affected credit growth, with a decline in credit

extension in foreign currency but a hike in rupiah

denominated loans. However, foreign currency credit

strengthened (9.1%) in June 2007 in line with a drop in

the value of the rupiah.

The lower lending rate did not trigger a direct rise in

all types of bank loans. Over the last 13 months, the fall in

interest rates of investment and consumption loans

reached 174 bps and 43 bps respectively, and banks have

Graph 2.12 Share of Earning Assets

EquityParticipation

Inter Bank

Securities

SBI & Fasbi

Loans

Percent

0

25

50

75

100

Dec Jun

2006 2007

53.5

14.0

22.0

10.10.4 0.4

55.1

13.7

20.8

10.1

Graph 2.13Loans Growth by Type

%

-6

-4

-2

0

2

4

6

8

2006 2007

Jan Jun Jan JunFeb Mar Apr May Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jul

Working Capital Loans Investment Loans Consumer Loans

Graph 2.14Share of Loans by Type

Working Capital Loans Investment Loans Consumer Loans

46%

21%

33%

24


The outlook for bank loans remains positive. Credit

will continue to grow, which is reflected by the increasing

amount of undisbursed loans, especially for working capital

purposes. Meanwhile, approval for investment loans since

quarter II 2007 has risen. The steady ratio of undisbursed

loans against total loans, at around 20%, indicates that

the rise in undisbursed loans is relatively manageable.

Credit Risk

Compared to the position at the end of semester II

2006, the nominal value of non-performing loans dropped

by Rp0.6 trillion (1.0%) to Rp57.5 trillion by the end of

semester I 2007. Meanwhile, the amount of banks

extending loans surged compared to the previous semester.

As a result, the NPL ratio fell from 7.0% to 6.4% (gross)

or from 3.6% to 2.9% (net).

Notwithstanding, the improvement in NPL did not

automatically mean better overall bank credit quality. This

is because the improvement in loans quality primarily

occurred in channeling credit, for which the risk does not

affect banks. In fact, the nominal value of non-performing

loans for non-channeling credit rose by Rp1.7 trillion (3.6%).

Although banks have made provisions for loan losses (PPAP)

to anticipate a surge in NPL, vigilance is still imperative to

prevent any early symptoms of credit risk developing and

subsequently disrupting financial system stability.

Regarding banks, NPL growth tended to vary. Gross

NPL at the large banks dropped from 8.4% to 7.4%,

primarily attributable to improved channeling credit quality

at state-owned banks. Meanwhile, foreign banks

experienced the highest NPL growth in recent years at

43.7% (Rp1.2 trillion) for the reporting period. A rise in

Graph 2.16Non Performing Loans (NPL)

% Trillions of Rp

NPL Gross

NPL Nominal (right axis)

NPL Net

-1

234

567

89

10

1112

2002 2003 2004 2005 2006 2007 Jun25

30

35

40

45

50

55

60

65

70

75

Graph 2.17NPL Value Performance

Sub-standard

Loss (right axis)Doubtful

Total NPL (right axis)

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

22.0

24.0

30.0

35.0

40.0

45.0

50.0

55.0

60.0

65.0

70.0

75.0

2006Jun

2007JunDecMar Sep Mar

Graph 2.18 Gross NPL Performance by Bank Group

2

3

4

5

6

7

8

9

10

11

12

13

June June June June

Big Middle Small Joint Venture Foreign

2004 2005 2006 2007

Graph 2.15Share of Loans by Economic Sector

21%

30%22%

3%

4%5%

11%1% 2% 1%

Trading

Others

Manufacturing

Transportation

Construction

Agribusiness

Business Services

Services

Mining

Electricity

25


loans throughout 2007 failed to raise credit quality. In fact,

consumption loans NPL increased significantly by Rp21.1

trillion (31.7%) during semester I 2007, rising from 2.9%

to 3.5% (gross).

It should be noted that the quality of consumption

loans began to decline in 2000. Since then the market

share of consumption loans NPL against total NPL has

persistently increased, peaking at 17.4% by the end of

semester I 2007; up from 13.7% at the end of the previous

semester. The concentration of loan extension for

consumption purposes must be closely monitored because

if household income is insufficient to repay the bank loans

it will raise credit risk.

NPL nominal value was also experienced by mid-sized

banks, by Rp0.8 trillion or 30.3%.

Credit quality in the industrial sector also improved

in line with more favorable macroeconomic conditions.

This was reflected by a decline in gross NPL from 10.5%

to 10.0%. However, non-performing loans in the industrial

sector require tight monitoring and effective resolution as

they dominate total bank NPL (37.2%) and are prone to

becoming a source of instability.

On the other hand, propitious economic conditions

did not ameliorate credit quality in the others sector

(generally consumption loans) or the trade sector. The

nominal value of NPL in the two sectors grew in semester

I 2007 by Rp2.0 trillion (31.4%) and Rp0.9 trillion (0.9%)

respectively. However, credit risk in the trade sector and

others sector was more manageable compared to the

industrial sector. This was due to: (i) credits in the trade

sector and others sector are generally working capital and

consumption loans with relatively smaller outstanding

credit; (ii) debtors are generally not corporations; and (iii)

nearly all banks have credit portfolios for these sectors,

further diversifying the risk.

Graph 2.19Gross NPL by Economic Sector

0.0 2.0 4.0 6.0 8.0 10.0 12.0

Jun-2007Dec-2006

Agribusiness

Mining

Manufacturing

Electricity

Construction

Trading

Transportation

Business Services

Services

%

Graph 2.20Share of NPL by Economic Sector

Agribusiness

Manufacturing

Trading

Other Sectors

Business Services

Other manufacturings = Mining, Electricity, Services, Construction, Transportation

%

0

20

40

60

80

100

2000 2001 2002 2003 2004 2005 2006 2007 Jun

Graph 2.21Share of NPL by Loans Type

%

0

20

40

60

80

100

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Investment

Working Capital

Consumer

Although favorable macroeconomic conditions

persist, credit quality in the household sector, which is

reflected by the performance of consumption loans,

continued to decline. A low interest rate for consumption

The quality of working capital and investment loans

only experienced slight improvements as reflected by a

drop in their gross NPL from 6.3% to 5.8% and 10.3% to

26


Graph 2.25NPL Gross of Corporate and MSME Loans

Corporation

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

13.0

2003 2004 2005 2006 2007 June2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5MSME (right)

% %

The quality of corporate loans tended to improve, as

reflected by a drop in gross NPL from 8.1% to 7.2%. This

is projected to have positive impacts on financial system

stability considering the substantial share of corporate loans

(48.6%) in total bank credit. Furthermore, the share of

corporate loans NPL in total bank NPL is very large (60.8%).

Credit restructuring by several state-owned banks improved

corporate loans quality.

A rise in gross NPL from 4.2% to 4.4% in the micro,

small and medium enterprise (MSME) sector indicated a

drop in credit quality. In terms of nominal value, NPL in

the MSME sector rose by Rp2.4 trillion (14.3%), the highest

in recent years. However, this is not expected to threaten

financial system stability in the short term, primarily due

to loan diversification with relatively little outstanding credit

and a relatively large number of debtors.

9.1% respectively. Despite the immaterial nature of these

improvements, they are expected to reduce potential

instability due to their dominance in total bank NPL, namely

52.1% (working capital loans) and 30.5% (investment

loans).

Graph 2.23NPL Gross Performance

Investment

Working Capital

Consumer (right axis)

2.0

7.0

12.0

17.0

22.0

1.5

2.0

2.5

3.0

3.5

4.0

2002 2003 2004 2005 2006 2007 Jun

% %

Graph 2.22Performance of Consumer Loans NPL

Trillions of Rp

Loans (right axis)

NPL

1

2

3

4

5

6

7

8

9

10

2003 2004 2005 2006 2007 June75

95

115

135

155

175

195

215

235

255

275

Graph 2.24NPL Value of Corporate and MSME

Trillions of Rp

Corporation

MSME

-

10

20

30

40

50

2001 2004 `2005 2006 2007 June

Occasional positive exchange rate performance

during semester I 2007 increased foreign denominated

loans and dissipate credit risk. During the reporting period

the rupiah strengthened against the USD by Rp191/USD.

Meanwhile, the majority of corporate debtors restructured

by state-owned banks were granted a foreign denominated

loans facility. As a result, the NPL of foreign denominated

loans dropped significantly by Rp1.7 trillion or 11.0%.

Therefore, the gross NPL ratio of foreign denominated loans

fell from 9.9% to 7.9%. Conversely, the quality of rupiah

denominated loans decreased as reflected by the increase

in NPL by Rp3.4 trillion or 10.4%. The gross NPL ratio for

27


Government Regulation No. 33/2006 on Procedures for

Writing Off State-Owned Receivables (Tata Cara

Penghapusan Piutang Negara), to facilitate credit

restructuring by state-owned banks. Through these various

efforts, banks are expected to surmount the prevailing

problem loans that can threaten financial system stability.

In line with the development in bank businesses, in

the future alternative credit risk mitigation measures need

to be developed. These include securitization and credit

derivatives, which would assist banks in managing credit

risk, ease liquidity problems and promote financial

deepening.

2.2.3. Market Risk

Favorable macroeconomic conditions, supported by

a relatively low inflation rate and the decreasing trend of

the interest rate, helped maintain bank market risk

exposure. The managed reduction of the interest rate,

which began mid 2006, continued in semester I 2007.

Reductions also occurred on the deposit interest rate (1-

month term deposits) and all types of lending rates

(working capital loans, investment loans and consumption

loans). Although the declines in the deposit interest rates

in semester I 2007 (150 bps) exceeded the declines in

lending rates, they failed to reach the magnitude of the

previous semester (238 bps). In contrast, the extent of the

Graph 2.28Loans, NPL and APLL

Trillions of Rupiah

NPL

Provision

Loans (right)

30

40

50

60

70

80

90

100

200

300

400

500

600

700

800

900

1,000

2000 2001 2002 2003 2004 2005 2006 2007June

Graph 2.27Performance of Foreign Exchange Gross NPL

%

Total of NPL

Rupiah NPL

0

5

10

15

20

25

30

35

40

45

2000 2002 2003 2004 2005 2006 2007 June

Foreign Exchange NPL

Graph 2.26Foreign Exchange Rate and Foreign Exchange NPL

Rp/USD Trillions of Rupiah

8,000

8,500

9,000

9,500

10,000

10,500

11,000

11,500

12,000

2001 2002 2003 2004 2005 2006 2007 June10

15

20

25

30

35

40

45

50

NPL of Foreign Exchange (right axis)Exchange Rate

rupiah denominated loans rose slightly from 5.1% to

5.3%.

Credit Risk Mitigation

To mitigate credit risk, banks maintained adequate

loan loss provisions. In semester I 2007, banks established

loan loss provisions of Rp4.2 trillion; representing an

increase of 10.7% over the previous semester. With such

provisions, net NPL at banks dropped from 3.6% to 2.9%.

Additional measures undertaken by banks to mitigate

credit risk include: (i) implementing appropriate risk-

management practices in loan extension; (ii) improving the

capabilities of bank loans officers in credit analysis; (iii)

overcoming asymmetric information in granting loans by

utilizing data and information provided by the Credit

Bureau; (iv) intensifying loan extension through syndication

and other risk sharing agreements: and (v) ratifying

28


decline in lending rates during semester I 2007 surpassed

those of the previous semester. As a whole, these factors

indicate that banks are now more willing to lower lending

rates. Nevertheless, the interest rate for consumption loans

remains high, particularly for foreign banks and joint-

venture banks, with an average rate of over 30%.

exchange maturity profile. This reflects a transition in bank

portfolio composition from rupiah to foreign exchange, in

line with the persistently lower interest rate.

Against this backdrop, risk would appear if there is

a swing in the interest rate to trend upwards. This potential

vulnerability requires close monitoring because the hedging

and derivatives markets that can facilitate the mitigation

of interest rate risk remain under developed. A swing in

the interest rate could trigger losses that place pressures

on banks» capital (CAR) if not well responded to by the

banks. Based on stress testing on interest rates, an average

drop in CAR of 28 bps would occur for every rise in the

interest rate by 100 bps.

Graph 2.29Interest Rate and Exchange Rate Performance

1-month Time Deposits

Investment Loans

Consumer Loans

Exchange Rate (right axis)

% Rp

4

7

10

13

16

19

22

2002 2003 2004 2005 2006 20077,500

8,500

9,500

10,500

11,500

Working Capital Loans

Graph 2.30Loans Interest Rate by Bank Group

%

Dec-05 Jun-06

Dec-06 Jun-07

0

10

20

30

40

State-OwnedBanks

Regional Dev.Banks

Domestic PrivateBanks

Foreign & JointVenture Banks

All Banks

WC I C WC I C WC I C WC I C WC I C

WC = Working CapitalI = InvestmentC = Consumer

Graph 2.32Foreign Exchange Maturity Profile

Billions of USD

(10)

(5)

0

5

10

< 1-month

Dec-05

Jun-06

Dec-06

Jun-07

1 √ 3-month 3 √ 6-month 6 √ 12-month > 12-month

Graph 2.31Rupiah Maturity Profile

Trillions of Rp

(450)

(300)

(150)

0

150

300

450

< 1-month 1 √ 3-month 3 √ 6-month 6 √ 12-month > 12-month

Dec-05

Jun-06

Dec-06

Jun-07

In general, banks managed interest rate risk by

maintaining portfolios with short positions for the short

term and long positions for the long term. Using short

profile maturity on short-term funds, banks realized profits

with the lower interest rate. Short and long positions were

used on both rupiah and foreign exchange portfolios. The

rupiah maturity profile showed a downward trend for the

short position for short term and long position for long

term. Oppositely, a growing trend was reported on the

short position for short term (up to 1 month) on the foreign

Meanwhile, to overcome exchange rate risk, banks

maintained a relatively low Net Open Position (NOP). On

average, bank NOP in semester I 2007 was between 3%

29


and 5%. By maintaining NOP (overall) at a low level, banks

were better able to manage exchange rate risk. Stress

testing on the impact of rupiah exchange rate fluctuations

against CAR showed that, in general, banks can maintain

CAR above 8%. Therefore, recent fluctuations in the rupiah

should not trigger any instability.

With the ability to maintain a reliable maturity profile,

the low NOP position, and a relatively strong capital

coupled with conducive economic conditions, banks are

expected to effectively manage market risk. However,

vigilance is imperative over a rise in the interest rate or a

sudden reversal of short-term foreign capital inflows. As a

result, banks must continuously improve their risk

management as well as prepare an adequate contingency

plan.

SUN ownership by banks for trading purposes grew

from 7.4% to 8.9% of total bank assets in semester I 2007.

In addition, their proportion against total SUN also

increased; from 46.3% to 61.1%. This rise in trading

portfolio could intensify market risk. Stringent monitoring

is required because the global financial market fluctuates

highly that could trigger a drop in the value of financial

assets of domestic banks, including SUN. The drop in

value of SUN could place pressures on bank capital. Based

on stress tests, should the SUN price drop by 15% or more,

the CAR of two large banks would fall below 10%.

Hedging and derivative markets are required to

support the capability of bank management in controlling

and mitigating market risk. Without hedging and derivative

markets, the ability of bank managers to control market

risk is very limited. Well-developed hedging and derivative

markets will assist in market risk-management

implementation and simultaneously bolster financial

deepening.

2.2.4. Profitability and Capital

Profitability

The profitability of banks improved during semester

I 2007 as reflected by the rise in net interest income (NII)

from Rp42.5 trillion to Rp46.4 trillion, and an increase in

return on assets (ROA) from 2.6% to 2.8%. Meanwhile, a

drop in the operating expense to operating income ratio

(BOPO) from 86.5% to 84.6% indicated an improvement

in operational efficiency.

Banks benefited from a decline in interest rates to

boost income. In general, banks widened their spread

through larger and more rapid reductions in their deposit

interest rates compared to the drop in lending rates. As a

result, the interest rate spread for rupiah denominated

credit increased slightly from 10.17% to 10.28%, whereas

the spread on the interest rate for foreign currency credit

widened from 5.30% to 5.47%. In addition, the growth

Graph 2.33NOP Performance (Overall)

%

Oct Nov Des Jan Feb Mar Apr May Jun2006 2007

17.115.6

14.716.3

18.416.9 16.6 17.0

15.3

0

4

8

12

16

20

Domestic Private Banks

Foreign Banks

Joint Venture Banks

All Banks

Regional Dev. Banks

The highest of NOP

State-Owned Banks

Graph 2.34Government Bonds in Bank Portfolio

% of Trading Government Bonds to Total Assets (right axis)

%

0

25

50

75

100

5

9

13

17

21

Dec Mar Jun Sept Dec Mar Jun2005 2006 2007

%

TradingInvestment % of Government Bonds to Total Assets (right axis)

30


of earning assets, especially credits, surpassed the growth

of the deposits. Therefore, the income generated from

loan payments exceeded the additional interest expense

for deposits. As a whole, the factors outlined above raised

NII in semester 1 2007.

On average, the rise in NII as well as non-operational

income raised bank profits by a greater amount than the

increase in total assets. In semester I 2007, bank profits

increased by 14.1%, contrasted against a rise in total assets

of only 7.7%. Consequently, bank ROA rose from 2.6%

to 2.8%. Based on bank group, ROA increased the most

for 15 large banks, namely from 2.4% to 2.6%.

Meanwhile, ROA climbed from 3.2% to 3.3% for the

others bank group. The relatively significant ROA increase

of large banks is a reflection of the crucial role these banks

play in determining the financial performance of the

banking industry.

The increase in loans during semester I 2007 led to a

rise in income generated from loans. The share of interest

income from loans in the previous semester was 60.1%

of total bank interest income; rising to 63.5% in semester

I 2007. The interest income from SBI/Fasbi investment also

increased, primarily due to greater bank placements in SBI/

Fasbi, to Rp6.5 trillion. However, the share of interest

income from other securities dropped significantly, from

21.4% to 16.8%, mainly due to the decline in bank

investment in corporate bonds by Rp16.5 trillion.

Graph 2.36Profit and Assets Performance

Millions of Rp

1,000,000

1,100,000

1,200,000

1,300,000

1,400,000

1,500,000

1,600,000

1,700,000

Jun Dec Jun Dec Jun Dec Jun25,000

30,000

35,000

40,000

45,000

50,000

Profit (right axis)Average Assets

2004 2005 20072006

Graph 2.35NII Performance

-

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

Interest Income Interest Expenses NII

2005 2006 2007

Feb Jun Oct Feb Jun Oct Feb Jun

%

Graph 2.37Structure of Bank Interest Income

BI Securities Loans Others

0

25

50

75

100

2003 2004 2005 2006 2007Dec Jun Dec Jun Dec Jun Dec Jun

7.0 7.8 6.9 7.3 8.9 9.2 8.2 7.9

49.856.4 59.7 63.3 63.1 59.2 60.1 63.5

32.526.3 25.1 22.2 22.0

22.9 21.4 16.8

10.8 9.5 8.3 7.2 6.0 8.7 10.4 11.8

%

Improved profitability was consistent with greater

business efficiency, which was reflected by a drop in the

BOPO ratio. Despite the additional burden of a rise in

provisions for loan losses (PPAP) from Rp2.6 trillion per

month to Rp3.0 trillion per month on average, robust

operational income originating from interest payments

triggered a drop in BOPO; from 86.5% to 84.6%. Higher

profitability and better bank operating efficiency has

contributed to financial system stability.

Capital

High profitability in semester I 2007 enabled banks

to expand their capital internally. Meanwhile, the rise in

bank capital outstripped the rise in risk-weighted assets

and, therefore, CAR increased slightly from 20.5% to

31


20.7%. The bank nominal capital increased by 8.8% to

Rp198.5 trillion, whereas risk-weighted assets increased

by 7.2% to Rp958.9 trillion.

2.3. NON-BANK FINANCIAL INSTITUTIONS AND

THE CAPITAL MARKET

The performance of finance companies and the

capital market as alternative sources of funding remained

healthy in semester I 2007. Robust performance was

primarily supported by conducive economic conditions that

enabled a decline in interest rates. In the capital market,

new issuances improved market liquidity slightly and

reduced volatility, thereby deflating the pressures

associated with investment risk. However, issuance growth

tended to be slow and, thus, unable to meet the rapid

surge in investor demand, especially from foreign investors.

As a result, a price bubble was unavoidable. Market

growth, primarily supported by foreign investor demand,

was vulnerable to corrections, especially in the event of a

sudden reversal. Therefore, financial deepening remains

crucial to offer investment alternatives and, consequently,

diversify risk.

2.3.1. Finance Companies

The decline in interest rates in semester I 2007 began

to impact the performance of finance companies, both

national private finance companies and joint ventures. As

such, increases in the financing to equity ratio and financing

to loan ratio were reported. Total assets of finance

companies grew by 4.2% in line with increased financing.

Graph 2.39Tier 1 Capital to Risk Weighted Assets Ratio and CAR

%

0.0

5.0

10.0

15.0

20.0

25.0

30.0

CAR

Tier 1 to Risk Weighted Assets Ratio

A B C D E F G H I J K L M N O

15 Bi

g Ban

ks

Fore

ign Ba

nks

Joint

Vent

ure B

anks

Othe

rs

All B

anks

B a n k s

Graph 2.38Risk Weighted Assets, Capital, and CAR

%

-

100

200

300

400

500

600

700

800

900

1,000

1,100

10

12

14

16

18

20

22Capital

Dec Jun Dec Jun Dec Jun Dec Jun

2003 2004 2005 2006 2007

Risk Weighted Assets CAR (right axis)

Trillions of Rp

On top of the high CAR ratio, banks also maintained

a high core capital against risk-weighted assets ratio of

17.8%. With such robust capital, banks could absorb

several types of risk, further bolstering financial system

stability. The high capital ratio also provided adequate room

to improve the bank intermediation function.

Although aggregate bank CAR is considered high,

there remain a very limited number of banks with marginal

CAR. A low CAR ratio leads to vulnerability to risks. To

overcome this, bank compliance to the minimum core

capital requirement of Rp80 billion by the end of 2007

and Rp100 million by the end of 2010 is imperative.

Graph 2.40Operational Activities of Finance Companies

Trillions of Rp

0

20

40

60

80

100

120

Assets Financing Funding Capital

20052006

Jan 07Mar 07May 07

32


The business activity of finance companies remains

concentrated on consumer financing (64%), especially to

finance motor vehicles. Notwithstanding, financing from

joint-venture companies exceeded the national private

companies. This is partly due to joint-venture companies

expanding their financing over the longer term, including

infrastructure and housing financing through leasing.

Bank loans remain the primary source of funds for

finance companies, with the share growing from 85%

(May 2006) to 90% (May 2007). National private finance

companies rely heavily on domestic banks, whereas joint-

venture companies rely more on overseas bank loans.

Regardless, joint-venture finance companies have begun

to diversify their fund sources by reducing overseas bank

loans and raising the issuances of stocks and bonds.

Reliance on domestic bank loans undermined the

national private finance companies» performance,

particularly due to the expensive cost of funds. Conversely,

the joint ventures operated more efficiently due to the

lower interest payments for overseas loans.

As previously mentioned, both the national privates

and the joint ventures relied heavily on bank loans for

funding but with the joint ventures using more overseas

bank loans. The lower interest rates offered by overseas

banks gives the upper hand to joint venture finance

companies in extending financing. Furthermore, joint

ventures also actively issue bonds abroad despite its

relatively small nominal value, around USD6 million.

Higher operational costs for the national privates are

mainly due to the increase in credit defaults, suggesting

the need for greater reserves. Subsequently, higher

operational costs lead to a growing deficit in operational

activity and therefore, higher liquidity risk.

Graph 2.44Net Cash Flow of Finance Companies

Billions of Rp

-4,000

-2,000

0

2,000

4,000

2007Jan Feb Mar Apr May

Net Cash Flow of Investment Activities

Net Cash Flow of Funding Activities

Net Cash Flow of Operation Activities

Graph 2.43Source of Fund of Joint Venture Finance Companies

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Dec May Dec Jan Feb Mar Apr May

2005 2006 2007

Domestic Borrowing Foreign Borrowing Securities

Billions of Rp

Graph 2.41Finance Companies

0

20,000

40,000

60,000

80,000

100,000

120,000

Dec May Dec May

2005 2006 2007

Total Finance Companies

Domestic Private Finance Companies

Joint Venture Finance Companies

Billions of Rp

Graph 2.42Source of Fund of Domestic Private Finance Companies

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000Domestic Borrowing Foreign Borrowing Securities

Dec May Dec Jan Feb Mar Apr May2005 2006 2007

Billions of Rp

33


The marked surge in capital inflows from foreign

investors bolstered capital market performance. However,

the absence of a hedging market and the illiquid capital

market lead to short-term foreign investment. Profit taking

by foreign investors in June 2007, which was triggered

primarily by negative sentiment in the international market,

spurred corrections in the domestic capital market. This

weakened the exchange rate.

Against this backdrop, fears of a repeat of the

financial crisis in 1998, which stemmed from a sudden

reversal of capital inflows, placed pressures on financial

system stability (see Box 2.2). However, prevailing financial

system resilience supported by more favorable economic

conditions and optimistic prospects are expected to negate

the effects of any capital outflows.

Equity Market

Equity markets in emerging countries remained

bullish in semester I 2007, supported by strong demand

from foreign investors. In early 2007, equity markets in

emerging countries faced a correction triggered by negative

sentiment in Chinese capital markets. Also, market

corrections occurred due to negative sentiment stemming

from US inflationary pressures, especially towards the end

In addition to liquidity risk, national private finance

companies also faced mounting credit risk due to the

tendency of aggressive and concentrated financing.

Aggressive expansion has potential contagion effects,

particularly due to growing conglomeration among banks,

finance companies, insurance agencies and automotive

businesses. From another perspective, high reliance of

finance companies on bank loans could intensify risk

exposure in the banking industry.

The tendency of joint-venture finance companies to

borrow money from overseas for expansion in rupiah could

mount pressure on financial system stability, in particular

if no hedging strategies are undertaken. Moreover, rupiah

weakening would also hinder the servicing of overseas

loans by finance companies. Collectively, such factors could

strain financial system stability.

2.3.2. Capital Markets

Positive sentiment that supported persistent capital

inflows stemmed primarily from well-managed inflation.

This provided ample room for declines in the BI-rate to

8.25% (June 2007) from 9.5% (early 2007). Capital inflows

emanating from foreign investment in SBI, SUN and

domestic shares surged dramatically (over 100%) in

semester I 2007; reaching Rp58 trillion compared to Rp24.5

trillion in the previous semester.

Graph 2.45 Capital Inflows in Government Bonds, SBI and Stocks

Trillions of Rp

-20

-10

0

10

20

30

Stocks Securities Government Bonds

2006 2007

Jun Jul Sep Oct Nov Dec Jan Feb Mar Apr May Jun

JCI 1,310.26 1,805.52 2,139.28 37.80 18.49

STI 2,435.39 2,985.83 3,548.20 22.60 18.83

KLCI 914.69 1,096.24 1,354.38 19.85 23.55

SET 678.13 679.84 776.79 0.25 14.26

PCOMM 3,020.13 3,940.47 5,148.42 30.47 30.65

HSCI 2,182.73 2,802.68 3,109.64 28.40 10.95

NIKKEI 309.54 336.39 356.40 8.67 5.95

NASDAQ 2,172.09 3,415.29 2,603.23 57.24 -23.78

DJI 11,150.22 12,463.15 13,443.75 11.77 7.87

SIASA 4,543.79 6,979.53 13,202.68 53.61 89.16

KOSPI 1,295.15 1,434.46 1,743.60 10.76 21.55

Table 2.1Selected Regional Price Index Performance

Sem II 06 Sem I 07

Growth (%)Jun 06 Dec 06 Jun 07

34


of semester I 2007. However, strong positive sentiment in

the international market, primarily attributable to the rising

metal commodity price and the prospect of continuing US

economic growth, strengthened equity markets in

emerging countries. In the domestic equity market, bullish

sentiment stemming from the prospective future economy

drove market growth. The JSX Composite rallied 18% to

reach 2,139.28 by the end of the semester.

Despite rapid growth in semester II 2006, market

corrections triggered a slowdown in the indices of nearly

all sectors. Improving economic prospects, which provided

space for regular interest rate reductions, have supported

rallies in the construction and real estate sectors; expanding

by 75% in semester I 2007 compared to 56% in semester

II 2006. Impressive growth in the mining sector was

primarily supported by improving commodity price

performance. Index growth in the basic industrial sector

remained steady at around 30% supported by rising metal

prices on the international market. Profit taking by foreign

investors in the equity of the financial institutions triggered

a price correction. However, bullish sentiment attributable

to interest rate cuts strengthened share prices in the

financial sector.

Soaring share prices are primarily due to the high

amount of transactions. In semester I 2007, transactions

rose by Rp38 trillion to Rp87 trillion. In parallel, foreign

investor transactions also climbed; recording net buying

of Rp11 trillion in semester I 2007 compared to Rp7.5

trillion in semester II 2006.

Strong price performance, which was only supported

by high transaction volume, led to market capitalization

growth despite low market liquidity. Equity market liquidity

was expected to improve in semester I 2007 with new

issuances totaling Rp11.5 trillion. However, such issuances

have, as yet, failed to stifle the strong demand of investors

to invest in shares; resulting in a price bubble. The trend

of restricted new issuances is also due to several businesses

opting for bond issuances, making use of strong market

growth momentum due to the interest rate cuts.

Graph 2.46Regional Index Performance

0

1000

2000

3000

4000

5000

6000JCI

SET

NIKKEI NASDAQ

PCOMM

STI KLCI

HSCI

2007

29Dec

12Jan

26Jan

9Feb

23Feb

9Mar

23Mar

6Apr

20Apr

4May

18May

1Jun

15Jun

29Jun

2006

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2007

29Dec

12Jan

26Jan

9Feb

23Feb

9Mar

23Mar

6Apr

20Apr

4May

18May

1Jun

15Jun

29Jun

2006

AgribusinessConsumerMining

Basic IndustryFinancialMiscellaneous

Construction, Property, Real EstateInfrastructureTrading Service

Graph 2.47Sectoral Index Performance

Agribusiness 661.25 1,190.71 1,680.12 80.07 41.10

Basic Industry 111.45 148.79 196.10 33.50 31.80

Construction,

Property, RE 77.43 120.82 211.72 56.03 75.24

Consumer 299.32 390.19 437.01 30.36 12.00

Financial 142.39 204.39 223.14 43.54 9.17

Infrastructure 585.96 754.54 750.43 28.77 -0.54

Mining 729.65 920.31 1,647.04 26.13 78.97

Miscellaneous 192.43 282.14 324.96 46.62 15.18

Trade Service 212.68 274.28 387.38 28.96 41.24

Table 2.2Sectoral Price Index Performance

Jun 06 Dec 06 Jun 07Sem II-06 Sem I-07

Growth (%)

35


Although not fully able to support efficient prices,

new issuances have improved liquidity, consequently

unbundling and transferring risk in the equity market. This

is evidenced by the market efficiency coefficient (MEC)

distribution that reflects a significant drop in short-term

volatility in semester I 2007 with an increasing MEC value

proportion of 0.75 - <1.4

Bonds Market

The bonds market experienced rapid growth in

semester I 2007, primarily supported by the lower interest

rate. Rapid price performance was not balanced by an

equivalent liquidity performance, resulting in an

excessively high price level. The position of SUN climbed

from Rp419 trillion to Rp451 trillion in semester I 2007.

The government also issued Treasury Bonds (SPN),

beginning in May 2007, totaling Rp3.9 trillion by the end

of the semester.

JCI 0.80 0.20 0.00 0.00 0.15 0.43 0.43 0.00Agribusiness 0.51 0.49 0.00 0.00 0.46 0.54 0.00 0.00Basic Industry 0.31 0.69 0.00 0.00 0.35 0.27 0.39 0.00Construction,Property, RE 0.31 0.69 0.00 0.00 0.39 0.61 0.00 0.00Consumer 0.33 0.67 0.00 0.00 0.37 0.27 0.35 0.00Financial 0.47 0.53 0.00 0.00 0.36 0.64 0.00 0.00Infrastructure 0.29 0.71 0.00 0.00 0.46 0.54 0.00 0.00Mining 0.34 0.66 0.00 0.00 0.39 0.45 0.16 0.00Miscellaneous 0.42 0.58 0.00 0.00 0.34 0.66 0.00 0.00Trade Service 0.31 0.69 0.00 0.00 0.39 0.34 0.27 0.00

Table 2.3Performance of Stocks Market Efficiency

< 0,5 0,5 - <0,75 0,75 - <1 >1 < 0,5 0,5 - <0,75 0,75 - <1 >1

Semester II-2006 Semester I-2007

Graph 2.48Stocks Transaction of Domestic and Foreign Investor

Trillions of Rp

0

20

40

60

80

100

120

Jun Sep Dec Jan Feb Mar Apr May Jun2006 2007

Total Indonesia Foreign

Graph 2.49Value of Capitalization and IPO

Capitalization (Trillions of Rp) IPO (Trillions of Rp)

270

272

274

276

278

280

282

284

286Capitalization Value (JSX)

IPO Value

Capitalization Value (SSX)

2006 2007

Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun0

200

400

600

800

1,000

1,200

1,400

1,600

Graph 2.50Performance of Selected Government Bonds Price

85

90

95

100

105

110

115

120

125

130

2Jan

16Jan

30Jan

13Feb

27Feb

13Mar

27Mar

10Apr

24Apr

8May

22May

5Jun

19Jun

2007

FR0040

FR0028

FR0045

FR0043

FR0034

FR0042

FR0044

4 Further information on the market efficiency coefficient (MEC) is available in FSR No 5,September 2005.

The illiquid market, especially for long tenures,

exacerbated by rumors surrounding a hike in the global

interest rate stemming from inflationary pressure in the

US, encouraged SUN primary investors, namely banking

and foreign investors, to adjust their portfolios by selling

overpriced SUN and buying lower priced SUN in the primary

market. Furthermore, bank investors diversified

investments in Treasury Bonds (SPN). Such behavior led to

a minor correction in the SUN price.

36


markets in emerging countries have been undermined by

market corrections triggered by negative sentiment

surrounding the rising global interest rate. Concerns

spurred a higher long-term investment yield in emerging

markets at the end of semester I 2007 due to the rise in

US dollar investment yield. Additionally, concerns have lead

to investment in financial instruments in emerging markets

to be short term in nature, which in turn leads to volatile

price performance.

Portfolio adjustments led to a decline in SUN

ownership by banks from Rp269 trillion (early 2007) to

Rp258 trillion (end of semester I 2007). However, the

prospective bonds market helped maintain investor

demand for SUN. In semester I 2007, SUN ownership by

non-bank resident investors and foreign investors rose

from Rp87 trillion and Rp55 trillion (early 2007) to Rp97

trillion and Rp82 trillion (end of semester I 2007)

respectively.

Expectations of persistent domestic interest rate

reductions have provided positive prospects for the bonds

market. High expectations were indicated by a drop in the

yield of long-tenure rupiah investments by 80 bps. Similar

performance was witnessed on the yield of long-term

investments in emerging Asian markets, such as Thailand

and Malaysia. However, the long-term prospects of bonds

Graph 2.53Yield of 20-year Government Bonds of Selected Countries

%

0

2

4

6

8

10

12

Indonesia Philippines Thailand

Malaysia Singapore US

2007

8Jan

22Jan

5Feb

19Feb

5Mar

19Mar

2Apr

16Apr

30Apr

14May

28May

11Jun

25Jun

Graph 2.54Comparison of Financial Asset Price Volatility

Percent0 5 10 15 20 25

30hr 100hr

Singapore

US

Indonesia

Philippines

Thailand

Malaysia

Graph 2.52Ownership of Government Bonds

Trillions of Rp

0

50

100

150

200

250

300

2006 2007Sep Oct Nov Dec Jan Feb Mar Apr May Jun

Banking Resident Foreign

Graph 2.51Distribution of Government Bonds by Maturity

Trillions of Rp

0

5

10

15

20

25

30

35

40

1 yr 3 yrs 5 yrs 7 yrs 9 yrs 11 yrs 13 yrs 15 yrs 17 yrs 19 yrs 30 yrs

Fixed Rate Variable Rate

The lower interest rate has begun to positively affect

the performance of the corporate bonds market in line

with the decline in bank lending rates. Corporate bonds

issuers grew from 162 in December 2006 to 168 in June

2007. The issuance value of the six new issuers reached

Rp3 trillion, whereas the issuance value of all issuers grew

37


by Rp18.5 trillion to Rp121.12 trillion at the end of

semester I 2007.

Taking into account the corporate bonds that have

matured, total issuances rose by Rp12.5 trillion. This

indicates the presence of several corporate bonds issuances

for refinancing purposes, primarily due to take benefits

from the lower interest rate.

Mutual Funds

Rapid growth in the equity and bonds markets, as

a result of the lower interest rate, affected the

performance of mutual funds. The reduced savings

interest rate also encouraged investors to diversify

investment from term deposits to capital market

instruments that yield higher returns, especially mutual

funds. Net asset value (NAV) increased by 31.7% to

Rp67.01 trillion in semester I 2007. The rise in NAV, along

with an increase in the number of participating units,

reflects additional investors. Mutual funds remain

concentrated on fixed income types with underlying

assets, especially SUN. The bullish equity market

Graph 2.55IPO and Position of Corporate Bonds

IPO & Position (Trillions of Rp) Issuer

0

20

40

60

80

100

120

140

156

158

160

162

164

166

168

170IPO Position Issuer

Jun Sep Dec Jan Feb Mar Apr May Jun

2006 2007

Graph 2.56Mutual Funds by Type

Source: Bapepam

Trillions of Rp

-

10

20

30

40

50

60

70

80

Mar Jun Sep Dec Jan Feb Mar Apr May Jun2006 2007

Fixed Income Stocks Mixed

Money Market

Protected Index

NAV

supported a rise in equity types of funds. Meanwhile,

along with the low interest rate trend, money market

mutual funds have suffered a decline.

In addition to mutual funds, investors are becoming

more active through investment managers (IM). Funds

managed through IM increased 15% to Rp90.53 trillion

in semester I 2007 (to April 2007), whereas the number

of IM rose from 90 to 94. The Rp90.53 trillion mentioned

consists of mutual funds, discretionary funds and others.

The majority of the managed funds (96%) are owned by

domestic investors, in particular institutions.

The growing number of capital market instrument

alternatives offered through funds management by IM has

led to a less substantial decline in the interest rate of mutual

funds compared to previous years. Furthermore, stricter

mutual funds regulations, specifically regarding the

implementation of mark-to-market and transparency

aspects, have increased public knowledge of the risks

associated with mutual funds. With the prevailing

regulations, growth in mutual funds will continue to

become more sustainable and risk exposure to investors

further anticipated.

38


Financial Deepening in IndonesiaBox 2.1

Financial deepening illustrates the development

of the financial sector (Lynch, 1996; Kiyotaki and Moore,

2005). It refers to the increased provision of financial

services with a wider choice of services geared to all

levels of society. Financial deepening is paramount to:

(i) enforce and improve financial system stability; (ii)

increase capital flows in the financial sector; (iii) improve

efficiency and the competitiveness of the financial

sector; and (iv) broaden access to financial products

and services, including access for the poor and

underprivileged.

To assess financial deepening, Shaw (1973) utilizes

the M2/GDP ratio, Claims on the Private Sector/GDP

ratio, and Fixed Capital Formation/GDP ratio. A higher

ratio indicates greater financial deepening, broader use

of money in the economy, or increased activity in the

financial sector. The assessment can also be performed

by comparing nominal finance and real finance, as well

as by examining whether low or negative real rates of

return are present.

The opposite of financial deepening is ≈shallow

financeΔ (Shaw, 1973), with the following

characteristics:

The financial sector is dominated by banks and

capital flows from abroad in the form of aid/loans,

supplier credits or direct investments.

Low or negative real rates of return. As a result, the

owner of financial assets fails to effectuate a gain

from real growth in the portfolio; instead suffering

a loss.

The economy relies heavily on the government»s

budget and international capital accounts.

Demand for financial assets is constrained by a low

real interest rate, whereas supply is restricted by

credit rationing.

Domestic money is over-valued in the foreign

exchange spot market. This does not encourage

exports and savings but promotes imports and

consumption.

Capital flight takes place.

Recently, the Indonesian financial sector has

indicated signs of progress, demonstrated by rapidly

expanding activity in the equity market and bonds

market (especially SUN). In addition, foreign exchange

transactions have gradually increased to meet the

needs of market players. This is also associated with a

rapid rise in the participation of foreign investors in

the domestic financial market.

Source: The result is processed from Bank Indonesia data. Index is calculated byusing year of 2000 as a base year, following real GDP calculation which use year of1000 constant price. Real finance is calculated by using Consumer Price Index (CPI)from related year as deflator.

2000 1.00 1.00

2001 1.13 0.97

2002 1.18 0.96

2003 1.28 0.99

2004 1.38 1.01

2005 1.61 1.03

2006 1.85 1.02

Table Box 2.1.2Indonesia Financial Deepening Performance

Y e a r Nominal FinanceIndex

Real Finance Index

1997 56.66 60.82 28.31

1998 60.41 53.21 25.43

1999 58.76 20.48 20.14

2000 53.75 19.45 19.85

2001 50.11 17.75 19.23

2002 47.44 18.91 19.00

2003 46.93 20.95 19.29

2004 45.48 24.97 21.68

2005 44.06 25.96 21.97

2006 41.40 23.86 23.97

Table Box 2.1.1 Indonesia Financial Deepening Performance

Y e a r M2/GDP(%)

Claims on PrivateSectors /GDP (%)

Fixed CapitalFormation/GDP (%)

Source: Bloomberg (processed).

39


2000 12.17 n.a n.a 2.82 n.a n.a2001 15.48 n.a n.a 2.93 n.a n.a2002 15.28 14.79 n.a 5.25 4.76 n.a2003 10.39 12.16 13.07 5.29 7.06 7.972004 7.07 8.66 10.27 0.67 2.26 3.872005 10.95 13.25 13.30 -6.15 -3.85 -3.802006 11.63 8.31 9.36 5.03 1.71 2.76

However, financial deepening in Indonesia

remains unsatisfactory, as reflected by the M2/GDP

ratio, Claims on the Private Sector/GDP ratio and the

Fixed Capital Formation/GDP ratio that tend to keep

decreasing (Table 2.1.1). Other indicators include far

lower real finance compared to nominal finance. For

example, M2 growth value in 2006 was 85%,

however, the real value was only 2% (Table 2.1.2).

Furthermore, using data for the term deposit interest

rate level and yields of SUN series FR05 and FR21 as

an example, the real rates of return are lower and

even negative in certain years (Table 2.1.3).

Other factors indicating Indonesia»s ongoing

problems in terms of financial deepening include: (i)

an ineffective bank intermediation function; (ii)

relatively expensive cost of funds; (iii) unavailability of

long-term funding sources; (iv) limited financial

instrument alternatives offered by financial institutions;

and (v) underdeveloped hedging and derivatives

markets. Notwithstanding, access to financial products

and services for the poor and underprivileged remains

limited.

In order to discuss measures to advance financial

deepening in Indonesia, an International Seminar was

held in Bali on 22-24 August 2007 entitled ≈Financial

Sector Deepening and Financial Stability: Benefits and

ChallengesΔ. Learning from the experiences of other

countries presented at the seminar, the importance

of financial deepening in Indonesia is obvious.

However, it should be undertaken while exercising

caution to avoid disrupting financial system stability.

To this end, several research proposals are currently

under discussion.

References:

Kiyotaki, N. and Moore, J.Kiyotaki, N. and Moore, J.Kiyotaki, N. and Moore, J.Kiyotaki, N. and Moore, J.Kiyotaki, N. and Moore, J. (2005), ≈Financial

DeepeningΔ, Journal of the European Economic

Association, 3(2-3): 701-713.

Lynch, D.Lynch, D.Lynch, D.Lynch, D.Lynch, D. (1996), ≈Measuring Financial Sector

Development: A Study of Selected Asia-Pacific

CountriesΔ, The Developing Economies, 34(1):3-

33.

Shaw, E. S.Shaw, E. S.Shaw, E. S.Shaw, E. S.Shaw, E. S. (1973), Financial Deepening in Economic

Development, Oxford University Press, London.

Table Box 2.1.3Indonesia Real Rates of Return

Y e a r Time DepositsInterest Rate (%)

Sources: Bloomberg and Bank Indonesia (processed)

Yield SUNFR05 (%)

Yield SUNFR21 (%)

Real Rates of ReturnsTime Deposits (%)

Real Rates of ReturnsSUN FR05 (%)

Real Rates of ReturnsSUN FR21 (%)

40


Capital Inflows and Sudden Reversal: Are We Ready toFace a Crisis?

Box 2.2

Resurgent capital inflows to Indonesia recently

have spurred concerns regarding a repeat of the

financial crisis that took place exactly 10 years ago.

Though no one can accurately predict when a crisis

will hit, it is imperative to analyze the possibility of

its occurrence and our readiness to confront the

crisis.

The most troublesome factor of any rise in capital

inflows is a sudden reversal, which could trigger a

recurrent crisis. Such concerns are warranted since the

majority of short-term capital inflows are generally

invested in SBI, SUN and equity shares. With investment

in short-term instruments, foreign investors can quickly

divert investment away from Indonesia.

What factors could trigger a sudden reversal?

Generally, there are two groups of triggers: economic

and non-economic factors. Economic factors include:

(i) a narrowing interest rate differential; (ii) low yield

that makes investing in Indonesia unattractive; and (iii)

contagion from other countries. Examples of non-

economic factors include political turmoil and

homeland security. To avoid a repeat crisis, it is essential

to take measures to eliminate, or at least minimize,

the presence of such triggers.

The effect of a sudden reversal can generally be

witnessed from at least two perspectives, namely (i)

First Round Effects; that directly affect a bank»s financial

position or which are micro in nature; and (ii) Second

Round Effects; or follow-through effects that are

indirect in nature to a bank»s individual financial position

and the banking industry (macro).

First round effects create losses for a bank due

to the repricing of foreign exchange assets/liabilities,

and disrupting liquidity supply in the fulfillment of the

foreign exchange need. In addition, first round effects

can also trigger losses on a securities trading portfolio.

For example, excessive selling by foreign investors could

instigate a price drop in SUN, therefore, any banks

that own SUN would incur losses.

In terms of second round effects, a sudden

reversal would spark a rise in foreign exchange NPL,

which would, in turn, trigger a subsequent rise in

rupiah NPL as debtors of foreign exchange loans

usually also have outstanding rupiah loans. Disruptions

in liquidity would magnify as rupiah depositors would

probably also withdraw their money to speculate in

foreign exchange. As a result, the rupiah would be

further weakened and a new crisis could be imminent.

How ready is Indonesia to face a crisis? Learning

from the 1997/1998 crisis experience, numerous

improvement measures have been taken to better

prepare this country. Furthermore, resilience in the

financial system is currently much more robust

compared to the pre-crisis period. Several of the salient

accomplishments made by the banking industry post-

crisis include:

Bank capital over the past few years has been far

higher compared to pre-crisis levels, which can act

as a buffer against any ≈shocksΔ. Profitability is

also better compared to pre-crisis levels.

Credit quality continues to improve, as reflected

by the declining NPL ratio, contrary to the pre-crisis

period when credit quality persistently deteriorated.

Banks have implemented a better risk-

management function in their daily operations.

Risk management was not implemented pre-crisis.

Banks have also implemented good corporate

governance principles; something that was

abandoned prior to the crisis.

The current level of bank Net Open Position (NOP)

is only 3% to 5%; very low compared to pre-crisis

levels. Furthermore, the use of derivative

transactions is less active than before the crisis.

Now, they are generally only used for hedging and

41


matching positions, not for the purpose of trading

as occurred prior to the crisis.

Currently there is good compliance to the legal

lending limit, whereas approaching the crisis there

were numerous legal lending limit violations

(especially by related parties).

Post crisis, bank risks have relatively been reduced

and diversified along with: (i) a shift in credit focus

from long-term to short-term tenors, or from

corporate loans to micro, small and medium

enterprise (MSME) loans; (ii) a shift from industrial

sector credit to trade sector credit; (iii) credit

denominated in foreign currency, which is sensitive

to exchange rate fluctuations, has remained steady

at around 20% - 23% of total loans; and (iv) the

rise in bank placements in SBI and SUN.

Additional factors accomplished post crisis and

which further bolster financial system resilience include:

The institution of a Financial Sector Safety Net

(FSSN), including the establishment of the

Graph Box 2.2.2.Foreign Exchange Loans Performance

700

600

500

400

300

200

100

0

12,000

10,000

8,000

6,000

4,000

0

2,000

1995 1997 1999 2001 2003 2005 2007

Credit of Foreign ExchangeCredit of Rupiah

Exchange Rate (right)

Trillions of Rp USD/Rp

Graph Box 2.2.1.Pre and Post Crisis of NPL & CAR

NPLs Gross CAR

Pre Crisis

Crisis

Recovery

1996 1998 2000 2002 2004 2006

-25.0

-15.0

-5.0

5.0

15.0

25.0

35.0

45.0

55.0

%

Indonesian Deposit Insurance Corporation (LPS) to

protect customer deposits. Prior to the crisis,

Indonesia did not have such a safety mechanism.

International foreign exchange reserves far exceed

pre-crisis levels and are following a growing trend.

Financial system resilience is more robust due to

greater foreign exchange reserves.

Post crisis, Indonesian Banking Architecture (API)

was compiled, comprising of programs such as

bank consolidation, compulsory risk-management

certification for banks, improvement in operational

quality and bank management, and best practices

for bank supervision.

The progress outlined above offers strong

indications that the Indonesian financial sector,

especially the banking industry, is comparatively more

resilient. Therefore, this country is now better prepared

to face numerous worst-case scenarios that could

disrupt financial system stability, including a sudden

reversal that could trigger a crisis.

42



43

Chapter 3 Prospects of the Indonesian Financial System

Chapter 3Prospects of the IndonesianFinancial System

44



45


Prospects of the Indonesian Financial SystemChapter 3

3.1. ECONOMIC PROSPECTS AND RISK PERCEPTION

Economic prospects remain positive with the

potential to improve in the future. Consensus forecasts

for the Asia/Pacific region also indicate prospective

economic growth supported by the expansion of

international trade and a controlled inflation rate at about

6%. Such conditions are expected to act as an impetus

for real sector development and improve economic

resilience against risks to financial system stability. Hedge

fund investment has proliferated, due partly to macro

stability and attractive yields, which has compensated the

decline in global investment credit spread.

Foreign investors still consider economic conditions

in Indonesia attractive and relatively stable. Therefore, both

long and short term investment flows are forthcoming,

Strong bank capital, favorable macroeconomic indicators and robust corporate

profitability underpinned relatively stable financial system performance in

Indonesia during semester I 2007. However, the surge in short-term capital

inflows to the capital market, various pressures stemming from the global

market with the potential to trigger a sudden reversal, and potential risks

emanating from domestic-bank-allocated credit require consistent and

constant vigilance.

Indo 14 BB- 6.57 196.5 114.8

Indo 17 BB- 6.74 203 124

Indo 35 BB- 7.34 245.6 177.4

Table 3.2Indonesian Risk Perception

Source: Bloomberg

Bonds Rating Y-t-m (%)Yield Spread (bp)

June December

GDP (% y-o-y) 6.0 6.3 6.1 6.1 6.2 6.0 6.1 6.0

Inflation (% y-o-y) 6.4 6.0 6.3 6.5 6.3 6.5 6.4 6.3

Balance of Trade (Billions of USD) 8.0 9.3 8.7 9.5 9.4 9.6 9.5 10.3

Table 3.1Concencus Forecast of Selected Economic Indicators

2007 2008

Q-I Q-II Q-III Q-IV Q-I Q-II Q-III Q-IV

Source: Asia Pacific Concensus Forecast

Graph 3.1Yield Curve

Source: Bloomberg Years

%

0

2

4

6

8

10

12

1 3 5 6 7 8 9 10 15 20

3/30/2007

6/29/2007Log. (6/29/2007)

Log. (3/30/2007)

46


which in emerging market countries take the form of

bonds, leveraged lending and structured credit products.

However, vigilance remains critical as an interest rate hike

in other developing or even developed countries of 200

bps could trigger fund migration, which would affect

economic growth in Indonesia.

3.2. BANK RISK PROFILE: LEVEL AND DIRECTION

Banks remain stable although the bank

intermediation function is still sub optimal. Such conditions

have encouraged banks to strengthen their capital with

CAR at 21.4% and ROA at 2.8%. However, the relaxation

of regulations, undertaken to encourage banks to extend

credit, requires caution to avoid an increase in non-

performing loans.

Market risks (exchange rate risks) remain low. Future

risks are expected to remain immaterial due to the banks»

insignificant foreign exchange portfolio, as reflected by

the low net open position. However, interest rate risks

are moderate due to the bank maturity profile being

sensitive to swings in the interest rate. In addition, financial

instruments used to hedge interest rate risks remain limited,

which can be attributed to the under-developed domestic

hedging and derivatives markets.

Similar to interest rate risks, market risks associated

with the SUN price also remain moderate. However,

interest rate risks are more alleviated than market risks

linked to the price of SUN. This is because the volatility in

the global market can directly influence the SUN price,

which is beyond the control of management.

The concentration on short-term deposits, large

depositors and individually owned deposits can trigger a

rise in liquidity risk. Banks have consequently opted for

low-risk placements in securities to mitigate liquidity risk.

Such measures have had significant results in minimizing

liquidity risk with the risk control system deemed as

acceptable and is expected to improve in the future.

Credit risk remains moderate amidst restructuring

efforts and an expansion of credit allocation due to better

credit access for infrastructure projects. However, the

protracted restructuring process of major debtors may

inflate restructuring costs, therefore, credit risk is expected

to intensify in the future. Credit risk may also emerge from

imperfect credit risk management that requires further

refinement, as well as weaknesses in the bank credit

management information system.

In terms of operational risk, Indonesian banks remain

beset with various challenges, such as the existence of

some banking crime cases and issues related to information

technology. In addition, natural disasters such as floods

and earthquakes, as well as various other disruptions have

crashed the telecommunications system. However, such

Graph 3.2Risk Profile of Banking Industry and Its Direction

Semester-I 2007

Outlook

Inherent Risk

HighM

oderateLow

Strong Acceptable Weak Strong Acceptable Strong Acceptable

Liquidity Risk Credit RiskMarket Risk

Exchange Rate

InterestRate Government Bonds

Price

Semester-I 2007

Outlook

Semester-I 2007

Outlook

Weak Weak

Risk Control Risk Control Risk Control

47


disruptions have not yet incurred significant losses or

reduced public confidence in banks. Another challenge

faced by banks is measuring operational risk and

formulating appropriate risk mitigation tools. This is

primarily due to data limitations and relatively low

competence in some banks. Basel II implementation is

expected to boost competence and capacity in measuring

and controlling operational risk.

3.3. PROSPECTS OF THE INDONESIAN FINANCIAL

SYSTEM

The financial system is expected to remain stable and

outperformed conditions as per year end 2006. This is

reflected by a decline in the financial stability index (FSI)

from 1.37 to 1.21 (see Box 3.1). Stability is supported by

improving macroeconomic conditions, healthy banks, as

well as robust equity and bonds markets. Financial system

resilience in the upcoming quarter is expected to remain

strong, reflected by FSI simulations that projected 1.25

(December 2007).

The greatest potential risk faced by Indonesia is the

result of burgeoning short-term foreign capital inflows to

the bubbling capital market. This is a phenomenon where

capital can migrate swiftly across state borders and is

attributable to three key aspects: (i) growth in assets

managed by investment managers; (ii) a change in

investment behavior away from ≈home-biasΔ; and (iii)

innovation and progress in banking and corporate risk

management. Hedge fund growth is projected to reach

US$1.4 trillion. Their focus of obtaining absolute income

affects hedge fund investment decisions in emerging

markets.

The rise in capital flows between financial markets

avails that the inter-state market is borderless. For example,

the bonds and equity markets in Europe, Japan and the

US are closer now compared to a decade ago. Pressures in

Estimation

0

0.5

1

1.5

2

2.5

3

3.5FSI FSI Average

2003 2004 2005 2006 2007M8 M10M12M2 M4 M6 M8 M10M12 M2 M4 M6 M8 M10M12M2 M4 M6 M8 M10M12 M2 M4 M6 M8 M10M12

Graph 3.3Financial Stability Index

the US market cause problems in other countries. This

was evidenced by the subprime mortgage crisis in the US,

which spread to Australia, Germany as well as other

countries.

Despite several stock exchanges returning to normal,

the potential for upcoming shocks remains significant.

Higher inflation in China, an increasing global interest rate

and the soaring international oil price, which peaked at

USD70.68 per barrel in semester I 2007, are considered

the primary sources of potential shocks and must be

monitored and mitigated to avoid aggravating conditions

in Indonesia. Contagion in the Jakarta Stock Exchange

could force investors to withdraw funds or migrate to

instruments in the global money market. However,

pressures are expected to be temporary in nature and, thus,

continuous supervision and vigilance is required to

anticipate and mitigate potential risks.

As mentioned earlier, results of stress tests on market

risk in banks indicate relatively strong resilience.

Meanwhile, a stress test conducted on a sample of 35

major conglomerates/large corporations in Indonesia with

foreign exchange liabilities demonstrated that their capital

is sufficient to absorb exchange rate risk with the rupiah

depreciating to Rp11,500/USD. Should the rupiah devalue

further, however, more than one conglomerate/large

corporation would struggle with capital.

48


The results of probability of default (PD) estimations

for a sample of 219 non financial public listed companies

indicated that the number of companies with a PD of more

than 0.5 will rise slightly; from 66 companies at the end of

December 2006 to 69 at the end of December 2007 (see

Box 3.1). This indicates that credit risk in the future will

increase a little. The estimation results are consistent with

previous analyses showing a small increase in upcoming

credit risk. However, existing provisions and strong bank

capital is expected to absorb the projected rise in credit

risk.

In the future, GDP growth and the low interest

rate are expected to boost the business activities of

banks. The sectors projected to exceed average

economic growth include construction (10.2%),

transportation and communications (9.7%), and

manufacturing (8.5%). Credit extension by banks is

planned to expand by 22% on average, supported by

growth in deposits. Increased activity in the capital

market, especially through corporate issued bonds, will

foster real sector performance.

3.4. POTENTIAL VULNERABILITIES

Financial system resilience in Indonesia will remain

robust in the future. Externally, however, volatility persists

stemming from sluggish global economic growth, a soaring

oil price and short-term capital inflows. The effectiveness

of measures to resolve the subprime mortgage crisis

undertaken by relevant monetary authorities of affected

countries could also influence financial stability in

Indonesia. Internally, volatility could arise from the

upcoming election, which is expected to affect business

activities and risks in the financial sector. In particular,

undesirable security conditions could trigger capital

outflows.

Meanwhile, banks will face intricate challenges, such

as resolving the credit restructuring, refining risk

management, upgrading the credit management

information system, as well as synchronizing efforts to

improve the bank intermediation function and reduce

credit risk. Other challenges include the development of a

contingency plan to ease operational risk as well as

Probability of Default

0

10

20

30

40

50

60

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

December 2007

Graph 3.4Non Financial Public Listed Companies Probability of Default


0

10

20

30

40

50

60

70

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

December 2006

Until Rp.11,000/USD 0






> Rp17,000/USD 35

Table 3.3Impacts of Exchange Rate to Conglomeration Equity

Exchange RateNumber of Conglomeration Which Have

Difficulties in Their Equity

49


improving the effectiveness of internal controls and

corporate governance in order to reduce vulnerabilities in

the banking industry. Compliance to the minimum core

capital requirement of Rp80 billion by the end of 2007

and Rp100 billion by the end of 2010 will also be a

challenge for a few banks as it could affect their risk

management capacity.

To reduce risk in the financial system close

coordination between Bank Indonesia and the Government

is required. As discussed in the following chapter, one joint

initiative is the establishment of the Financial System

Stability Forum (FSSF), which aims to act as a medium to

exchange information and resolve risks in the economy

that could trigger a crisis.

50


Financial Stability Index and Probability of DefaultBox 3.1

Financial Stability Index

The Financial Stability Index (FSI) is an indicator

used to assess financial stability performance in a

country. FSI is established upon three primary blocks in

the Indonesian financial sector: banks, the equity

market and bonds market (see Hadad et al., 2007).

The three blocks correlate with one another and their

interactions affect financial stability. Each block is

represented by a set of behavioral equations, whereas

inter-block relationships are explained using an identity

equation. The equations are written as follows:

Behavioral Equations:

Bank Block: Non-Performing Loans

npl = f[rlnd, log(income), (rlnd-sbi)]

Equity Market Block: Jakarta Composite Index (JCI)

Log(ihsg) = f[log(ihsg(-1)), log(income), rlnd, log(er)]

Bonds Market Block: Government Yield Bonds (5 Years)

yield5yr = f[rdep1m, log(income), log(er)]

Identity Equation:

Income = cons + inv

Explanation of Variables:

Utilizing monthly data from the Monthly Report

of Commercial Banks (LBU), Bloomberg and CEIC, each

equation is calculated using the Three Stage Least

Squares (3SLS) method. In this Review, the observation

period is from January 2003 to June 2007. The validity

of the estimation results on the endogenous variables

are tested using root mean squared error, mean

absolute error, mean absolute percentage error, and

theil inequality coefficients. The results are then

weighted to form the FSI. Considering that banks

dominate the financial sector, greater weight is

allocated to banking indicators, i.e. NPL. Estimation

results for this model can be used to predict FSI up to

one year in advance.


Probability of default (PD) is used to predict the

probability of a default occurring (failure to meet

company liabilities) in the future. In this Review, the

Barrier Option method is used to estimate a PD, which

models company asset behavior towards its liabilities

based on balance sheet data. This method is preferable

since it can calculate individual company»s PD using

only limited data, whereas other models require larger

quantities of data. This method is also suitable to

measure a PD for an economic sector or for an industry

with specific characteristics.

The Barrier Option method is based on the

Merton (1974) approach where assets are assumed

to follow Brownian Motion, a stochastic process with

a continuous time frame limited by the Random Walk

process. The Barrier Option method can determine the

probability of an asset»s value dropping to a specified

threshold. The threshold in this regard is the company»s

liabilities. This approach is deemed superior to the usual

option approach as it can be used to calculate the

likelihood of an asset»s value reaching its specified

threshold prior to the asset»s maturity date.

(rlnd-sbi) Difference between lending rate and SBI

Log(er) Nominal exchange rate (Rp per USD)

Log(ihsg) Jakarta Composite Index

Log(income) Aggregate income; consumption and

investment sectors

Npl Non performing loans

Rdep1m 1-month deposit interest rate

Rlnd Lending rate

Sbi 1-month SBI rate

Yield5yr Government yield bond, 5 years

Cons Consumption

Inv Investment

Variable Description

#) log : natural logarithm##) ≈(-1)Δ : value of in the previous period

51


In this Review, data for total assets and total

liabilities is taken from a sample of 219 non financial

public listed companies on the Jakarta Stock Exchange

at the end of December 2005 and 2006. Based on

total asset data, estimations are made based on

average asset growth and its corresponding volatility

values. The results are subsequently used to calculate

a PD up to 1 year ahead. To calculate a PD for the end

of 2006, company balance sheet data for the end of

December 2005 is used. Likewise, to calculate a PD

for the end of December 2007, data for December

2006 is used. Estimation results indicate that the

number of companies with a PD of greater than 0.5

at the end of 2007 is slightly higher than the position

at the end of 2006.

References

Crosbie, P. (2003), Modeling Default Risk; Modeling

Methodology, Moody»s KMV, 18 December 2003.

Hadad, M.D., Safuan, S., Santoso, W., Besar, D.S., and

Rulina, I. (2007), ≈Macroeconomic Model to

Measure Financial Stability Index: The Case of

IndonesiaΔ, Financial Stability Review (FSR), II -

2006 No.8, March 2007.

Merton, R.C. (1974), ≈On the Pricing of Corporate

Debt: The Risk Structure of Interest RateΔ, Journal

of Finance, 29:449-470.

Reisz, A. S. and Perlich, C. (2007), ≈A Maket-Based

Framework for Bankruptcy PredictionΔ, Journal of

Financial Stability, doi:10.1016/j.jfs.2007.02.001.

Graph Box 3.1.1.Probability of Default - Barrier Option Methods

Graph Box 3.1.2.Probability of Default - Common Option Methods

Source: Reisz and Perlich (2007)

Asset

Possible assetvalue path

DefaultPoint

Tt*Default Event

Source: Crosbie (2003)

AssetPossible

asset valuepath

Distributionof asset valueat the horizon

DefaultPoint

Probability ofDefault

H0

Vo

52



53

Chapter 4 Financial Infrastructure and Risk Mitigation

Chapter 4Financial Infrastructureand Risk Mitigation

54



55


Financial Infrastructure and Risk MitigationChapter 4

4.1. PAYMENT SYSTEM

The payment system in Indonesia remained reliable

with no demonstrable risks to disrupt financial system

stability. Furthermore, default risk in the payment system

was greatly minimized.

million) are settled through BI-RTGS, the payment system

is dominated by BI-RTGS. The BI-RTGS system accounted

for 92.85% of settlements in semester I 2007, with the

clearing system making up 3.50% and the balance settled

outside of Bank Indonesia. Transactions through BI-RTGS

during the reporting period increased in value and volume

compared to the previous semester. Transaction value in

semester I 2007 reached Rp22.09 thousand trillion; up

38.03% over the previous semester (Rp16.01 thousand

trillion). The volume for the semester totaled 3.87 million

transactions; up 6.48% compared to the previous semester

(3.63 million transactions).

Reliability of the payment system, which represents Indonesia»s prime financial

infrastructure, was maintained and, consequently, bolstered financial system

stability throughout semester I 2007. The payment system functioned well

despite a rise in transaction volume and value. Meanwhile, efforts to strengthen

the Financial System Safety Net (FSSN) were continued. Refinements were

made to the Financial System Stability Forum (FSSF), which began operation

in semester I 2007. In the future, FSSF will also be responsible for coordinating

the implementation of the Financial Sector Assessment Program (FSAP) as

well as Indonesian Financial System Architecture (IFSA).

A rise in settlement volume and market share

indicated that Bank Indonesia»s real-time gross settlement

system (BI-RTGS) played a more important role in the

payment system. Since all major transactions (>Rp100

Transaction Transaction Transaction Transaction Value Volume

Value Volume Value Volume

(thousand (thousand

trillions) (millions) trillions) (millions)

Table 4.1BI-RTGS Settlement Performance (in Value and Volume)

Growthq-t-q

Semester II-2006 Semester I-2007

Rp 16.01 3.63 Rp 22.09 3.87 38.03% 6.48%

Graph 4.1Activities of Payment System Transaction

Semester I 2007

92.85%

3.48%

0.18%3.50%

RTGSClearingCredit Card Account Based Card (ATM,ATM+debet & debet)

56


The rise in settlement value and volume was primarily

attributable to the hike in money market transaction value.

Money market activity still represents the largest share of

BI-RTGS. The value of transactions in the money market

topped out at Rp9.16 thousand trillion in semester I 2007;

a rise of 58.45% over the previous semester. Although

the volume of transactions in the money market represents

a mere 1.19% of total RTGS transaction volume, the share

of total transaction value in BI-RTGS is 41.46%.

Growth in inter-bank transactions also continued

apace. Inter-bank transaction value in semester I 2007

reached Rp9.72 thousand trillion; up 36.32% over the

previous semester. Considering total transactions in BI-

RTGS this past semester, inter-bank transactions constitute

the largest share with 44.01% of total transaction value

and 86.66% of total transaction volume. Inter-bank

transactions primarily consist of trading on securities; rising

by Rp586.81 trillion, or 81.04%.

Settlements processed through clearing also ran

smoothly. Implementation of the Bank Indonesia National

Clearing System (BI-NCS) began in 2005 and, up to

semester I 2007, covered 36 Bank Indonesia branch office

areas and 28 non branch office areas. Clearing is grouped

into the following cycle: credit clearing I, credit clearing II

and debit clearing (comprising of incoming and return

clearing). Credit clearing is performed centrally in Jakarta

whereas debit clearing is performed locally in each Bank

Indonesia branch office region; however, settlements are

still centralized. The value of credit clearing transactions

through BI-NCS in the reporting semester totaled

Rp170.26 trillion comprising of 18.01 million transactions,

while the value of debit clearing transactions using

cheques and giro instruments amounted to Rp466.76

trillion consisting of 19.85 million transactions. Full BI-

NCS implementation in all domestic regions would have

an immense stimulatory impact on economic activity as

fund transfers could be performed rapidly at relatively

low cost.

Meanwhile, the use of payment card instruments

(APMK) is expanding, not only in volume and value, but

also in the types and the number of cards available.

Currently, APMK cards include credit cards, ATM cards as

well as ATM cards that also function as debit cards

(ATM+Debit). To the end of the reporting semester, the

number of all three card types outstanding was 40.46

million cards with a transaction volume of 809.22 million

and transaction value of Rp1.103 thousand trillion. Of the

three card types, ATM+Debit cards represent the largest

market share in number, value and transaction volume.

The number of ATM+Debit cards in the market at the end

of semester I 2007 was 29.63 million cards or 73.23% of

total cards, with a volume and transaction value share of

61.95% and 66.07% respectively.

Default risk in the payment system has been further

minimized. Currently, only 3.72% of transactions in the

Indonesian payment system are not covered, due to the

use of «no money no game» principles in BI-RTGS and the

implementation of failure-to-settle (FtS) in BI-NCS. As

regulator, Bank Indonesia has intensified risk mitigation

efforts in the payment system through the ratification of

appropriate regulations. Such regulations include

prudential principles and customer protection, as well as

enhanced security to safeguard payment card activity.

Credit Card 8.44 62.01 33.05

ATM Card 2.39 245.86 341.46

ATM + debet Card 29.63 501.29 729.39

TotalTotalTotalTotalTotal 809.22809.22809.22809.22809.22 1,103.891,103.891,103.891,103.891,103.89

Table 4.2Card Based Payment Transaction

Total Transaction TransactionCards Type Cards Volume Value

(millions) (millions) (trillions)

57


4.2. PAYMENT SYSTEM POLICY AND RISK

MITIGATION

Bank Indonesia continued its risk mitigation efforts

throughout semester I 2007, and close attention was paid

to the intensification of payment system reliability. Efforts

focus on four salient principles: risk minimization, efficiency

optimization, access equality and customer protection.

Several payment system policies instituted and

accomplishments made in semester I 2007 are outlined as

follows:

1.1.1.1.1. Intensification of BI-RTGS Adherence to CoreIntensification of BI-RTGS Adherence to CoreIntensification of BI-RTGS Adherence to CoreIntensification of BI-RTGS Adherence to CoreIntensification of BI-RTGS Adherence to Core

Principles for Systemically Important Payment SystemsPrinciples for Systemically Important Payment SystemsPrinciples for Systemically Important Payment SystemsPrinciples for Systemically Important Payment SystemsPrinciples for Systemically Important Payment Systems

(CP SIPS)(CP SIPS)(CP SIPS)(CP SIPS)(CP SIPS)

Core Principles for Systemically Important Payment

Systems (CP SIPS) is an international standard issued

by the Bank for International Settlements (BIS),

through the Committee on Payment and Settlement

Systems (CPSS). CP SIPS stipulates the principles

necessary in designing and operating a payment

system in each country. Based on the results of self-

assessment of the BI-RTGS system, performed in

semester II 2006, several core principles (CP) are yet

to be met by BI-RTGS. To this end, Bank Indonesia

has refined the application of BI-RTGS system as well

as related regulations. In terms of application, security

features of BI-RTGS are in the process of being

upgraded. In particular, improvements have been

made to BI-RTGS system reliability regarding system

availability during operational hours; both the main

system and backup. Regulations have been refined

by explicitly defining Bank Indonesia»s role in running

BI-RTGS, namely as regulator, administrator and

supervisor of the BI-RTGS system.

2.2.2.2.2. Oversight of BI-RTGSOversight of BI-RTGSOversight of BI-RTGSOversight of BI-RTGSOversight of BI-RTGS

BI-RTGS oversight is intensified to ensure system

reliability. The BI-RTGS system is a systemically

important payment system (SIPS). Thus, the BI-RTGS

system is a prime concern in terms of payment system

oversight. BI-RTGS system oversight is necessary to

ensure that BI-RTGS system implementation is

performed accurately, securely and in a reliable fashion

in order to support financial system stability in

compliance with customer protection principles.

BI-RTGS system implementation involves two parties,

namely Bank Indonesia as administrator, and the

participants. BI-RTGS system oversight previously only

focused on the participants; however, during the past

semester oversight has also focused on the

administrator. Oversight of the administrator

concentrates on an overall assessment on BI-RTGS

system implementation based on security, efficiency,

customer protection, compliance to prevailing

regulations, agreed implementation standards and

current payment system policies.

3.3.3.3.3. Business Continuity Plan (BCP) for BI-RTGSBusiness Continuity Plan (BCP) for BI-RTGSBusiness Continuity Plan (BCP) for BI-RTGSBusiness Continuity Plan (BCP) for BI-RTGSBusiness Continuity Plan (BCP) for BI-RTGS

Bank Indonesia continues to build the capacity and

competence of human resources (HRD) under

emergency conditions for BI-RTGS participants. This

is carried out through routine BCP tests. The continuity

of the BI-RTGS system for both the administrator and

the participants requires not only infrastructure

reliability (application, hardware and network), but

also on the availability of a competent human

resources department (HRD) that fully comprehends

all procedures involved in the contingency plan. To

this end and in accordance with one of the

recommendations of the self-assessment, Bank

Indonesia routinely tests the preparedness of all

participants using BCP testing. In addition, to improve

the readiness of the backup system, Bank Indonesia

facilitates periodic backup tests for the participants.

To improve infrastructure awareness from the

58


administrator»s side, Bank Indonesia regularly tests the

BI-RTGS system through the backup system at the

Disaster Recovery Centre (DRC).

Regarding emergency procedures, Bank Indonesia will

refine regulations on alternatives to transaction

settlement through the BI-RTGS system in order to

enable participants to maintain operations in case of

a system shutdown. This will reduce the potential

systemic risk of one participant»s downtime on the

whole system. Improvements include additional

alternative transaction settlement mechanisms that

can be used by the participants in emergency

conditions. Previously only the Bank Indonesia Giro

Account (BGBI) was available, however, Bank

Indonesia intends to provide a backup RT system at

Bank Indonesia head office to be used by participants

in an emergency.

4.4.4.4.4. Security Upgrades for Payment Cards (Security Upgrades for Payment Cards (Security Upgrades for Payment Cards (Security Upgrades for Payment Cards (Security Upgrades for Payment Cards (APMKAPMKAPMKAPMKAPMK)))))

Bank Indonesia amended payment card regulations

governing APMK online reporting. As a result, indirect

oversight of payment cards was simplified. In addition,

this has lead to improved customer protection.

Furthermore, payment card information is now more

up to date so that policy making is more akin to the

rapid nature of payment cards. Apart from indirect

oversight through APMK report analysis, Bank

Indonesia also conducts direct oversight on APMK

administrators to ensure the risks of APMK activities

are not allowed to crystallize. Aware of the potential

risk of card fraud and misuse, Bank Indonesia will strive

to mitigate risk by implementing chip technology for

ATM cards and debit cards in the future.

5.5.5.5.5. Risk Assessment and Risk Management in theRisk Assessment and Risk Management in theRisk Assessment and Risk Management in theRisk Assessment and Risk Management in theRisk Assessment and Risk Management in the

Payment SystemPayment SystemPayment SystemPayment SystemPayment System

Bank Indonesia has two roles in the BI-RTGS system

and clearing system, namely as administrator and

participant. As administrator and participant, Bank

Indonesia faces potential risks including financial,

reputation and legal risks emanating from operational

problems caused by system disruptions and human

error. To alleviate the possibility of human error, Bank

Indonesia regularly conducts risk assessments to

identify factors that can trigger operational problems,

risks and their impacts, and the anticipatory measures

required. These are performed using a number of

methods, one being Control Self Assessment (CSA).

6.6.6.6.6. National Black List (National Black List (National Black List (National Black List (National Black List (DHNDHNDHNDHNDHN)))))

Through the implementation of customer protection

principles and to maintain public confidence in cheque

and giro accounts as payment instruments, Bank

Indonesia introduced a new regulation on a National

Black List (DHN) of dishonored cheques and giro using

a different method compared to the previous

regulation. An additional clause was attached to the

terms and conditions of checking accounts governing

the use of cheques and giro as well as a bank»s

obligations in terms of the DHN in order to improve

customer protection.

Furthermore, Bank Indonesia also amended the way

the DHN is administrated. Under the previous

regulation, the black list was managed by Bank

Indonesia whereas the new regulation stipulates that

each bank manage the black list. This method was

implemented because individual banks are generally

better at identifying their own customers»

characteristics than Bank Indonesia. In addition, the

method also improves the banks» awareness of Know

Your Customer (KYC) principles.

4.3 FINANCIAL SECTOR SAFETY NET (FSSN)

Implementation of the financial sector safety net

(FSSN) continued in earnest during semester I 2007 in order

59


to increase financial sector resilience, particularly the

banking sector. The current structure of the safety net has

been included in a draft act that comprehensively includes:

(i) regulations and supervision of institutions and financial

markets; (ii) lender of the last resort facility; (iii) deposit

insurance program; and (iv) crisis management.

The deposit insurance scheme administered by the

Deposit Insurance Corporation (LPS) and the Emergency

Funding Facility (EFF) are two of the most important

components of FSSN. In terms of the deposit insurance

scheme, LPS has resolved claims from the customers of

nine rural banks that were closed in 2005 and 2006.

Meanwhile, although EFF has been in effect since 2005,

to date no bank has made use of the facility. To some

extent, this reflects sound bank conditions and the absence

of systemic liquidity problems.

On 29th June 2007, the Governor of Bank Indonesia

(BI) and the Chairman of LPS signed a Memorandum of

Understanding (MoU) as a part of the efforts to strengthen

the Financial Sector Safety Net (FSSN). The MoU regulates

coordination and information exchange between Bank

Indonesia and LPS listing five key aspects regarding the

deposit insurance scheme, bank oversight and the handling

of unhealthy banks, namely: (i) implementation of the

deposit insurance scheme; (ii) handling of problem banks;

(iii) resolution and/or handling of failed banks, (iv) follow-

up on banks with revoked license; and (v) deciding an

appropriate interest rate level for claims to be paid. With

the signing of the MoU between Bank Indonesia and LPS,

Indonesia is approaching completion of its legal

infrastructure and a clear guide to prevent and overcome

financial crises.

4.4. FINANCIAL SYSTEM STABILITY FORUM (FSSF)

Coordination to maintain financial system stability

can be achieved through the Financial System Stability

Forum (FSSF). FSSF was established on 30th December 2005

through a Joint Decree between the Minister of Finance,

the Governor of Bank Indonesia and the Chairman of the

Deposit Insurance Corporation (LPS). A recent reshuffle in

Ministry of FinanceMinistry of FinanceMinistry of FinanceMinistry of FinanceMinistry of Finance- Director General of Financial Institutions- Director General of The Treasury- Head of the Economic Policy, Finance and International Cooperation Body.

Bank IndonesiaBank IndonesiaBank IndonesiaBank IndonesiaBank Indonesia- Senior Deputy Governor- Deputy Governor of Banking Research and Regulations- Deputy Governor of Bank SupervisionDeposit Insurance CorporationDeposit Insurance CorporationDeposit Insurance CorporationDeposit Insurance CorporationDeposit Insurance Corporation- Executive Director of the Deposit Insurance Corporation (LPS)

Consisting of 18 members, namely six officers from the second echelon of theMinistry of Finance, six directors from related departments of Bank Indonesia and twodirectors from the Deposit Insurance Corporation (LPS).

The Working Group is made up of officials from the Ministry of Finance, BankIndonesia and the Deposit Insurance Corporation (LPS) based on proposals fromrespective institutions and agreed by the Directive Forum. In addition, it is possible toform a Task Force for specific projects, for example IFSA and FSAP.

Directive ForumDirective ForumDirective ForumDirective ForumDirective Forum works to provide directionsfor the Executive Forum

Executive ForumExecutive ForumExecutive ForumExecutive ForumExecutive Forum works to implement thefunctions of the FSS Forum in line with thedirection provided by the Directive Forum.

Working Group Working Group Working Group Working Group Working Group functions to support thework of the Directive Forum and theExecutive Forum.

Table 4.3Structure and Membership of Financial System Stability Forum (FSSF)

F o r u m M e m b e r s

60


the Ministry of Finance and reorganization in Bank

Indonesia affected FSSF membership, therefore, on 29th

June 2007 the following joint decrees were signed between

the Minister of Finance, the Governor of Bank Indonesia

and the Chairman of LPS: No 299/KMK.010/2007, No 9/

27/KEP.GBI/2007, and No 015/DK-LPS/VI/2007. In essence,

the joint decrees solemnize new memberships and reiterate

the role of the Forum. FSSF has operated and met regularly

since 1st July 2007. The Directive Forum (Forum Pengarah)

meets quarterly and the Executive Forum (Forum Pelaksana)

meets monthly every second Monday. In addition, meetings

are also held at the Working Group (Tim Kerja) level.

In the near future, FSSF will act as the coordination

centre for the implementation of the Financial Sector

Assessment Program (FSAP) and Indonesian Financial

System Architecture (IFSA). An FSSF working group will

collaborate to prepare and implement FSAP, which will be

conducted by the World Bank and IMF. The goal of FSAP

is to assess the resilience of the financial market and assess

financial system compliance to international standards

regarding prudential principles. FSSK working group will

also coordinate and harmonize IFSA, which directs the

Indonesian financial system over the mid and long term.

In the latest developments, at the FSSF Executive

Forum held on 13th August 2007 the establishment of two

working groups was agreed upon to coordinate the Macro

Early Warning System (EWS) and Crisis Management

Protocol (CMP). The Macro EWS working group is expected

to review a potential global crisis; how long would the

crisis persist and what steps could be taken. Meanwhile,

the CMP working group will provide guidance to the

financial authority on handling a crisis. CMP will outline

the crisis management mechanism for banks, non-bank

financial institutions, the capital markets and financial

markets. With CMP, the relevant authorities will provide

an accurate and effective response to address the crisis,

therefore, minimizing any negative impacts to the financial

system.

61

The Dynamics of Banking Industrial Structure, Strategic Risk, and Their Implications on Financial Systems Stability

Ar t ic les

62



63


The Dynamics of Banking Industrial Structure, StrategicRisk, and Their Implications on Financial Systems Stability

Muliaman D. Hadad1 , Wimboh Santoso2 , Bambang Hermanto3

Dwityapoetra S. Besar4 , Ita Rulina W. S5

This paper aims to analyze the structure, dynamics, and performance of banking industry against industrial

stability and strategic risk by using concentration indices, among others: HHI, HTI, CR 15, and HHI-CR 15. In

addition, Markov probability transition matrix was employed to examine the dynamics and industrial risk. The

industrial stability and strategic risk were measured by entropy values of performance rank dynamics both on the

industry and individual levels, while industrial performance was determined with profitability and share ratio.

Using monthly banking financial reports, we includes the entire commercial banks in Indonesia for the periods of

September 2000 (156 banks) through May 2006 (131 banks). The research demonstrates that Indonesian banking

industry is still in a stable condition, as normal competition level has not yet occurred. In terms of asset measure

and performance, however, a tight competition is found to be discrete within the inter sub-industry. The tight

competition exists in the medium sub-banks. The research also shows that declining number of banks within the

industry has been followed by lowering concentration indices, particularly HHI and HTI, as well as decreasing

market share of 15 large banks. In the initial period, the 15 large banks had approximately 70% market shares

and subsequently fell to about 60% in the end of the period. Mathematically, the declining number of banks

within the industry shall be followed by rising concentration index, ceteris paribus. In contrast, the research

demonstrates reversing facts which indicate industrial consolidation process is formed.

1 Deputy Governor of Bank Indonesia. This article was written by Muliaman D. Hadadwhen he was the Director of the Directorate of Banking Research and Regulation, BankIndonesia; e-mail addressΩ: [email protected]

2 Head of Financial System Stability Bureau, Directorate of Banking Research and Regulation,Bank Indonesia; e-mail address: [email protected]

3 Lecturer of Economics Faculty, University of Indonesia; e-mail address:[email protected]

4 Senior Researcher at Financial System Stability Bureau, Directorate of Banking Researchand Regulation, Bank Indonesia; e-mail address: [email protected]

5 Researcher at Financial System Stability Bureau, Directorate of Banking Research andRegulation, Bank Indonesia; e-mail address: [email protected]

1. INTRODUCTION

The purpose of this research is to measure one type

of risks, namely strategic risk in Indonesian banking industry

regarding the stability in banking industry, by using

Industrial Organization approach. The Structure√Conduct√

64


2. METHODOLOGY

This research used monthly population data of

commercial banks as of March 2006 during periods

between September 2000 and May 2006, covering

variables, among others: Asset, Costs, Profit, Capital,

Credit, Third Party Fund, and Incomes.

An industrial organization approach, Structure√

Conduct√Performance paradigm was operationally

translated into measuring-variable forms, namely:

concentration index, time series movement, and entropy.

The work flow of the research is presented in Figure A1.1.

Performance paradigm was operationally translated in the

forms of measuring variables, namely: concentration index,

time series measurement, and entropy.

It is generally agreed that market concentration is

one of the significant determinants of competition level,

although highly concentrated market does not necessarily

reflect the scarcity of competitive behavior in the market

(Nathan and Neavel, 1989). Both studies provide empirical

evidences which suggest that highly market concentration

tends to reduce the degree of competition in the banking

sector (see among others Gilbert, 1984 and Bhattacharya

and Das, 2003). Following Rueffli (1990), this research

subsequently associated competition with risk, and thus

industrial stability.

Having no references of its kind, this research is the

first study so that it is more explorative with limited initial

analysis. The research report is systematically presented in

the followings.

Part 2 Part 2 Part 2 Part 2 Part 2 will outline methodology employed to review

industrial structure, industrial dynamics, and the

measures of performance, risk, and stability in the

banking industry.

Part 3 Part 3 Part 3 Part 3 Part 3 will present the results of static concentration

measures of assets, credits, and Third Party Deposits (TPD)

in terms of HHI, HTI, and Static Entropy of both industrial

and sub-industrial group market shares as industrial

structure indicator variables with their changes from

beginning to ending period of the research. It will also

present relative entropy of banking industry rank dynamics

from various variables during the observation period to be

used as the banking industry stability measurements;

individual entropy of fifteen large banks as strategic risk

measurements, and changing dynamics of individual large

banks in the sub-groups.

Section IV Section IV Section IV Section IV Section IV will recapitulate findings in the previous

parts by utilizing framework of industrial organization.

Industrial Concentration

Measures of Static Concentration

One of static concentration measures popularly used

is the Herfindahl-Hirschman Index (HHI). For n firms in an

industry with market-share of Si, and the HHI is:

Figure A1.1Research Framework

Relative Ranking MatricesIncident Matrices

Frequence TransitionRank Matrices (total)

Dynamic Entropy ofPerformance Rank (system)

Dynamic Entropy ofPerformance Rank (individual)

Markov ProbabilityTransition Matrices (every 4 periods)

Frequence TransitionRank Matrices (every 4 periods)

Dynamic Size,Strategic Risk,

and System Risk

Descriptive Statistic(industry and group)

Static Concentration Size(industry and group)

Markov ProbabilityTransition Matrices (total)

The more similar size of the firm, the HHI will be

smaller. By definition, HHI will have values between 10,000/

n and 10,000 where n is the number of firms in an industry.

HHI =

65


This index provides weightings for a firm»s market-

share in relation to its relative ranks. The number of banks

should be taken into account when calculating

concentration index to reflect existing dominant players.

Relative Entropy Measure

The concept of entropy derived from information

theory aims at measuring the degree of information about

ex-ante expectation of some distribution. It is formulated

as:

industry (system) stability are also measured by using the

entropy.

System Risk Measure

The information needed to measure uncertainty for

a system can be obtained from probability distributions

which describe system behavior. This research uses a

conditional relative entropy measure H(KIJ)rel to represent

the relative uncertainty of the system. H(KIJ) is an average

conditional absolute entropy of the system and is defined

as:

Maximum concentration index is 10,000 when a firm is a

monopolist in an industry. Other measures of static

concentration extensively used in many industrial

organization studies are follows.

The Hall-Tideman Index (HTI)

HTI which is defined as:

HTI =

Entropy-Share =

As the entropy-based measurement has a more general

character and is relatively easier to apply, its use in the

context of measuring market concentration is often

recommended in numerous textbooks. This research would

also measure the entropy of market share in the banking

sector in Indonesia.

Strategic Risk and Stability

The strategic risk concept is based on ordinal

approach, following Collins and Rueffli (1992). This

strategic risk in this context is translated as the probability

of a firm in experiencing a loss of relative competitive

position in its business sector. Strategic risk and banking

H(KIJ) indicates the information value carried by

observations on the system if sequence of firm»s ranks at

the moment is given, while H*(KIJ) = is maximum average

conditional entropy of the system and has the same value

as()lnn. Thus, relative entropy of performance rank

dynamics H(KIJ)rel is:

H(KIJ)rel expresses relative uncertainty of a system

which constitutes the value of average information about

a transition of a firm in the system between two conditions

in two different periods of time, given initial condition of

each firms. H(KIJ)rel has the range values of 0 to 1. If the

system is definitely certain, i.e. if the probability of all

conditioned-output is 0 or 1, then the entropy of the

system, H(KIJ)rel, is 0; and if the entropy of the system is

close to the possible maximum entropy, H*(KIJ), H(KIJ)rel

is close to 1. This means that when the probability of

individual transition in the transitional matrix is close to

1/n, then the entropy will be close to maximum entropy

(or the system is random).

H(KIJ) =

H(KIJ)rel =

66


Individual Strategic Risk Measure

As noted earlier, assuming is the total numbers of

transitions experienced by firm i moving from rank j to

rank k. Next, given that for all firms pk|j = N.jk/N.j., then

the expected-weighted value of information associated

with conditional rank transition can be defined as:

h(k|j) = - [N.jk/N.j.]ln(pk|j)

Furthermore, the expected value of total weighted-

information associated with the transitions in the system

from rank j to rank k, which is assigned to an individual

firm i, can be formulated by using individual entropy as

follows:

hi(k|j) = - [Nijk/N.j.]ln(pk|j)

The portion of total uncertainty contributed by firm i in

the system is determined by:

a modified BCG matrix of industrial organization discipline

is used. This is because the number of time-series data is

too small to analyze econometrically. The modification is

made into Matrixes of Share-Performance, Share-Risk,

and Risk-Performance in two different periods of time

for comparison reasons; at the beginning and ending

period

3. BANKING INDUSTRY STRUCTURE, INDUSTRY

STABILITY, AND BANK STRATEGIC RISK

Competition pattern in an industry is determined by

industry structure which is measured by: number of players,

existence of dominant players and level of industry

concentration. Results of statistical computation for several

banking industry concentration indices during the research

periods by various variables are presented in the followings:

Concentration Measurements: Assets, Credits

and Third Party Deposits

It can be seen from the table that the HHI-asset

average is 888, which means that Indonesian banking

industry is relatively unconcentrated in which four big

players are unable to dominate the market or big players»

market power is small. This is consistent with CR-15

average indicating that the fifteen banks with the biggest

asset values, collectively, dominates sixty eight percents of

banking assets on the average, with the standard deviation

of two percents; and so are with the small value of HTI

index, the trends of time-series for both asset concentration

indexes, HHI, HHI-15 and HTI tend to keep declining, as

shown in Graph A1.1 below. The trend suggests that big

players» market power is getting smaller.

The interesting fact above is that the declining asset

concentration indexes coincide with the lowering number

of banks which reach 16%. At the beginning period, 156

banks were recorded in the research but it became only

131 banks at the ending period. The declining number of

Figure A1.2Dynamics Matrix of Risk-Performance√

Beginning/Ending Period of T

Dynamics Matrix Risk-Performance - at Period of T

Performance

Risk

1Balance

Laggart Superior

Alert2

3 4

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Source: processed

Notes :- Quadrant 1 : position of individual banks with risk and performance values of more than

group average (Balance)- Quadrant 2 : position of individual banks with performance value of less than but with

risk value of more than group average (Alert)- Quadrant 3 : position of individual banks with risk and performance values of less than

group average (Laggard)- Quadrant 4 : position of individual banks with performance value of more than but risk

value of less than group average (Superior)

HWi(K|J)rel = HWi(K|J)/ HW*(K|J).

For a purpose of movement dynamics analyses of

individual bank associated with risk and performance,

HWi(K|J) =

67


HHI AVG 10.000/n < HHI < 10.000 888.937 601.451 890.798SD 151.718 31.995 113.625CV 5.859 18.798 7.840

HHI - CR 15 AVG 10.000/n < HHI-15 < 10.000 870.096 568.062 717.506SD 153.562 41.908 105.800CV 5.666 13.555 6.782

ENTROPY - STATIC AVG 0 < ENTROPY-STA < 1 0.643 0.701 0.648SD 0.023 0.010 0.025CV 28.514 71.702 25.571

CR-15 AVG 0 < CR-15 < 1 0.681 0.588 0.549SD 0.025 0.047 0.027CV 26.918 12.390 20.038

HTI AVG 0 < HTI < 1 0.042 0.034 0.045SD 0.003 0.002 0.002CV 15.792 19.543 19.020

banks should theoretically be accompanied by the

increasing concentration indexes, or rising market power

in the banking industry.

This result is consistent with Bikker and Haaf (2002)

who have conducted similar study in numerous countries

and Bhattacharya and Das (2003) who have investigated

concentration dynamics of banking sector in India.

Credit market concentration is measured by

numerous static concentration measures. As shown in

Table A1.1, it indicates that average-level of player»s market

power is smaller than the asset concentration. Under

Table A1.1.Mean, Standard Deviation and Several Static Banking Industry Concentration Coefficient Covariation

R A N G E A S S E T S L O A N S D E P O S I T S

Source: processed

Total Bank HHI 15 Large HHI HTI 15 Large Share

SepNov JanMarMayJul Sep NovJanMarMayJul Sep Nov JanMarMayJul Sep Nov JanMarMayJul Sep Nov JanMarMayJul Sep Nov JanMarMay

2000 2001 2002 2003 2004 2005 2006

0

200

400

600

800

1000

1200

1400

Graph A1.1Movements of HHI, HHI-15, CR-15, and Banks Number

Based on Assets : 06/2000 √ 05/2006

0.54

0.56

0.58

0.6

0.62

0.64

0.66

0.68

0.7


2000 2001 2002 2003 2004 2005 2006

Graph A1.2Movements of Entropy Share Based on Assets :

06/2000-05/2006

Note: Market share of 15 large banks is multiplied by 1000 in purpose to adjust the graph scale.Source : processed Source : processed

Graph A1.3 below, it shows that from early 2002 to early

2005, there is an increase on HHI Industry, HHI-CR 15,

and CR-15, which coincides with the declining share

entropy. It also indicates that the player»s market power is

increasing during that period, but starting from mid-2002

to the first quarter of 2006 the HHI and HTI indices just

return to a decrease while the entropy values are rising

meaning that the market power is declining.

Absolute concentration index value of TPD is closer

to the asset index but it is different from that of credit

index. Hence, the interpretation and understanding of the

68


results over the competition are in line with the asset index.

Table A1.1 above also illustrates that, on average, market

power of TPD is relatively low throughout the research

period. Similar to the asset, there is a trend in the declining

power of dominance over the market for the large banks

as could be inferred from the lowering trends of HHI and

HTI, while market entropy values are getting close to one

as shown in the following graph. From the competition

side, it is found that the competition in the credit market

is more intense than in the TPD market.

Industry Stability and Strategic Risk

Banking industrial stability is reflected from the

random intensity of bank performance ranks, resulting

from the competition in the inputs and outputs market,

as indicated by Rank Dynamics of Relative Entropy (RDRE)

performance of all banks in the industry. This industry

stability is closely related to banks» strategic risk measured

by individual Rank Dynamics of Absolute Entropy since

both are derived from the same Markov probability

transition matrix.

Scatter diagram of transitional probability distribution

by assets and ROIs shown in Figure A1.3 and Figure A1.4

below indicates that values of transitional probability, both

ROI and asset ranks, scatter along the diagonal line

spreading across the matrix in a regular way. This suggests

that the increase or decrease of performance ranks occurs

within a time-range in a relatively less random way. But

Graph A1.4Movements of Entropy Share Based on Loans :

09/2000-05/2006

0.67

0.68

0.69

0.7

0.71

0.72

0.73


2000 2001 2002 2003 2004 2005 2006Source : processed


Based on Loans : 06/2000 √ 05/2006

0

100

200

300

400

500

600

700


2000 2001 2002 2003 2004 2005 2006


Note: Market share of 15 large banks is multiplied by 1000 in purpose to adjust the graph scale.Source : processed

Graph A1.6Movements of Entropy Share Based on Deposits :

09/2000-05/2006

Note: Market share of 15 large banks is multiplied by 1000 in purpose to adjust the graph scale.Source : processed

Source : processed


Based on Deposits : 06/2000 √ 05/2006

0

200

400

600

800

1000

1200


2000 2001 2002 2003 2004 2005 2006


0.54

0.56

0.58

0.60

0.62

0.64

0.66

0.68

0.70


2000 2001 2002 2003 2004 2005 2006

69


are presentation and analyses on the computational results

of entropy in the banking industry.

Entropy Performance of 15 Large Banks

Absolute entropy shows the pattern of individual

bank»s behavior dynamics over time where high dynamics

indicates the uncertainty of position, both in obtaining

market and achieving performance, and is used as the

strategic risk indicators/measures. Table A.1.2 contains a

computational results summary of quarterly absolute

entropy for fifteen large banks.

From the table, it can be seen that there is a decline

in the average entropy from beginning to ending period

of research; four of five banks with the least entropy and

smaller than average entropy at beginning period maintain

their positions until the end of period. In contrast, five

entropies at the beginning period rank the highest with

four of them have experienced position-swapped amongst

the banks; and further, only two banks retain its high

entropy, namely IIHB dan IIHI. In other words, high entropy

does show higher dynamics level than the small entropy.

Entropy»s position of the bank coded with IIPI, although

on the average possesses the smallest entropy, turns out

to have always been above the average of the fifteen large

banks» from beginning to ending period, which implies

that only in the middle of the period does the bank»s

entropy fall far below the average. The entropy figures of

time series movement of the fifteen large banks during

the research period are attached as the Appendix.

Strategic Risk Dynamics and Individual

Performance of 15 Largest Banks

Empirically there has no reference which states a

pattern of relationship between strategic risk and

performance of a bank. Matrices of Risk-Performance,

Share-Performance, and Share-Risk were used to plot the

relative positions of individual banks at the beginning and

Figure A.1.3Distribution Pattern of Banking Industry

ROI Rank Transition Probability

Figure A.1.4Distribution Pattern of Banking Industry

Assets Rank Transition Probability

some pattern is visible: on both ends of diagonal ranges,

these are relatively narrow; three to seven on ROI ranks

and then it becomes wider around the center of the

diagonal line, between ranks twenties to eighties with a

range of twenty to seventy ranks. Similar pattern is also

found on the RDRE asset but with narrower range. This

pattern indicates that it is the banks in the medium rank,

rank twenties to eighties as the most dynamics groups,

which have greater probability to trigger the industrial

instability than are the top and low rank groups. Markov

probability transition matrix serves as the basis to calculate

banking industry stability, rank dynamics of relative entropy,

and certain individual bank»s strategic risk. The followings

70


ending period of the research as a means to classify and

describe the dynamics and their implications on the

banking industry stability. The plotting results by various

matrixes are presented in the following.

Absolute Entropy versus ROI of 15 Large Banks

From the matrix of risk-performance dynamics below,

it can be seen that, firstly, distribution of each individual

banks in the respective quadrants from beginning to ending

research period remains unchanged; secondly, there is no

linear pattern of relationship between performance and

strategic risk. Four individual banks (IIHP, IIIR, IIPI, and IIPP)

are seen to have above-average performances, but only

one individual bank (IIPI) indicates above-average absolute

entropy value or to be in quadrant balance, while the other

11 banks are in the below-average performance quadrants.

Table A.1.2 contains figures of variable values

indicating the positions of 15 individual large banks in the

matrix quadrant above, from beginning to ending period

of sampling.

Figure A.1.5Risk Dynamics Matrix-Absolut Entropy and ROI

Performance of 15 Large Banks - Beginning and Endingof Sample Period

Source : processed

ROI

Abs

olut

e En

trop

y

Absolute Entropy vs ROI Standard-Beginning of Sample Period

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

ROI

Abs

olut

e En

trop

y

Absolute Entropy vs ROI Standard-Ending of Sample Period

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

-3 -2 -1 0 1 2 3 4 5 6 7

1 Iipi 0.01052 Iihp 0.01543 Iiir 0.01734 Iipp 0.01875 Iipb 0.02036 Iisi 0.02267 Irsb 0.02328 Iiap 0.02409 Iiar 0.024210 Iihb 0.024311 Irrb 0.024612 Iaia 0.024813 Iisb 0.025114 Iibi 0.025515 Iihi 0.0260

Average 0.0218

1 Iisb 0.01572 Iihp 0.01573 Iipp 0.02144 Iiir 0.02145 Iaia 0.02146 Irsb 0.02707 Irrb 0.02708 Iisi 0.02709 Iipi 0.027010 Iipb 0.027011 Iiar 0.027012 Iihi 0.027013 Iihb 0.027014 Iibi 0.027015 iiap 0.0270

Average 0.0244

1 Iipp 0.00332 Iipb 0.01233 Iihp 0.01574 Iiir 0.01575 Iaia 0.02146 Iiap 0.02147 Iiar 0.02148 Irrb 0.02149 Irsb 0.027010 Iibi 0.027011 Iihb 0.027012 Iihi 0.027013 Iipi 0.027014 Iisb 0.027015 iisi 0.0270

Average 0.0215

Table A.1.2ROI Rank Dynamic Absolute Entropy - 15 Large Banks

Average Value During Research Period, Q-II 2001 and Q-I 2006

Source: processed

No. Code of15 Large Banks

Average

Individual EntropyNo. Code of

15 Large Banks

Average

Beginning Positionof Period

No. Code of15 Large Banks

Average

End Positionof Period

71


Market share is often used as a performance measure

in obtaining market. In order to find out the behavior of

individual banks over time, the relationship pattern

between market share and ROI is presented in the following

section.

Market Share versus ROI of 15 Large Banks

The dynamics matrix below shows the position of

15 large banks by their market shares and performances.

Similar to the entropy-ROI matrix above, it can be seen

that there are four individual banks (IIP, IIIP, IIPI, and IIPP)

which have above-average market share and performance

and remains steady from beginning to ending period of

sampling. The other 11 banks are assembled on quadrant

III having below-average market share and performance

which implies that both matrices below also demonstrates

the probability of linear-relations between market and

financial performance, although they are accompanied by

heteroskedastic phenomenon or a non-linear change at

the end of the period.

To provide detailed-information regarding the above

matrices, Figure A1.6 below contains the position of 15

large banks from beginning to ending period of sampling.

Bank intermediation function in putting the third party

deposit collection and credit distribution into equilibrium

has attracted public concerns most of which has centered

around the low-optimum level of credit distribution

functions. This research also examined these realities in a

more comprehensive manner by examining behavior

pattern of TPD growth/credit growth and ROI of 15 large

banks.

A measure of equilibrium for bank intermediation

function could be indicated by TPD growth-level in

proportion to credit growth level. In the next section, a

review on the relationship pattern of comparison between

TPD and credit growth against performance (ROI) of 15

large banks would be presented.

TPD Growth / Credit Growth versus ROI of 15

Large Banks

The ratio between TPD growth and credit growth

from two different points of time, in addition to the

previous matrix, may demonstrate strategy focus stability

and policy of bank resources allocation. As shown in the

matrix below, the resources allocation to credit market is

relatively high for the majority of banks, as is with the four

best performance banks lined-up in quadrant IV or superior

(i.e. IIHP, IIR, IIPI, and IIPP). There are only 2 banks (IIBI,

IIHB) which show the contrary, along the beginning to

ending period of sampling, where their positions are

replaced by IIAR and IIRB. And when the credit qualities of

both banks are becoming less or poor, this condition may

Figure A.1.6Risk Dynamics Matrix-Market Share and ROI Performance

of 15 Large Banks - Beginning and Endingof Sample Period

Source : processed

ROI

Mar

ket S

hare

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

Market Share vs ROI Standard-Beginning of Sample Period

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Market Share vs ROI Standard-Ending of Sample Period

ROI

Mar

ket S

hare

-0.06-0.04-0.02

00.020.040.060.08

0.10.120.14

-3 -2 -1 0 1 2 3 4 5 6 7

72


undermine future performance and in turn becomes the

trigger for banking industry instability. Relative positions

of the remaining large banks in this matrix are stable from

beginning until the end of the period.

Individual banks dynamics for TPD growth/credit

growth variables appear only two individual banks having

above-average values, which imply two meanings:

whether the individual banks» TPD value is relatively great

or else its credit distribution value is relatively small.

Examining the data, it indicates that both banks have too

much TPD collection whereas their credit distributions are

relatively small (e.g. IIBI has TPD share of 5% with 0.6%

credit share). However, ROI performance of both individual

banks is relatively below average which means that both

banks have been attempting to focus themselves on

market expansion (inputs or outputs) with relatively low

margins. Table 3.6 below presents individual banks

positions of the 15 large banks from beginning to ending

period of sampling

As shown in the presentation above, the change or

movement of 15 large banks is relatively small, only few

individual banks demonstrate significant changes from low

to high positions in terms of their average values of this

group (absolute entropy, market share, gD/gK, ROI). Even

more, the individual banks are seen to have high market

share but their performance levels are relatively low which

indicate a mass product activity with low market gain and

margin orientation.

The three relationships above (Section 3.6.1-3.6.13)

demonstrate a dynamics map within the 15 large banks in

which four individual banks (IIHP, IIIR, IIPI, IIPP) tend to have

above-average performance levels, whereas the rest have

below-average levels. The movements of those 15 large

banks are relatively stable over time, although several

individuals (IIBI, IIHB, IIHI, IISB, IIPI, IISI, and IRSB) show

above-average absolute entropy values. What draws

attention is the competition dynamics among eleven

individual banks (other than the four high-performing

banks) which demonstrate changes and position swaps

from beginning to ending period of sampling. The tight

competition among these eleven banks is probably due to

their relatively similar measures compared to the four high-

performing banks.

OEOI vs. ROI of 15 Large Banks

From the dynamics matrix below, it is shown that

four individual banks with relatively above-average

performances demonstrate relatively high Operating

Expense to Operating Income (OEOI) values (IIHP, IIIIR, IIPI,

IIPP). This indicates that those 4 banks possess relatively

high operational costs but their ROI performances are also

relatively high. In contrast, the other 11 banks show

relatively below-average OEOI values with relatively low

performance within their group. In other words, the 15

Figure A.1.7Risk Dynamics Matrix-Deposits Growth/Loans Growth

and ROI Performance of 15 Large Banks- Beginning andEnding of Sample Period

Source : processed

Deposit Growth/Loan Growth (gD/gL) vs ROI Standard-Beginning of Sample Period

ROI

gD/g

L

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

-3-2-101234567

Deposit Growth/Loan Growth (gD/gL) vs ROI Standard-Ending of Sample Period

-20-10

0

10203040

5060

3-3 -2 -1 0 1 2 4 5 6 7

ROI

gD/g

L

73


large banks could be classified into 2 groups, namely those

in quadrant I with above-average OEOI values and

performance, and those in quadrant III with below-average

OEOI values and performance. This phenomenon is

consistent with that of the relationship between linear

market-ROI and heteroskedasticity.

Relationship pattern between OEOI and ROI in the

two different points of time observation seems illogical,

counter intuitive. But, in fact, it occurs in other countries

too (see Neceur, 2003 and Bhattacharya, 2003), and thus

it could be concluded that the pattern constitutes unique

to the banking industry. Figure A1.8 below demonstrates

the positions of individual banks of the fifteen large banks

from beginning to ending period of sampling.

The table below provides additional information to

the above figure showing that there is no significant

change or movement, by observing relationship pattern

of OEOI and ROI of those 15 large banks. The apparent

changes in positions are still seen in the same quadrant

from beginning to ending period. This OEOI and ROI

relationship also demonstrates the same result with the

previous discussion in which the segregation in this group

happens, namely four banks with relatively high ROI and

OEOI and the other eleven banks with relatively low ROI

and OEOI.

4. CONCLUSION

The purpose of this explorative research is to find

out the relationship between structure, dynamics, and

performance in the banking industry, especially industrial

stability and strategic risk of individual banks by using

industrial organization approach. The findings and

limitations can be summarized as follows.

1. Bank grouping is done on the basis of numerous

criteria of Central Bank (Bank Indonesia) supported

by the existence of differences in statistical

distributions of various financial variables amongst

the groups. Those groups could be considered as

banking industry sub-groups which are materially

discrete.

2. A gradual consolidation in the banking industry exists,

as the number of banks declines and the big players»

market powers become less, whereas medium

players» market power are on the increase. This implies

some possibilities. First, the liquidated banks»

customers do not move to the large banks. Second,

the medium banks succeed in attracting new

customers. Third, the medium banks manage to get

the large banks» old customers. Fourth, the

combination of the three above.

3. Related to point 2, for some Indonesian bank

customers and to the extent of relationship to the

trade off between return and risk, the large banks»

services/products are the same as medium/small

Figure A.1.8.Risk Dynamics Matrix-Kinerja OEOI and ROI Performance

of 15 Large Banks-Beginning and Endingof Sample Period

Source : processed

OEOI vs ROI Standard-Beginning of Sample Period

ROI

OEO

I

-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

OEOI vs ROI Standard-Ending of Sample Period

ROI

OEO

I

-0.1-0.05

00.05

0.10.15

0.20.25

-3 -2 -1 0 1 2 3 4 5 6 7

74


banks». This will likely make asymmetric information

problems, popularly known as Akerloff»s lemon

market, more serious in Indonesian banking industry.

4. On the whole, Indonesian banking industry is in a

stable condition, as normal competition level has not

happened. However, on the basis of asset measure

and performance, a tight competition would be

discrete within inter sub-industry. The tightest

competition exists in medium sub-banks.

5. The pattern of linear relationship between strategic

risk with numerous performance variables and share

can not be derived from the data. This may be

associated with non linearity risk phenomenon which

is dissimilar to clearer pattern of relationship between

share and numerous financial performances. What

unique to the banking industry is the existence of

positive relationship between profitability ratio and

OEOI ratio.

5. LIMITATIONS

Some notes concerning limitations of this research

are:

1. Being the first explorative research in strategic risk

(entropy) measurement, this research has not analyzed

and modeled the pattern of relationship of entropy

(risk) using widely-used systemic risk indicator

variables, for example Non Performing Loan.

2. From the perspective of time series investigation,

twenty points are considered so small to be used in a

research that long-term patterns could not be

covered.

3. Because the identities of individual banks are hidden

in this research, the researcher has not recognized

them, validation of the results are made on the basis

of group identification of consistent variable patterns.

4. Given the individual bank analysis done only on 15

large banks, the findings above do not represent the

medium banks.

6. POLICY IMPLICATIONS

The above conclusions and limitations provide some

implications for policy makings:

1. In relation to banking industry restructuring, API

(Arsitektur Perbankan Indonesia), understanding over

banking industry behavior by the sub-clusters of

measures and dynamics within a cluster and inter-

cluster will assist in developing future banking industry

scenario. Thus, it is important to measure and publish

the statistics of concentration indicators,

competitions, and industrial and sub-industrial risks

as a means to monitor the industrial dynamics.

2. Referring to lemon market problem which is generally

attributable to financial industry, it is institutionally

necessary to establish an independent rating agency

which prepares an assessment and rating on banks

and financial institutions under various performance

measures, especially risk, to provide customers with

a balanced information in selecting banks and

financial services.

3. Concerning with bank strategic risk control, the

grouping of individual bank strategic behavior can

be of great help, but further study focusing on

medium banks group is needed.

75


Avi Fiegenbaum and Howard Thomas (2004),Δ Strategic

risk and competitive advantage: an integrative

perspectiveΔ, European Management Review (2004)

1, 84√95

Basel Committee on Banking Supervision. 1998. Risk

Management for Electronic Banking and Electronic

Money Activities.

Basel Committee on Banking Supervision. 2001. Working

Paper on the Regulatory Treatment of Operational

Risk.

Basel Committee on Banking Supervision. 2003. Risk

Management Principles for Electronic Banking.

Basel Committee on Banking Supervision. 2003. Sound

Practices for the Management and Supervision of

Operational Risk.

Ben Naceur, Samy.October (2003).ΔThe Determinants of

The Tunisian Banking Industry Profitability: Panel

EvidenceΔ. Universite» Libre de Tunis. Department of

Finance.

Boss, Michael, Helmut Elsinger, Martin Summer, dan Stefan

turner (2004),ΔThe Network Topology of the

Interbank MarketΔ, Working Paper.

Boss, Michael, Helmut Elsinger, Martin Summer, dan Stefan

turner (2003), ≈An Empirical Analysis of the Network

Structure of the Austrian Interbank MarketΔ, Financial

Stability Report 7.

Collins, James M and Ruefli, Timothy W (1992), Δ Strategic

Risk : An Ordinal ApproachΔ, Management Science

Vol.38 No.12

Elsinger, Helmut, Alferd Lehar, dan Martin Summer

(2006),ΔUsing Market Information for Banking System

Risk AssessmentΔ, International Journal of Central

Banking, Vol 2 No 1.

Elsinger, Helmut, Alferd Lehar, dan Martin Summer,≈A New

Approach to Assessing the Risk of Interbank LoansΔ,

Financial Stability Report 3.

References

Freddy Delbaen (2000), ≈Coherent Risk Measures on

General Probability SpacesΔ, ¨ossische Technische

Hochschule, Z¨urich ,March 10

Jorion, Philippe. (2001). Value at Risk: The New Benchmark

for Managing Financial Risk, 2nd edition. New York:

McGraw-Hill.

Kaushik Bhattacharya and Abhiman Das (2003),Δ Dynamics

of Market Structure and Competitiveness of the

Banking Sector in India and its Impact on Output and

Prices of Banking ServicesΔ, Reserve Bank of India

Occasional Papers Vol. 24, No. 3, Winter.

Mehra Ajay (1996), Δ Resources and Market based

Determinants of Performance in the US Banking

IndustryΔ, Journal of Strategic Management Vol 17

No.4

Peraturan Bank Indonesia No 5/8/PBI/2003 tanggal 19 Mei

2003 tentang Penerapan Manajemen Risiko.

Ruefli, Timothy W (1990),Δ Mean-Variance Approaches

to Risk-Return Relationships in Strategy: Paradox

LostΔ,Management Science; Mar 1990; 36, 3; ABI/

INFORM Global pg. 368

Ruefli, Timothy W.; Wilson, Chester L. (1987), ΔOrdinal

Time Series Methodology for Industry and

Competitive AnalysisΔ, Management Science, May;

33, 5; ABI/INFORM Global pg. 640

The Financial Services Roundtable. (1999). Guiding

Principals in Risk Management for U.S. Commercial

Banks: A Report of The Subcommittee and Working

Group on Risk Management Principles. Washington

D.C.

Winfrey, Frank L. dan James L. Budd (1997),Δ Reframing

Strategic RiskΔ, S.A.M. Advanced Management

Journal. Autumn; 62, 4; ABI/INFORM Global, hal. 13-

21.

76



77

Credit Risk Modelling : Rating Transition Matrices

Credit Risk Modelling:Rating Transition Matrices

Muliaman D. Hadad1 , Wimboh Santoso2 , Bagus Santoso3

Dwityapoetra S. Besar4 , Ita Rulina W. S5

1. INTRODUCTION

Credit risk remains the dominant problem

confronting banks. Nevertheless, banks need to identify,

monitor and control credit risk as well as ensure capital

This paper aims to estimate a credit rating transition matrix, specifically used to identify: rating migration

at a certain period, the heterogeneity of rating migration and the volatility level of rating migration. By using

company ratings and debt specific ratings published by PT Pemeringkat Efek Indonesia (Pefindo) in February

2001 up to June 2006, we calculate the transition matrices, using both Cohort and Continuous methods. We

performed annually, bi-annually (2004-2005), every three years (2003-2005), every four years (2002-2005)

and five years (2001-2005) for both methods. The result shows us that continuous method provides more

efficient results than the Cohort method. Additionally, estimations using the continuous method are best for

corporate or bond ratings, producing transition matrices with a more spread probability distribution. In terms

of the rating migration trend, estimation results using Cohort and continuous methods provided relatively

consistent results. Rating migration tends to upgrade, which is consistent with the analysis conducted on

rating activity and rating drift.

adequacy to anticipate the risk (Basel Committee on

Banking Supervision, 1999). Basel II confirmed that financial

institutions must have the ability to analyze credit models

and internal ratings to ensure the model is calibrated to

measure credit risk consistently and meaningfully. Van

Deventer and Imai (2003) specifically mentioned that credit

risk is the major reason for bank default.

There are several difficulties in determining credit

risk solutions that cover a number of companies. First, credit

risk has different types and sizes. Second, the different

1 Deputy Governor of Bank Indonesia. This article was written by Muliaman D. Hadadwhen he was the Director of the Directorate of Banking Research and Regulation, BankIndonesia; e-mail addressΩ: [email protected]

2 Head of Financial System Stability Bureau √ Directorate of Banking Research andRegulation, Bank IndonesiaΩ; email address: [email protected]

3 Researcher at University of Gadjah Mada, Yogyakarta; email address:[email protected]

4 Senior Bank Researcher at Financial System Stability Bureau √ Directorate of BankingResearch and Regulation, Bank Indonesia; email address: [email protected]

5 Bank Researcher at Financial System Stability Bureau √ Directorate of Banking Researchand Regulation, Bank Indonesia; email address: [email protected]

78


types of credit risk are generally managed centrally and

are closely monitored. The source of credit risk also varies

widely; from corporate or sovereign bonds, credit

derivatives, over the counter derivatives (such as interest

rate swap), commercial lending, retail mortgages and credit

cards. Third, banks tend to manage their credit risk

separately from market risk.

In measuring credit risk, Kamakura Risk Information

Services-KRIS (2004) applied three quantitative approaches

to model default probabilities, namely: Jarrow Chava

Model, Merton Structural Model and Jarrow Merton Hybrid

Model. The three approaches incorporate information

regarding a company»s equity market prices and interest

rates, so that prevailing market expectations can be

accommodated in the default probability estimates. Van

Deventer and Wang (2003) use this model by estimating

default probability explicitly using logistical regression with

a historic default database.

In addition to default probability estimates, credit

risk analysis can also be performed using risk migration

analysis (migration probability of the bond rating). The

bond rating is an important indicator to evaluate a

company»s credit quality, as well as their default probability.

A change in a company»s rating reflects the credit quality

of that company, either improved (upgrade) or

deteriorating (downgrade). Analysis of the rating»s

transition, including default, is useful in the credit risk

model to measure future credit loss. Thus, the matrix

containing rating transition probability (transition matrix)

plays an important role in credit risk modeling.

Theoretically, the transition matrix can be estimated

for the desired transition horizon. However, the matrix

commonly used is an annual or five-yearly transition matrix.

Specifically, a transition matrix illustrates the default risk

and high migration volatility of a low quality portfolio.

The default likelihood increases exponentially with a decline

in grade. All transition matrices exhibit the same

characteristic; they all have high probabilities in a diagonal

matrix: the obligor tends to maintain its current rating.

The second largest probability is around the diagonal.

Meanwhile, the farther from the diagonal, the lower the

rating transition (Violi, 2004). A study by Kryzanowski and

Menard (2001) shows that the probability of a bond

remaining at its initial rating reduces as the time horizon

analyzed becomes longer.

The discussion on credit modeling not only focuses

on the probability of default, but also analyzes what is

happening to credit that is close to default (McNulty and

Levin, 2000). For that reason, researchers began to focus

on the probability of credit rating transition from one

level to another. One of the representative ways of

presenting such information is through a transition

matrix.

2. THE OBJECTIVE OF THE RESEARCH

This research aims to estimate a credit rating

transition matrix, specifically used to identify:

- Rating migration at a certain period;

- The heterogeneity of rating migration; and

- The volatility level of rating migration.

3. LITERATURE STUDY

Transition Matrix Rating

Credit migration, or a transition matrix, indicates

changes in the quality of settled credit at a particular

company. Transition matrices are the main input in various

applications of risk management. One example, in the New

Basel Accord (BIS, 2001), capital requirement is based on

the rating migration. In 1999, the Basel Committee on

Banking Supervision (BCBS) confirmed the use of transition

matrices and has since advocated their use as a basis to

fulfill the securitization framework.

79


Credit rating is a process where any credit rating

observation can form one of several state ratings. In this

research, it is assumed that the credit rating process follows

the Markov Chain process. This means that the probability

placed on one state can only be determined by knowing

the state from its previous observation. The assumption

of Markov Chain in the credit rating process implies that

the credit transition is more time invariant or time

homogenous, where the transition probability remains the

same towards time and constant during the predetermined

horizon.

If one Markov Chain has State Space S = {1,2,º..k},

the probability of the credit rating process in state j for

one observation after being in state i in a previous

observation, is noted by pij. This p

ij is known as the

transition probability from state i to state j. A matrix with

a transition probability from State i to State j is known as

the transition matrix of Markov Chain (Anton and Roses,

1987). Subsequently, the transition matrix is noted with P.

The general format of the one step transition probability

matrix is as follows:

(3.1)

At equilibrium (3.1) above, pij verifies the

transition probability from state i at time t to state j at

time t+1. In addition, the Markov Chain transition matrix

above has the characteristic that all entries on one line

equal 1. Mathematically, that characteristic can be written

as follows:

(3.2)

The state vector x(t) for one Markov Chain

observation with State Space S = {1,2,º..k} is defined as

the vector of column x where the i component, namely xi,

is the probability of State i at time t. The column vector

can be formulated as:

(3.3)

According to theorem by Anton and Rorres (1987),

if P is the Markov Chain transition matrix and x(n) is the

state vector at observation n, it makes:

(3.4)

From 3.4, it is known that:

(3.5)

In other words, Equation 3.5 verifies that the previous

state vector x(0) and transition matrix P reveal the value of

state vector x(n).

4. SPECIFICATION OF THE TRANSITION MATRICES

APPROACH USED

In this study, a transition matrix is constructed for

both discrete and continuous timescales. Based on the

discrete approach, changes in the obligor rating (credit

score) are only monitored after a certain period of time

(fixed), such as six months, nine months, one year or other

specific periods. Meanwhile, based on the continuous

approach, any change in rating can be monitored at any

time, even minute by minute (Ahmed et al. (2004).

Building a transition matrix using the discrete

approach follows Jafry and Schuermann (2004).

Meanwhile, the transition matrix based on the continuous

approach was adapted from Lando and Skodeberg

(2002).

80


λij ≥ 0, for i≠j

(3.7)

This entry explains the probabilistic behavior of

holding time in state i as it is exponentially distributed with

parameter λi, where λi = - λii, and the probability of

shifting from state i to j is λij /λi .

The transition probability for each time horizon is

the function of the generator. Thus, we can obtain the

maximum likelihood estimator from the transition

probability matrix using the estimation from the generator.

This is subsequently applied to the exponential matrix for

the maximum likelihood estimation of that generator.

Based on the assumption of time homogeneity, the

element from the matrix generator is calculated using the

maximum likelihood estimator as performed by Kuchler

and Sorensen, 1997:

for i ≠ j (3.8)

Where:

Nij (T) : number of transitions from state rating i to state

rating j in the period.

Yij (s) : number of companies with state rating i

during s.

In other words, the denominator from Equation 3.8

shows the number of «firm-years» of all companies included

in the sample that were initially state i . Thus, the state of

each company for each period is also counted in the

denominator.

The Continuous Method with the assumption of

Time Non-Homogeneity:

According to a study carried out by Lando and

Skodeberg (2002), one of the means to calculate a

Transition Matrix, Discrete Timescale: Cohort

Method (Frequentist)

One method to calculate changes in probability from

the data estimated using a discrete timescale is the Cohort

method. The Cohort method has been widely used as it

applies simple calculations, although sometimes the results

are less efficient.

Transition Matrix, Continuous Timescale

Constructing a transition matrix using a continuous

timescale approach has fascinated many modelers in recent

years. Ahmed at al. mentioned two key elements when

applying this approach:

1. To facilitate the transition probability estimation where

the transition to a certain rating rarely occurs, for

example an indirect default (default through a

sequential downgrade)

2. To facilitate the construction of a transition matrix

for all lengths of time (for example the 73-day

transition matrix)

Continuous Method with the Assumption of

Time Homogeneity

Using this approach, we get a K-state Markov

Chain where state 1 is the highest state and state K is

default. The transition probabilities for a certain period

are calculated in matrices P(t) KxK where ij is the migration

probability from state i to state j during period t. The

generator matrix with KxK dimension is λ with

nonnegative, off diagonal entries and the number of lines

equal to zero (Israel et al., 2001), where (Lando and

Skodeberg, 2002):

P(t) = exp (λt), t ≥ 0 (3.6)

Matrix λt is matrix λ multiplied by t for each entry

and the exponential function denotes the exponential

matrix. The entry for matrix λ is:

81


The Discrete Hazard Model

A credit risk model used to analyze credit risk is

known as the hazard rate model. The hazard rate model is

a method to measure bankruptcy by including default

intensity. The model is widely used in operational

measurements. One of the applications of this model is

for pricing, bankruptcy and estimating the probability of

company default. There are two types of hazard models,

discrete hazard rate and continuous hazard rate. The

difference between the two models is in the survival

function applied. This research paper focuses on discrete

hazard. The discrete hazard model is an appropriate model

to analyze data consisting of binary observations, time-

series and cross-sectional data, like the one in cases of

bankruptcy. The hazard rate is defined in economic studies

as the transitional risk of different states. In financial

literature, the hazard rate indicates credit default risk.

5. DATA SOURCES

The data used originates from PT Pemeringkat Efek

Indonesia (Pefindo). Company ratings as well as debt

specific ratings published by Pefindo in February 2001 up

to June 2006 were used to calculate the transition matrices,

using both with discrete and continuous methods.

However, several bond ratings published by Pefindo also

contained the bond rating given by other rating agencies,

such as KASNIC.

The rating agency data published during the period

consists of a semi-annual publication, published every

February and August. The publication in February year i is

the rating agency data from 31st December year i-1,

whereas the publication in August year i is the rating data

from 31st June year i. Meanwhile, bond rating data used

in the estimation is for the period of 2001-2005, published

monthly by Pefindo, from July 2003 to June 2006; and a

semi-annual publication from 2001 to 2002. The data from

Pefindo comprises of 115 company ratings and 412 bond

transition probability matrix from continuous data,

assuming non homogeneity, is by applying the Aalen-

Johansen estimator. Based on Jafry and Schuerman (2003),

the Aalen-Johansen estimator, or non-parametric product

limit, obtained is consistent. The construction of transition

matrices using this method follows the Cohort Method

over a very brief period, such as on a daily basis

(Landschoot, 2005).

In estimating the transition matrix using a continuous

timescale and assuming non-homogeneity, is the transition

probability matrix for period [s,t]. Element ij from the matrix

notes the Markov probability process, beginning with the

transition from state i at time s to state j at time t. Then, if

several m transitions are identified during the period [s,t],

can be estimated by applying the Aalen-Johansen estimator

(Jafry and Schuerman, 2003).

(3.9)

Evaluating Rating Quality

To intensify the analysis results, several indicators

must be observed. One of the most important indicators

in evaluating the quality trend of corporate ratings is rating

activity. According to Carty and Fons (1993), rating activity

can be calculated from the sum of rating shifts, both the

upgrades and the downgrades, divided by several issuers

operating at the beginning of the year. Another important

indicator is rating drift. Rating drift is the dependency on

previous ratings and is identified as non Markovian

behavior (Lando Skodeberg, 2002). Rating drift is

calculated by the total number of upgrades subtracted by

the number of downgrades and divided by the number of

issuers operating at the beginning of the year. Based on

the sample given by Carty and Fons (1993), a rating change

from BBB to A represents one rating, whereas from BBB

to AA is a change of two ratings.

82


ratings from 119 companies. However, not all the data

could be included in the estimation due primarily to a lack

of available data at the beginning of the estimation period.

6. ANALYSIS OF THE TRANSITION MATRIX

RESULTS

6.1 Evaluating Rating Quality

Graph A2.1 illustrates that the corporate rating

quality of the sample, in general, showed improvement.

This is indicated by the decline in the percentage of

downgraded companies during 2001-2004 (from 25% to

3.23%). Nonetheless, in 2005, the percentage of

downgraded companies increased to 4%. On the other

hand, higher corporate rating quality was evidenced by a

rise in the number of upgraded companies, from 10% in

2001 to 14.3% in 2003. However, the percentage declined

again in 2004 and 2005. Since 2003, the number of

upgraded companies has exceeded the number of

downgraded companies. This is a preliminary indication

of an improvement in the conditions of the sample

companies.

This is further emphasized in Graph A2.2 where the

number of downgraded bonds has shown a declining trend

over the past five years. In 2001, the number of

downgraded sample bonds was 13.5%, while in 2005 it

was only 1.3%. In brief, Graph A2.1 and A2.2 indicate

initial improvements in the creditworthiness of sample

companies issuing bonds. This was buttressed by the fall

in both downgraded companies and bonds; as well as the

rise in the percentage of upgrades.

Rating Activity and Rating Drift:

A positive (+) rating drift shows that the number of

upgrades has surpassed the downgrades, more specifically

indicating an improvement in rating quality. Conversely, a

negative (-) rating drift shows that the number of

downgrades has surpassed the upgrades, ergo a decline

in credit quality. In brief, rating drift indicates whether a

rating shows any improvement or decline over a certain

period of time.

The rating activity and rating drift of sample

companies during 2001-2005 is presented in Graph A2.3.

It can be seen that there was a regression in letter activity

rating of the sample companies from 2001-2004. However,

in 2005, rating activity increased to 15%.

Even though the percentage of rating activity showed

a decline, conversely, the rating drift experienced an

escalating trend. This indicates that despite an

unsatisfactory activity rating for the sample companies over

the past few years, the rating is beginning to show

improvement. In 2001 and 2002, the rating drift was

negative (-), which means that the number of downgrades

exceeded the upgrades. However, the rating drift has

Graph A2.2Number of Upgraded and Downgraded Sample Bonds

13.5

6.3

2.51.3

16.8

7.1 7.48.2

5.1

7.7

0

2

4

6

8

10

12

14

16

18

2001 2002 2003 2004 2005

UpgradeDowngrade

Source: Pefindo, processed

%

Graph A2.1Number of Upgraded and Downgraded

Sample Companies

0

5

10

15

20

25

30

25.0

7.1

3.2 4.0

14.3

7.5 7.0

12.2 12.2

10.0

2001 2002 2003 2004 2005

Downgrade Upgrade


%

83


declined since 2004 but not as bad as during 2001 and

2002.

Graph A2.4 shows the letter rating activity and rating

drift of sample bonds from 2001-2005. The percentage

of letter rating activity of sample bonds has declined; from

65.4% in 2001 to 8.7% in 2005.

Despite a decline in rating activity, rating drift

improved, which is shown by its escalating trend. This

means that even though the percentage of activity rating

over the past few years experienced a decline, the rating

still showed improvement.

In 2001 and 2002, the rating drift was negative,

which means the number of downgrades exceeded the

upgrades. However, the rating drift continued to increase;

reaching 21% in 2003, which indicated that the number

of upgrades outperformed the downgrades, as

experienced by the rating drift in sample companies.

More concisely, it can be concluded that the

percentage of rating activity and sample bonds during

2001-2005 declined relatively. Nevertheless, rating activity

showed improvements as indicated by the positive rating

drift. This is initial evidence of improved creditworthiness

for sample bonds over the past few years.

6.2 Analysis of the Transition Rating Matrix

There are two main approaches to estimate a

transition matrix, namely the Cohort Method and the

continuous/discrete method. The continuous method was

identified based on time homogenous and time non

homogenous assumptions. In this study, the transition

matrix is estimated using the Cohort Method and

Continuous Method assuming time homogeneity.

In constructing a transition matrix based on a discrete

timescale, the Cohort method was used derived from Jafry

and Schuerman (2004). Meanwhile, the transition matrix

based on a continuous timescale approach was adapted

from the study of Lando and Skodeberg (2002). In this

paper, we only present Continuous Method.

The Corporate Rating Transition Matrice by

Using The Continuous Method Assuming Time

Homogeneity

Estimations were made using a continuous

approach on an annual, bi-annual (2004-2005), three

yearly (2003-2005), four yearly (2002-2005) and five

yearly (2001-2005) timeframe. The most salient matrices

are presented here.

The Five-year Transition Matrix (2001-2005):

During 2001-2005, the total number of transitions

based on the continuous method assuming time

homogeneity was 38 with two not-rated transitions. The

probability distribution of the five-year default transition

matrix was similar to the four-year pattern. Moreover, the

Graph A2.3Letter Rating Activity and Rating Drift

of a Sample Companies

-60

-40

-20

0

20

40

60

80

100

120

2001 2002 2003 2004 2005

-55.0

-10.2

12.54.3

-1.0

110.0

55.1

26.8

10.8 15.0

Rating DriftRating Activity


%

Graph A2.4Letter Rating Activity and Rating Drift of Sample Bonds

-60

-40

-20

0

20

40

60

80

2001 2002 2003 2004 2005

-42.3

-18.4

21.0

4.2 6.1

65.4

36.7 36.4

10.8 8.7

Rating Activity Rating Drift


%

84


Graph A2.5Corporate Rating Stability for The Investment Grade Group

Based on The Time Homogeneous Continuous Method

50

60

70

80

90

100

2001 2002 2003 2004 2005

AAA

AA BBB

A

%

In terms of rating stability, the five-year and four-

year transition matrices show that the investment grade

category maintains fairly high stability. Meanwhile, the

speculative rating category also displayed relatively high

stability for companies rated B and C for the four-year

transition matrix and rated B for the five-year transition

matrix.

Corporate Rating Stability based on the Continu-

ous Method Assuming Time Homogeneity

The distribution of rating stability for investment

grade companies is illustrated in Graph A2.5, whereas the

non investment and speculative grade categories are

illustrated in Graph A2.6. From Graph A2.7, it can be seen

that the investment grade generally maintains a stability

level above 65%.

distribution of transitional probability in 2001-2005 was

wider spread.

In terms of a symmetrical relationship between rating

stability and rating quality, the estimation results for 2001-

2005 illustrate a similar relationship for the transition matrix

of two, three and four years. The rating stability level

declined in line with a drop in rating, reaching BB.

Furthermore, rating B has greater stability than BB.

Transitional probability generally declined in line with

the wider gap in transitional distance, although several

ratings displayed a fairly high probability of migration.

After five years, the possibility of transition emerged

from speculative grade to the investment grade and vice

versa. However, the transition direction of upgraded ratings

surpassed the downgraded ratings. This implies that the

sample companies, over the long term, improved in terms

of creditworthiness, although several companies also

experienced a decline in credit quality.

Over the five years measured, companies also faced

the probability of default or being downgraded to rating

D. Even companies rated AA and A faced the possibility of

default. The safest companies are the ones rated AAA.

This is similar to the results of the four-year transition

matrix. The probability of default increases with a decline

in rating quality, except for BBB and B.

AAAAAAAAAAAAAAA 1 100 0 0 0 0 0 0 0 0AAAAAAAAAA 5 0 94.31 5.42 0.06 0.04 0 0.05 0.06 0.02AAAAA 20 0 4.51 86.72 2.02 1.31 0.23 1.37 2.19 0.70

BBBBBBBBBBBBBBB 7 0 0.38 14.61 82.87 0.13 0.24 0.13 0.17 1.39BBBBBBBBBB 0 0 0.10 5.50 27.41 41.40 1.40 13.48 10.32 0.28BBBBB 2 0 0.03 1.55 7.70 4.94 78.33 5.18 1.29 0.07

CCCCCCCCCCCCCCC 1 0 0.56 19.19 2.59 6.82 8.99 39.50 21.43 0.11DDDDD 3 0 0 0 0 0 0 0 100 0

NRNRNRNRNR 1 0 0.61 21.89 1.29 0.94 20.61 0.97 0.41 52.31TOTALTOTALTOTALTOTALTOTAL 40 Ω Ω Ω Ω Ω Ω Ω Ω Ω

Table A2.1Corporate Rating Transition Matrix Based on The Continuous Approach (%), 2001-2005.

Number of Companies at Beginning AAA AA A BBB BB B CCC D NR

of Period

85


Rating A experienced an escalating stability trend

from year to year. Meanwhile, ratings AA and BBB

experienced significant fluctuations.

Sample companies rated AAA maintained high

stability from year to year. This indicated that issuers rated

AAA tend to maintain high stability and are somewhat

resistant to negative market influences. However, it is noted

that the number of observations for this rating was very

limited and, therefore, not fully representative of market

conditions. On the other hand, the most unstable rating

among the investment grade is BBB with the smallest

stability percentage.

Graph A2.7 illustrates the rating stability of the

investment grade category for each estimation period. For

the five estimation periods, rating stability remains relatively

high, always above 75%. In general, higher ratings lead

to greater stability. Graph A2.7 also implies that the rating

stability will continue to decline as more periods are added.

Slightly different from previous estimations, the BBB rating

shows fluctuations.

Rating stability of the speculative or non-investment

grade category generally experienced a decline in stability

as the estimation period lengthened (Graph A2.8).

However, fluctuations were also visible, particularly for

rating CCC.

Corporate Rating Transition Matrices by Using

Continuous Method Assuming Time Homogeneity:

The Five-year Transition Matrix (2001-2005)

In the given period, the total number of bond rating

transitions based on the continuous method was 29 with

two not rated. The estimation results for 2001-2005 are

presented in Table A2.2.

The stability of bond ratings during 2001-2005 was

sufficiently high, at around 88-100%, except for the CCC

rating with only 50.58%. It is due to its junk bond or

speculative grade status, implying a low quality bond with

a relatively high default probability. Since investment grade

bonds are stable, such bonds are not speculative but for

investment. On the other hand, speculative grade bonds

with high rating fluctuations are often used by speculators

to generate high returns.

Graph A2.6Corporate Rating Stability for The Speculative Grade Group

Based on The Time Homogeneous Continuous Method

2001 2002 2003 2004 2005

BB

B

CCC

0

10

20

30

40

50

60

70

80

90

100%

Figure A2.7Corporate Rating Stability For The Investment Grade Group

Based on The Time Homogeneous Continuous MethodUsing Various Estimation Period

75

80

85

90

95

100

AAA AA A BBB

(2001-2005)

(2002-2005)

(2003-2005)

(2004-2005)

2005

%

Figure A2.8Corporate Rating Stability for The Speculative Grade Group

Based on The Time Homogeneous Continuous MethodUsing Various Estimation Period

0

20

40

60

80

100

120

BB B CCC

(2003-2005)2005 (2004-2005) (2002-2005) (2001-2005)

%

86


Figure A2.9Stability of Investment Grade Bond RatingBased on The Continuous Method Using

Time Homogeneous Assumption

50

70

90

110

2001 2002 2003 2004 2005

AAA

AA

A

BBB

%

around 70-100%. Furthermore, from Graph A2.10 one

can determine that speculative grade bond stability is

around 20-100%. The graph showing investment grade

bonds was flatter compared to the speculative grade.

Among investment grade bonds, AAA rated are the most

stable, followed by AA, BBB and A. The highest quality

rating is AAA, which also represents the most stable. The

stability of BBB outperforms A, which is illustrated by the

flatter line compared to line A. However, the stability trend

of A from 2001 to 2005 increases. This is contrasted against

the BBB rating, which regresses.

The stability of bond ratings from 2001-2005

fluctuated wildly, as shown by increasing and decreasing

shifts on the graphs. In terms of the speculative grade,

Table A2.2 illustrates that a CCC rating has a

transition probability to upgrade to a B rating of 22.48%,

to a BB rating of 23%, a BBB rating of 1.72% and an A

rating of 0.03%. However, the CCC rating has a default

probability of 1.32%.

The transition matrix for 2001-2005 did not return

a symmetrical distribution. The farther from the diagonal,

the magnitude of rating transition varied and the

probability did not always decline. Even from the stability

side (diagonal side), there was no consistent distribution.

Lower bond quality leads to less stability.

Regarding the five-year transition matrix, only A- and

BB-rated bonds (investment grade category) displayed a

small transitional probability towards the speculative grade.

In addition, all speculative grade bonds (BB, B and CCC),

show a positive transitional probability to become

investment grade.

Bond Rating Stability using the Continuous

Homogenous Method

The stability of bond ratings from 2001-2005 can

be analyzed separately between investment grade and

speculative grade respectively. The stability of investment

grade bonds is higher than speculative grade bonds. Graph

A2.9 illustrates that investment grade bond stability is

AAAAAAAAAAAAAAA 0 100 0 0 0 0 0 0 0 0AAAAAAAAAA 1 0 100 0 0 0 0 0 0 0AAAAA 27 0 7 .14 86 .76 0 .99 0 0 .01 0 5 .10 0

BBBBBBBBBBBBBBB 11 0 0 .18 4 .54 93 .58 0 .03 1 .47 0 .04 0 .16 0BBBBBBBBBB 2 0 0 0 .14 5 .82 87 .96 0 .05 0 6 .03 0BBBBB 8 0 0 0 .15 6 .13 3 .77 82 .29 4 .43 3 .23 0

CCCCCCCCCCCCCCC 3 0 0 0 .03 1 .72 23 .87 22 .48 50 .58 1 .32 0DDDDD 0 0 0 0 0 0 0 0 100 0

NRNRNRNRNR 0 0 0 .04 1 .22 39 .66 6 .85 6 .95 26 .98 0 .30 18 .01TotalTotalTotalTotalTotal 52 Ω Ω Ω Ω Ω Ω Ω Ω Ω

Table A2.2Transition Matrix of Bond Ratings Based on The Continuous Method (%), (2001-2005)

Number of Bondsat Beginning AAA AA A BBB BB B CCC D NR

of Period

87


the BB rating is the most stable followed by B and CCC

ratings. From Graph A2.10, it can be concluded that the

lower the bond rating quality, the lower the stability level

will be.

CONCLUSION OF ESTIMATION RESULTS AND

POLICY IMPLICATIONS

Rating Activity and Rating Drift

1. The sample of bond issuers improved their

creditworthiness over time. This was evidenced by a

decline in the percentage of downgraded companies

and bonds as well as a rise in upgrades.

2. The percentage of rating activity of the sample of

companies and bonds during 2001-2005 decreased

relatively. However, the current trend of rating activity

is improving, which is reflected by an increase in rating

drift. This implies that the creditworthiness of the

sample of companies and bonds has improved over

the past few years.

Estimation Results of the Rating Transition

Matrix

1. Estimations using the continuous method provide

more efficient results than the Cohort Method.

Furthermore, the method also facilitates indirect

estimations of a rating in a sequential way.

Additionally, the method facilitates the construction

of transition matrices which can accommodate the

dynamic factors of rating activity throughout the

period, not only at the beginning or the end.

2. The Cohort Method produced a transition matrix with

an uneven probability distribution concentrated

around the diagonal. Meanwhile, estimations using

the continuous method are best for corporate or bond

ratings, producing transition matrices with a more

spread probability distribution. This spread facilitates

the probability of distant migration far from the

diagonal (extreme transition), even to default without

direct transition to that rating, and is possible through

indirect transition through other ratings. The type of

probability distribution shown is primarily illustrated

by the estimation results for a longer-than-one-year

period.

3. Estimations using the Cohort Method failed to show

the relationship between stability and rating; indicated

by the rating stability level not declining in line with

the drop in the rating level. This mainly occurred for

estimation results using a one-year period.

Meanwhile, several estimation results for periods of

longer than one year indicated a symmetrical

relationship between rating stability and rating level,

but only when investment grade ratings were used.

4. Most estimations, for various time periods, indicated

consistent results; that there is a symmetrical

relationship between rating stability and rating level.

This distribution was mainly found at the investment

grade rating. The stability level of the rating varies,

but in general was above 65%.

5. Ratings in the speculative grade fluctuated and did

not show a consistent distribution due to a limited

number of samples, both corporate and bonds. Thus,

a one-sample transition in the speculative grade

Graph A2.10Stability of Speculative Grade Bond RatingBased on The Continuous Method Using

Time Homogeneous Assumption

0

20

40

60

80

100

120

2001 2002 2003 2004 2005

BBBCCC

%

88


category had a significant impact on the migration

probability distribution.

6. In terms of the rating migration trend, estimation

results using Cohort and continuous methods

provided relatively consistent results. Rating migration

tends to upgrade, which is consistent with the analysis

conducted on rating activity and rating drift.

7. It can be concluded that using the continuous

method, assuming time homogeneity, produced a

transition matrix, which is more efficient. The matrix

indicated the possibility of rating migration where

historically it had rarely occurred. For example, to

experience default through an indirect default

mechanism. In addition, the estimation results for

both the Cohort method and the continuous method

indicated that the sample of companies and bonds

improved in creditworthiness over time. This was

expressed by the rating migration trend, which leaned

towards higher ratings.

However, the major constraints of this study were

the limited number of periods and samples. This is also

true for rating activity variation, which is shown by the

limited number of rating transitions. Such a brief sample

period prevented any long term transition matrix

estimations and, unfortunately, the timescale did not date

back far enough to the Indonesian recession post Asian

crisis. Consequently, the limited number of samples

caused a one-rating transition to have a substantial impact

on the probability distribution. This mainly affected

samples in the speculative grade category. This prevented

any creditworthiness analysis of bond issuers in this

category.

89


Ahmed, Sarfaraz dkk. 2004. ≈Use of Transition Matrices

in Risk Management and ValuationΔ. A Fair Isaac

White Paper

Aldridge, Irene E. 2006. ≈Risk-Rating Private Loans:

Strategic Defaults.Δ

Altman, E. I. 1968. ≈Financial Ratios, Discriminant Analysis

and the Prediction of Corporate Bankruptcy.Δ Journal

of Finance, Vol. 23, pp. 589-609.

Altman, E. I., dkk. 1977. ΔZETA analysis: A New Model to

Identity Bankruptcy Risk of Corporations.Δ Journal of

Banking and Finance 10:29-54

Altman, E. I., dan Duen Li Kao. 1991. ≈Examining and

Modeling Corporate Bond Rating Drift,Δ Working

Paper Series, New York University Salomon Center.

Altman, E., dan Kao, D.1992. ΔThe Implications Of

Corporate Bond Rating Drift.Δ Financial Analysts

Journal, 64√75.

Anderson, Ronald dan Suresh Sundaresan. 2000. ≈A

Comparative Study of Structural Models of Corporate

Bond Yields: An Explanatory Investigation,Δ Journal

of Banking & Finance 24 P. 255-269

Arora, N., dkk. 2005. ≈Reduced Form Vs. Structural Models

Of Credit Risk: A Case Study Of Three ModelsΔ Journal

Of Investment Management, Vol. 3, No. 4, pp. 43√

67

Bank for International Settlements, (1999), ≈Estimating

And Interpreting Probability Density Functions,Δ

Proceedings of the workshop held at the BIS on 14

June 1999

Basel Committee on Banking Supervision (1999): Credit

risk modelling: current practices and applications.

Basel Committee on Banking Supervision, (2000), ≈Best

Practices for Credit Risk DisclosureΔ

Basel Committee on Banking Supervision, (2000), ≈Industry

Views on Credit Risk MitigationΔ

Basel Committee on Banking Supervision, (2001) ≈Working

Paper on the Internal Ratings-Based Approach to

Specialised Lending ExposuresΔ

References

Cantor, Richard dan Frank Packer, ≈The Credit Rating

Industry,Δ Federal Reserve Bank of New York Quarterly

Review 19 (Summer-Fall 1994), 1-26.

Carty, L. dan D. Lieberman (1996) Corporate bond defaults

and default rates 1938-1999, Moody»s Investors

Service Global Credit Research, New York, NY.

Chava S., Jarrow R.A., Bankruptcy Prediction With Industry

Effects, Market Versus Accounting Variables, And

Reduced Form Credit Risk Models, SSRN, October 20,

2001.

Chava, Sudheer dan Robert A. Jarrow. 2004. Bankruptcy

Prediction with Industry Effects. Review of Finance 8:

537-569

Crouhy, Michel , Dan Galai, Robert Mark, (2000), A

Comparative Analysis of Current Credit Risk Models,

Journal of Banking and Finance 24 (2000) 59-117

Crouhy, M., D. Galai & R. Mark (2001) ≈Prototype risk

rating system.Δ Journal of Banking & Finance 25, pp.

47-95.

D. Duffie, L. Pedersen dan K. Singleton (2000). ΔModeling

Sovereing Yield Spreads: A Case of Study of Russian

DebtΔ. Working paper, Graduate School of Business,

Stanford University.

Duffie, D. dan K. Singleton, 1999, ≈Modeling Term

Structures of Defaultable Bonds,Δ Review of Financial

Studies, 12 (4), 197-226.

Duffie, D. dan D. Lando (2000) ≈Term Structure of Credit

Spreads with Incomplete Accounting InformationΔ

Duffie, D., Pedersen, L. dan Singleton, K. (2000).

≈Modeling Sovereign Yield Spreads: A Case Study of

Russian Debt.Δ Working paper, Stanford University.

Fender, Ingo dan John Kiff (2004) ≈CDO rating

methodology: Some thoughts on model risk and its

implications. BIS Working Papers No 163

Fernandes, Joao Eduardo (2005), ≈Corporate Credit Risk

Modeling: Quantitative Rating System And Probability

Of Default EstimationΔ

90


Frey, Rudiger dan Jochen Backhaus, (2004) ≈Portfolio

Credit Risk Models with Interacting Default Intensities:

a Markovian ApproachΔ

Gagliardini, P. dan C. Gouriéroux, 2005, ≈Stochastic

Migration Models with Apllication to Corporate,Δ

Journal of Financial Econometrics.

Gordy, Michael B., (1998), ≈From CreditMetrics to

CreditRisk+ and Back AgainΔ

Hadad, M. D., W. Santoso; D. S. Besar, dan Ita Rulina,

(2004), ≈Probabilitas Kegagalan Korporasi Dengan

Menggunakan Model MertonΔ Bank Indonesia

Heitfield, Erik (2004), ≈Rating System Dynamics and Bank-

Reported Default Probabilities under the New Basel

Capital AccordΔ

Hillegeist, Stephen A., Elizabeth Keating, Donald P. Cram

dan Kyle G. Lunstedt, 2004, Assessing the probability

of bankruptcy, Review of Accounting Studies 9, 5√

34.

Hurd T.R., dan A. Kuznetsov, 2005, ≈Affine Markov Chain

Model of Multifirm Credit Migration,Δ Natural

Sciences and Engineering Research Council of Canada

dan Mathematics of Information Technology and

Complex System in Canada.

Jarrow, Robert A & Lando, David & Turnbull, Stuart M,

1997. ≈A Markov Model for the Term Structure of

Credit Risk Spreads,Δ Review of Financial Studies,

Oxford University Press for Society for Financial

Studies, vol. 10(2), pages 481-523.

Kamakura Risk Information Services, 2004, Kamakura

pabulic Firm Models, Version 3.0.

Kaplan, R. dan G. Urwitz (1979), «»Statistical Models of

Bond ratings: A Methodological Inquiry»», Journal of

Business, April: 231-262

Kijima, M., dan K. Komoribayashi, 1998, ≈A Markov Chain

Model for Valuing Credit Risk DerivativesΔ, The

Journal of Derivatives, Fall, 97-108.

Lando, D. (1998) ≈On Cox Processes and Credit Risky

SecuritiesΔ Review of Derivatives Research, Vol. 2 (2/

3), pp. 99-120

Lando, David dan Torben M. Skμdeberg. 2002. Analyzing

rating transitions and rating drift with continuous

observations. Journal of Banking and Finance 26. 423-

444. www. Elsevier. Com

McNulty, Cynthia dan Ron Levin, 2000, ≈Modeling Credit

Migration,Δ JP Morgan Securities

Morokoff, William J., 2005, ≈Simulation Analysis of

Correlation and Credit Migration Models for Credit

Portfolios,Δ Proceedings of the 2005 Winter

Simulation Conference.

Nickell, P., Perraudin, W., dan S. Variotto, 2000, ΔStability

of Rating TransitionsΔ, Journal of Banking and

Finance, 24, 203-227.

Nickell, Pamela et al. 2001. Stability of ratings transitions

Nickell, Pamela et. al. 2001. Rating versus equity based

credit risk modelling: an empirical analysis

Nickell, Pamela et.al. 2005. Rating-Based credit Risk

Modelling : an Empirical analysis

Pefindo,2001. Indonesian Rating Highlights. Annual Report

Rommer, Anne Dyrberg, 2005, ≈Testing the Assumptions

of Credit-Scoring Models,Δ Danmarks Nationalbank

dan Centre for Applied Microeconometrics, Institute

of Economics, University of Copenhagen.

Sabourin, Patrick. 1999. Analyzing and Forecasting Credit

Ratings: Some Canadian Evidence

Schuermann, Til dan Yusuf Jafry. (2003). Measurement and

Estimation of Credit Migration Matrices. Wharton

Financial Institutions Centre.

van Deventer, D. dan K. Imai, (2003). ≈Credit Risk Models

and the Basel Accords: The Merton Model and

Reduced Form Models, John Wiley & Sons

van Deventer, Donald R. and Xiaoming Wang, (2003).

≈Advanced Credit Model Performance Testing to

Meet Basel Requirements,Δ Forthcoming in The Basel

Handbook √ A Guide for Financial Practitioners from

RISK Publications

Van Landschoot, Astrid (2003), ≈The Term Structure of

Credit Spreads on Euro Corporate BondsΔ

Van Landschoot, Astrid. 2005. Transition Probabilities

andAsset Correlation:Structured Products versus

Corporates. The McGrawhill Companies.

Violi, Roberto. 2004. Credit Ratings Transition in Structured

Finance. CGFS Working Group on Ratings in

Structured Finance.

91

Glossary

Glossary

92

Glossary


93

Glossary

Cost of loanable funds:Cost of loanable funds:Cost of loanable funds:Cost of loanable funds:Cost of loanable funds: includes interest on funds,

overhead costs, the deposit insurance premium and

minimum reserve requirement.

Bank Indonesia Real Time Gross Settlement (BI-RTGS):Bank Indonesia Real Time Gross Settlement (BI-RTGS):Bank Indonesia Real Time Gross Settlement (BI-RTGS):Bank Indonesia Real Time Gross Settlement (BI-RTGS):Bank Indonesia Real Time Gross Settlement (BI-RTGS):

Electronic transaction settlement in real time where

accounts are debited and credited multiple times per day.

Business continuity management:Business continuity management:Business continuity management:Business continuity management:Business continuity management: Risk management to

ensure critical functions during disruptions as well as having

an effective recovery process.

Downside Risk:Downside Risk:Downside Risk:Downside Risk:Downside Risk: The likelihood that a security or other

investment will decline in price, or the amount of loss that

could result from that potential decline.

Failure to settle: Failure to settle: Failure to settle: Failure to settle: Failure to settle: a mechanism which obliges participants

of the clearing system to provide a pre-fund to anticipate

liabilities emerging at the end of the day.

Discount Window: Discount Window: Discount Window: Discount Window: Discount Window: credit extended to banks by the central

bank to overcome liquidity problems caused by a temporary

mismatch in liquidity management.

Financial Deepening: Financial Deepening: Financial Deepening: Financial Deepening: Financial Deepening: the development of the financial

sector; the increased provision of financial services with a

wider choice of services geared to all levels of society.

Financial Sector Assessment Program: Financial Sector Assessment Program: Financial Sector Assessment Program: Financial Sector Assessment Program: Financial Sector Assessment Program: a joint program by

the IMF and World Bank to assess the resilience of a

country»s financial system and its adherence to international

standards.

Flight to safety: Flight to safety: Flight to safety: Flight to safety: Flight to safety: switching funds from banks considered

less safe to safer banks.

Four-eyes principle: Four-eyes principle: Four-eyes principle: Four-eyes principle: Four-eyes principle: credit approval considering business

prospects and risk management.

Financial Safety Net: Financial Safety Net: Financial Safety Net: Financial Safety Net: Financial Safety Net: framework to strengthen financial

system stability through four key elements: i) bank

Glossary

regulation and supervision; ii) lender of last resort; iii)

deposit insurance; and iv) crisis management.

Capital Adequacy Ratio (CAR): Capital Adequacy Ratio (CAR): Capital Adequacy Ratio (CAR): Capital Adequacy Ratio (CAR): Capital Adequacy Ratio (CAR): The ratio of a bank»s total

regulatory capital to its risk-weighted assets.

Non-performing loans (NPL): Non-performing loans (NPL): Non-performing loans (NPL): Non-performing loans (NPL): Non-performing loans (NPL): a loan that is in default or

close to being in default categorised as sub-standard (SS),

doubtful (D) and loss (L)

Lender of last resort: Lender of last resort: Lender of last resort: Lender of last resort: Lender of last resort: the function of a central bank in

extending credit to banks to overcome liquidity problems

caused by a mismatch in funds and to prevent systemic

crisis.

Crisis management: Crisis management: Crisis management: Crisis management: Crisis management: a comprehensive framework to

identify, mitigate and resolve crises.

Mark to market: Mark to market: Mark to market: Mark to market: Mark to market: Evaluating the price or value of a security,

portfolio, or account on a daily basis, to calculate profits

and losses or to confirm that margin requirements are being

met.

Risk mitigation: Risk mitigation: Risk mitigation: Risk mitigation: Risk mitigation: efforts to reduce the possibility and effects

of risk.

Economic capital:Economic capital:Economic capital:Economic capital:Economic capital: is the amount of real capital required

to cover accumulative excess or unexpected losses over a

fixed time period with a set confidence level.

Moral Hazard: Moral Hazard: Moral Hazard: Moral Hazard: Moral Hazard: behaviour of business players (bank owners,

managers and customers) that triggers financial losses for

the bank.

Crisis prevention: Crisis prevention: Crisis prevention: Crisis prevention: Crisis prevention: efforts to prevent crises through policies

for micro prudential regulation and supervision of financial

institutions and financial markets as well as macro

prudential surveillance of the financial system.

Crisis resolution: Crisis resolution: Crisis resolution: Crisis resolution: Crisis resolution: efforts to overcome crises including

restructuring and recapitalising banks with systemic effects.

94

Glossary

Profit taking: Profit taking: Profit taking: Profit taking: Profit taking: the selling of assets or securities by investors

at a high price to receive profit.

Regulatory capital: Regulatory capital: Regulatory capital: Regulatory capital: Regulatory capital: the minimum capital required applied

to banks set by the regulator.

Restructuring: Restructuring: Restructuring: Restructuring: Restructuring: the act of improving loan conditions by

applying several options: i) adjusting the covenants to

provide additional financing; ii) converting all or partial

interest as new loans; iii) converting all or part of the loan

as equity for the bank in the company with or without

rescheduling or reconditioning.

Credit risk: Credit risk: Credit risk: Credit risk: Credit risk: the risk of loss due to a debtor»s possibility of

default, or non-payment of a loan.

Liquidity Risk:Liquidity Risk:Liquidity Risk:Liquidity Risk:Liquidity Risk: risk that an institution will not be able to

execute a transaction at the prevailing market price because

there is, temporarily, no appetite for the deal on the other

side of the market.

Operational risk: Operational risk: Operational risk: Operational risk: Operational risk: the risk of loss resulting from inadequate

or failed internal processes, people and systems, or from

external events.

Market risk: Market risk: Market risk: Market risk: Market risk: the risk that the value of an investment will

decrease due to the movements in market factors.

Systemic risk:Systemic risk:Systemic risk:Systemic risk:Systemic risk: describes the likelihood of the collapse of a

financial system, such as a general stock market crash or a

joint breakdown of the banking system.

Risk-free assets: Risk-free assets: Risk-free assets: Risk-free assets: Risk-free assets: an asset whose future return is known

with certainty. However, such assets remain subject to

inflation risk.

Systemically Important Payment Systems: Systemically Important Payment Systems: Systemically Important Payment Systems: Systemically Important Payment Systems: Systemically Important Payment Systems: are those that,

in terms of the size or nature of the payments processed

via them, represent a channel in which shocks could

threaten the stability of the entire financial system.

Risk-control system: Risk-control system: Risk-control system: Risk-control system: Risk-control system: is a system to control risk implemented

through bank policy and procedure in line with sound risk

management principles.

Credit scoring systems: Credit scoring systems: Credit scoring systems: Credit scoring systems: Credit scoring systems: provide a consistent, mathematical

system to evaluate potential debtors. A credit scorecredit scorecredit scorecredit scorecredit score is a

numerical expression based on a statistical analysis of a

potential debtor»s credit files, to assess the creditworthiness

of that debtor, which is the likelihood the debtor will pay

his or her debts.

Financial system stability: Financial system stability: Financial system stability: Financial system stability: Financial system stability: refers to a state in which a

financial system, consisting of financial institutions and

markets, functions properly. In addition, the participants,

such as firms and individuals, have confidence in the

system.Ω

Stress testing: Stress testing: Stress testing: Stress testing: Stress testing: is a simulation technique used on asset

and liability portfolios to determine their sensitivities to

different financial situations. Stress-testing is a useful

method of determining how a portfolio will fare during a

period of financial crisis.

Undisbursed Loans: Undisbursed Loans: Undisbursed Loans: Undisbursed Loans: Undisbursed Loans: are loans that have been agreed but

are yet to be withdrawn.

Unexpected losses: Unexpected losses: Unexpected losses: Unexpected losses: Unexpected losses: are defined as the difference between

expected loss and worst case loss. Expected losses are

≈smallΔ losses, unexpected losses are ≈low probability high

impactΔ losses and worst case losses are losses of such

magnitude that they would render most institutions

bankrupt.

Volatility: Volatility: Volatility: Volatility: Volatility: is the relative rate at which the price of a security

moves up and down. Volatility is found by calculating the

annualized standard deviation of daily change in price. If

the prices of securities move up and down rapidly over

short time periods, it has high volatility. If the price almost

never changes, it has low volatility.

Yield:Yield:Yield:Yield:Yield: The rate of income generated from a stock in the

form of dividends, or the effective rate of interest paid on

a bond, calculated by the coupon rate divided by the bond»s

market price. Furthermore, for any investment, yield is the

annual rate of return expressed as a percentage.

DIRECTOR

Halim Alamsyah Wimboh Santoso

COORDINATOR & EDITOR

Agusman

WRITER

Sukarela Batunanggar, Linda Maulidina, Herawanto, Ronald L. Toruan, Dwityapoetra S. Besar,

Pipih Dewi Purusitawati, Wini Purwanti, Endang Kurnia Saputra, Ferial Ahmad, Ita Rulina,

Ricky Satria, Fernando R. B, Noviati, Sagita Rachmanira, Reska Prasetya, Leanita Indah P.,

Elis Deriantino, Hero Wonida, Mestika Widantri, Heny S

COMPILATOR, LAYOUT & PRODUCTION

Ita Rulina Ricky Satria Primitiva Febriarti

CONTRIBUTOR

Directorate of Bank Supervision 1



Directorate of Rural Bank Supervision

Directorate of Sharia Banking

Directorate of Banking Investigation and Mediation

Directorate of Bank Licensing and Banking Information

Directorate of Accounting and Payment System

Credit Bureau

Directorate of Economic Research and Monetary Policy

Directorate of Monetary Management

Directorate of Reserve Management

DATA SUPPORT

Suharso I Made Yogi Tita Hapsari

Financial Stability ReviewNo. 9, September 2007

bank indonesia, financial stability review no.9 september 2007

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