the determinants of bank capital ratio in east asia
TRANSCRIPT
The Determinants of Bank Capital Ratio in East Asia
Thiam Chiann Wen
Bachelor of Science (Financial Mathematics) University Malaysia Terengganu
2007
Submitted to the Graduates School of Business Faculty of Business and Accountancy
University of Malaya, in partial fulfillment of the requirement for the Degree of Master of Business Administration
July 2009
ii
ABSTRACT
This study use balance panel data in analysing the determinants of bank capital ratio
for seven countries in East Asia. The results are consistent with the previous literature.
There is a strong positive relationship between bank capital and bank risk taking
behaviour. Besides, the result shows capital requirement pressure does not have an
influence of low capitalised banks. Liquidity, leverage and profitability show positive
link with the bank capital which support most of the bank literature. Finally, the
country macro variables seemly do not influence the target capital level. Specification
of banks type and ownership structure may be included in the future studies in bank
capital ratio in East Asia region.
iii
ACKNOWLEDGEMENTS
It was my great privilege to have Dr. Rubi Ahmad as my supervisor. I would like to
express my utmost gratitute to Dr. Rubi for her thoughtful suggestions and guidance.
Without her insight, knowledge and assistance, this paper could not have been
completed. I cannot express the level of gratitude that I feel for the patience and
kindness displayed by her
I would like to thank my family and friends for their support and encouragement
throughout my coursework. Of special mention here are my parents; their love and
sacrifices are indescribable.
iv
TABLE OF CONTENTS
ABSTRACT............................................................................................................ II ACKNOWLEDGEMENTS...................................................................................III TABLE OF CONTENTS....................................................................................... IV
LIST OF TABLES .................................................................................................VI CHAPTER 1: INTRODUCTION ........................................................................... 1
1.0 STATEMENT OF THE PROBLEM .................................................................. 1 1.1 CAPITAL ADEQUACY FRAMEWORK ......................................................... 3 1.3 OBJECTIVES OF THE STUDY..................................................................... 12 1.4 SCOPE OF THE STUDY................................................................................ 13 1.5 ORGANISATION OF THE STUDY............................................................... 14
CHAPTER 2: LITERATURE REVIEW.............................................................. 15 2.0 EARLY RESEARCH...................................................................................... 15 2.1 DEFINATION AND ROLE OF BANK CAPITAL .......................................... 16 2.2 BANK CAPITAL MANAGEMENT............................................................... 17 2.2 BANK CAPITAL AND BANK BEHAVIOURS.............................................. 18 2.4 RESEARCH ON DETERMINANTS OF BANK CAPITAL............................ 20
2.4.1 THE PRESENCE OF GOVERNMENT GUARANTEES ............................ 20 2.4.2 THE EFFECTS OF CAPITAL REGULATIONS ........................................ 21 2.4.3 SHAREHOLDERS AND MANAGERS’ RISK AVERSION.......................... 23 2.4.4 BANK EARNINGS OR CHARTER VALUE............................................... 24
2.5 EMPIRICAL FINDINGS ON ASIAN BANKS ............................................... 25 2.6 CHAPTER SUMMARY................................................................................. 27
CHAPTER 3: DATA AND RESEARCH METHODOLOGY.............................. 28 3.0 RESEARCH HYPOTHESES.......................................................................... 28 3.1 RESEARCH METHODOLOGY .................................................................... 30 3.2 DEPENDENT VARIABLE – CAPITAL ADEQUACY RATIO (CAR) ............ 31 3.3 VARIABLES AFFECTING TARGET CAPITAL ............................................ 32 3.4 METHOD SELECTION................................................................................. 36 3.5 SAMPLING AND DATA COLLECTION ....................................................... 37 3.6 STASTICAL ESTIMATION AND INFERENCE ............................................ 38 3.7 CHAPTER SUMMARY................................................................................. 39
CHAPTER 4: RESEARCH RESULTS ................................................................ 40 4.0 DESCRIPTIVE STATISTICS ......................................................................... 40 4.1 TEST OF MULTICOLLINEARITY............................................................... 41 4.2 ANALYSIS OF VARIANCE........................................................................... 43 4.3 FINDINGS AND RESULTS ........................................................................... 44 4.4 SUMMARY AND DISCUSSION OF THE FINDINGS................................... 53 4.5 ROBUSTNESS CHECK................................................................................. 56
4.5.1 ROBUSTNESS CHECK – JAPANESE BANK EXCLUDED....................... 56 4.5.2 ROBUSTNESS CHECK – ANALYSIS BASED ON BANK SIZE................. 60 4.5.3 ROBUSTNESS CHECK – ANALYSIS BASED ON COUNTRY .................. 64
4.6 CHAPTER SUMMARY................................................................................. 67
v
CHAPTER 5: CONCLUSION AND RECOMMENDATION ............................. 69 5.0 OVERVIEW OF THE STUDY ....................................................................... 69 5.1 INTERPRETATION OF MAJOR FINIDNGS................................................. 70 5.2 LIMITATIONS OF THE STUDY ................................................................... 71 5.4 RECOMMENDATIONS FOR FUTURE RESEARCH ................................... 71 5.5 CHAPTER SUMMARY................................................................................. 72
BIBLIOGRAPHY.................................................................................................. 73 APPENDICES ....................................................................................................... 76
A) OLS RESULTS IN EVIEWS................................................................................ 76 B) BANKS INCLUDED IN SAMPLE ........................................................................... 90
vi
LIST OF TABLES
TABLE 1: VARIABLES AND PREDICTED SIGNS .............................................................. 35 TABLE 2: POOLED-SAMPLE DESCRIPTIVE STATISTIC OF THE SELECTED DEPENDENT AND
EXPLANATORY NON-DUMMY VARIABLES ............................................................. 40 TABLE 3: THE PAIRWISE CORRELATION MATRIX FOR DEPENDENT VARIABLES AND
EXPLANATORY NON DUMMY VARIABLES............................................................. 41 TABLE 4: MULTICOLLINEARITY TEST ........................................................................ 43 TABLE 5: TEST FOR EQUALITY OF MEANS BETWEEN SERIES ...................................... 43 TABLE 6: LAGRANGE MULTIPLIER TESTS – GLESJER TEST.......................................... 45 TABLE 7: DETERMINANTS OF CAPITAL RATIO.............................................................. 47 TABLE 8: WALD TEST FOR BANK SPECIFIC VARIABLES................................................. 49 TABLE 9: WALD TEST FOR COUNTRY BINARY VARIABLE .............................................. 50 TABLE 10: WALD TEST ON COUNTRY MACROECONOMIC VARIABLES ............................ 52 TABLE 11: DETERMINANTS OF CAPITAL RATIO (ROBUSTNESS CHECK, WITHOUT
JAPANESE BANKS) ............................................................................................. 58 TABLE 12: DETERMINANTS OF CAPITAL RATIO ACCORDING TO BANK SIZE................... 62 TABLE 13: DETERMINANTS OF CAPITAL RATIO ACCORDING TO COUNTRY..................... 65 TABLE 14: BANK SPECIFIC VARIABLE ONLY - PERIOD FIXED....................................... 76 TABLE 15: BANK SPECIFIC VARIABLE ONLY - PERIOD FIXED AND COUNTRY FIXED ..... 76 TABLE 16: BANK SPECIFIC VARIABLE ONLY - FIRM FIXED AND PERIOD FIXED............. 77 TABLE 17: BANK SPECIFIC AND COUNTRY MACRO VARIABLES - PERIOD FIXED ......... 78 TABLE 18: BANK SPECIFIC AND COUNTRY MACRO VARIABLES - COUNTRY FIXED AND
PERIOD FIXED.................................................................................................... 78 TABLE 19: BANK SPECIFIC AND COUNTRY MACRO VARIABLES - FIRM FIXED AND
PERIOD FIXED.................................................................................................... 79 TABLE 20: ROBUSTNESS CHECK (I), BANK SPECIFIC VARIABLES ONLY, PERIOD FIXED 80 TABLE 21: ROBUSTNESS CHECK (I), BANK SPECIFIC VARIABLES ONLY, COUNTRY FIXED
AND PERIOD FIXED ............................................................................................ 80 TABLE 22: ROBUSTNESS CHECK (I), BANK SPECIFIC VARIABLES ONLY, FIRM FIXED AND
PERIOD FIXED.................................................................................................... 81 TABLE 23: ROBUSTNESS CHECK (I), BANK SPECIFIC VARIABLES AND COUNTRY MACRO
VARIABLE, PERIOD FIXED .................................................................................. 82 TABLE 24: ROBUSTNESS CHECK (I), BANK SPECIFIC VARIABLES AND COUNTRY MACRO
VARIABLE, COUNTRY FIXED AND PERIOD FIXED ................................................ 82 TABLE 25: ROBUSTNESS CHECK (I), BANK SPECIFIC VARIABLES AND COUNTRY MACRO
VARIABLE, FIRM FIXED AND PERIOD FIXED........................................................ 83 TABLE 26: LARGE BANK ........................................................................................... 84 TABLE 27: MEDIUM BANK ........................................................................................ 85 TABLE 28: SMALL BANK ........................................................................................... 85 TABLE 29: REGRESSION RESULTS, CHINA.................................................................. 86 TABLE 30: REGRESSION RESULTS, JAPAN .................................................................. 86 TABLE 31: REGRESSION RESULTS, KOREA ................................................................. 87 TABLE 32: REGRESSION RESULTS, INDONESIA ........................................................... 88 TABLE 33: REGRESSION RESULTS, MALAYSIA ........................................................... 88 TABLE 34: REGRESSION RESULTS, PHILIPPINES.......................................................... 89 TABLE 35: REGRESSION RESULTS, THAILAND............................................................ 89 TABLE 36: SAMPLE BANKS OF CHINA........................................................................ 90
vii
TABLE 37: SAMPLE BANKS OF JAPAN ........................................................................ 91 TABLE 38: SAMPLE BANKS OF KOREA ....................................................................... 93 TABLE 39SAMPLE BANKS OF INDONESIA ................................................................... 93 TABLE 40: SAMPLE BANKS OF MALAYSIA ................................................................. 94 TABLE 41: SAMPLE BANKS OF PHILIPPINES................................................................ 95 TABLE 42: SAMPLE BANKS OF THAILAND .................................................................. 95
CHAPTER 1: INTRODUCTION
1.0 STATEMENT OF THE PROBLEM
The research problem of this study is centred on the determination of bank capital
level and the possible factors that affect the target capital level. This study is an
empirical exposition of how banking firms in East Asia set their capital ratios and
whether these capital decisions are related to their risk taking behaviours. The
analysis draws on the theoretical model of the multivariate panel regression model as
proposed by (Ahmad. R, Ariff, & Michael, 2008). This study extends the earlier
model to include the neighbouring countries of Malaysia in South East Asia and also
three countries in East Asia in order to investigate whether the setting of capital ratios
are heterogeneous across the region.
Most of the studies which examine the bank capital requirements and regulators
policy with respect to bank risk taking behaviours are using sample in United States
and European countries. We find that verification of the association between bank
capital regulations and managerial capital decisions is seldom researched using Asian
countries data although the effectiveness of regulatory framework and banking system
operations are often stressed as an important policy issue in regulating banks. In line
with similar research conducted on other countries, this paper aims at investigating
whether regulatory constraints do have an impact on banks' behaviours.
In the light of aforesaid discussion, the present paper seek to address the following
issues, first to investigate whether the higher capital requirements introduced by
regulators during the test period, 2004-2007, did produce the required increase in
2
capital ratios in order to reduce risk. The empirical verification of the association
between capital regulations and bank managements’ capital decisions might provides
some about the effectiveness of a regulatory framework of these countries’ banking
system operation. The countries in South East Asia adopted the Basel Committee on
Banking Supervision (BCBS) standard of 8 per cent risk-weighted capital adequacy
ratio (CAR), which the adoption date of the eight Asian countries are as follow,
Malaysia in 1989, Japan in 1989, Singapore in 1992, Indonesia in 1993, Korea in
1993, China in 1994, and Philippines in 2001. In 1996, these countries again followed
the Basel Committee’s recommendation and incorporated market risk into its CAR
calculation except for Philippines in year 2002. These regulations were designed to
create a safe and sound banking system by strengthening capital adequacy. Therefore,
the empirical analysis also explores whether the regulations are able to obtain the
desired response from bank management.
Secondly, this study attempts to investigate the link between bank capital and bank
risk simultaneously compares the results for the four South East Asia countries and
three East Asia countries. Following the framework introduce by (Ahmad et al., 2008),
the Basel 1988 risk-weighted capital adequacy ratio is used as a proxy for bank capital.
To identify the risk level of the bank, we used loan loss reserve ratio as the
measurement. Loan loss reserve ratio indicates the default risk level of the bank
(Shrieves and Dahl,1992).
Guttentag and Herring (1983) define the charter value as "the present value of the net
income the bank would be expected to earn on new business if it were to retain only
its office, employees, and customers. (...) [It] depends on the bank's authorized
3
powers, including power to do business within specified areas, the market structure in
the area, the expertise of the bank's employees, and the customer relationships it has
developed". Over the twenty years prior to the Asian financial crisis in year
1997-1998, banks have enjoyed high earnings. Bank literatures show that bank
earnings is one of the important determinants of bank capital ratios. Also, high profit
and cost efficiency encourage the banks’ management to keep more capital from
earnings to protect against liquidation.
Sauders and Wilson (2001) and Konishi and Yasuda (2003) find that in developed
countries, a high charter value would provide self-regulatory incentives for banks to
raise capital while at the same time also help to minimize their risk taking. Fisher et.
Al (2001) use data on commercial banks in Canada, Mexico and the United States to
test the disciplining role of charter value hypothesis in these three NAFTA countries
and find that no empirical support for the hypothesis in Canada and Mexico, and a
strong empirical evidence of the self-disciplining power of charter value in U.S
commercial banks. On the other hand, Kentaro (2007) discover that capital-risk
relationship is nonlinear and changes from positive to negative as franchise value falls.
Therefore, it is interesting to see how the management of banks in developing
countries for instance South East Asian countries behave.
1.1 CAPITAL ADEQUACY FRAMEWORK
There was no standard definition of capital before 1988. Since central banks used
different approach to measure capital, it was difficult to evaluate and compare the
financial position of banks in different countries. As a result, the concept of capital for
regulatory purposes was standardised in the first Basel Capital Accord (Basel I). Basel
4
I was formulated by the BCBS at the Bank for International Settlements (BIS), an
international organisation formed in 1930 to promote international monetary and
financial co-operation and to serve as a bank for central banks around the world.
The guidelines were intended to 1) establish a systematic analytical framework that
make regulatory capital requirements more responsive to differences in risk profiles
among banking organizations, 2) take off balance sheet exposure into explicit account
in evaluating capital adequacy, and 3) minimize disincentives to hold liquid and low
risk assets.
The Basel rules included a schedule for implementing the new system worldwide,
with a ratio of 8 percent, of which at least 4 per cent must be in the form of tier 1
capital. This framework aims to provide a common standard for safe and prudent
banking capitalization. Next section we will briefly present the adoption and
implementation of the capital adequacy framework in the sample countries.
CHINA
In mid 1980s, the Big Four Banks, Agriculture Bank of China, Bank of China, China
Construction Bank, and Industrial and Commercial Bank of China were established as
fully state-owned enterprise. In China, capital was not clearly defined either as an
accounting concept or an economic concept for long time. The 1990s and the early
2000s, the Chinese banking system has started a reform process based on three main
pillars, among which bank restructuring, financial liberalization and strengthened
financial regulation and supervision. (Garcia-Herrero et al. 2006).
5
Identified the need to require banks to maintain a significant level of capital adequacy,
China began to apply capital adequacy requirement, which is also the first banking
law in China’s history, in year 1994 to commercial banks. Furthermore, the People’s
Bank of China (PBC), the central bank of china, sets the regulation that the risk
weighted capital adequacy ratio (RWCAR) may not be less than 8%, the tier 1 capital
not less than 4% and the supplementary capital may not exceed 100% of the tier 1
capital. Therefore China appears to have accepted of the Basel Capital Accord and
adapted it for the Chinese banking sector (Jin, 2003). In 1996, following the spirit of
Basel Accord I and China’s Commercial Bank law, the PBC published the important
regulation on “Asset Liability Ratios Management of Commercial Banks,
Measurement, Controlling, Monitoring and Evaluation”. Effectively from 1997, PBC
states that, it is mandatory for the commercial banks to have a minimum risk weighted
capital adequacy ratio of 8%.
JAPAN
Japan similarly adopted The Basel Accord which introduced in 1988. The capital
standard became effective in March 1989 and internationally active banks were
required to achieve the benchmark by December 1992. However, for Japanese banks
which operate domestically, the deadline was March 1993, the end of their accounting
year. (Ito & Sasaki, 2002) In detail, under the 1988 capital regulation requirements,
Japanese banks with international activities are obliged to keep capital of 7.25 percent
of risky assets by the time of March 1991, and 8 percent by the time of the deadline
March 1993. They are allowed to include up to 45 per cent of unrealized capital gains
on equity markets into the Tier 2 bank capital as long as the bank has enough Tier 1
capital. (Brana & Lahet, 2009)
6
In June 1996, a law implemented measures in order to strengthen Japan's regulatory
system. Banks had to provide self-assessment on the quality of loans and the capital
adequacy, which was followed by external audit and regular inspection of supervisory
authorities. An independent Agency, the Supervisory Agency for Financial Entities
became in charge of bank supervision, instead of the Japanese Ministry of Finance.
The most important part of the law was the Prompt Corrective Action (PCA). It
required banks to strengthen their risk management, classifying their loan portfolio
rigorously, and allowed the regulators to force banks to take corrective measures, or
ultimately to close. The PCA also demanded that Japanese banks publish their capital
ratios. In early 1997, most of the major Japanese banks still did not meet the BIS ratio.
In 1997, private-sector financial institutions showed greater willingness to reduce
risky assets as capital constraints were intensified (BIS, 1998).
KOREA
In 1981, the Office of Bank Supervision in Korea first introduced capital adequacy
requirements by setting numerical capital-to-deposit guidelines at 10 per cent for all
banks. However, in 1988 the guideline was changed from the capital-to-deposit ratio
to the capital-to-total asset ratio in order to control the increases of financial
leveraging by banks. Corresponding to the ongoing financial liberalization, the
minimum required capital ratio was at 6 per cent for nationwide city banks and 8 per
cent for the regional banks.
The Office of Bank Supervision realised that it was necessary to follow the Basel
Capital Accord guidelines to ensure the capital adequacy of Korean banks as well as
to make sure the Korean Banks are able to compete with international banks in global
7
financial markets. Therefore, in July 1992, the Office introduced the risk-weighted
capital standards suggested by the Basel Committee as an additional measure to
ensure capital adequacy.
Korea implemented the Basle guidelines over a three year period. This result in
Korean commercial banks were required to maintain a capital ratio of at least 7.25 per
cent at the end of 1993, and to meet the full 8 per cent standard by the end of 1995.
The domestic banks find difficult to increase capital due to the slow growth in Korean
stock market after 1990, therefore, this result a longer transitional period. It was also
felt that the banks needed time to prepare and adapt for the new standards.
There is also other legal capital requirement that has been set in addition to the risk
weighted capital adequacy requirement. For example, the minimum paid-in-capital
requirements are 100 billion won (Korean currency) in the case of nationwide
commercial bank, and 25 billion won in the case of regional bank. On the other hand,
in the case of a branch of a foreign bank, the minimum paid-in capital requirements
are set at 3 billion won. Beside the obligatory minimum capital requirements, all
banks in Korea, including foreign bank branches, are obliged to maintain aggregate
amount of equity capital equivalent to at least one twentieth of its outstanding
liabilities arising from guarantees or other contingent liabilities, as a prescribed
solvency position under the provisions of the General Banking Act.
MALAYSIA
In 1989, Bank Negara Malaysia introduced the capital adequacy framework (also
known as the Risk Weighted Capital Adequacy Framework). This framework was
8
developed based on the international standards on capital adequacy introduced by the
Basel Committee on Banking Supervision (BCBS) in 1988 (known as Basel I). The
capital adequacy framework sets out the approach for the computation of minimum
capital required by a banking institution to operate as a going concern entity. The
capital adequacy framework can be divided into three broad categories which consist
of the general capital adequacy requirements, components of eligible regulatory
capital and the Risk-Weighted Assets (RWA). Banking institutions incorporated in
Malaysia are required to maintain a minimum Risk-Weighted Capital Ratio (RWCR)
of 8% at all times at the entity, global and consolidated level based on Basel I.
Malaysia incorporated market risk into its CAR calculation in respond to the
recommendation of Basel Committee in 1996. In 1999, Bank Negara decides to raise
the capital adequacy requirement from 8 per cent to 10 percent. Based on the
guideline of Bank Negara, capital funds for domestic banking groups are calculated
based on the aggregate capital funds of the commercial bank and investment bank in
each group. Nevertheless, these banking groups are still given the flexibility to
determine the relative size of each entity within their groups as long as the aggregate
capital funds of all the entities amounts to at least RM 2 billion effectively start from
the end of 2001.
Bank Negara Malaysia intends to adopt Basel II in full by 2010 using a two-phased
approach. Phase 1 schedule to be implemented in January 2008; all banks are to adopt
the Standardised Approach for credit risk and Basic Indicator Approach for
operational risk. Bank Negara Malaysia may permit banks to remain on Basel I if they
intend to adopt the Internal Rating Based (IRB) Approach instead. Phase 2 will be
implemented by 2010; implementation of the IRB Approach is completed for banks
9
that choose this approach. Banks on the Standardised Approach are not mandated to
migrate to the IRB Approach.
INDONESIA
Banking supervisory in Indonesia was coordinated under Master Dokumen
Pengawasan Bank (MDPB) which includes the Master Plan (MP) and the Detailed
Action Plan (DAP). Under the MP, Bank Indonesia, the central bank of Indonesia acts
as a sole regulator for the banking industry which conducts Special Surveillance (SS)
and On-Site Supervisory Presence (OSP) to the banks. In the period of 1988-1999,
series of bank reform packages as part of financial liberalisation was introduced over
these ten years period. To stabilise the competition among banks due to the financial
liberalisation, capital requirements which represent the main banking supervisory
instrument in Indonesia was initiated as part of Policy Package of October 1988
(PAKTO 1988). The Indonesian approach is fully consistent with the basic standards
laid down in the Basel Accord that banks were required to reach at a minimum of 8
per cent CAR by the end of December 1992, albeit many banks are unable to meet the
requirements. Because of the financial crisis in 1997, Bank Indonesia adopts a
regulatory relief or tolerance of CAR from 8 percent to 4 percent, notionally to
provide a breathing space for the banks and their borrowers. However, the CAR is
restored again to 8 percent in 2001.
Indonesian Banks would get administrative approval or forced into the
recapitalization program if fail to meet the regulatory requirements. Therefore, as in
September 1998, the banking recapitalisation program was conducted by Indonesian
Bank Restructuring Agency (IBRA), under the Ministry of Finance. In classifying
10
which banks to go for the recapitalisation program, IBRA proposed three groups
based on an audit by international accounting firms. Although Indonesian banks
appeared to have a lower capital regulation, the situation post crisis pushed the
regulator to strengthen the capital requirement rules.
THAILAND
The Bank of Thailand (BOT) has steadily enhanced the supervision and examination
framework of financial institutions in line with the standards set by the Basel
Committee on Banking Supervision (e.g. Basel Core Principles for Effective Banking
Supervision). The BOT has implemented the Basel Accord since the beginning of
1993. The minimum requirement of the capital adequacy ratio was initially set at 7
per cent and was gradually raised to 8.5 per cent in October 1996. Additionally, Tier 1
capital was also raised to 6 per cent.
Therefore, BOT ensures every process of banking supervision such as licensing;
issuance of prudential regulations; regular risk examination; timely resolution of
problem banks; domestic and foreign supervisory coordination is conducted with
transparency and professionalism. In addition, to increase the efficiency and
effectiveness of financial institutions supervision, the BOT also considers other
relevant international standards and encourages the growth of essential financial
institutions infrastructures. At the present time, the BOT intends to move from the
capital adequacy framework under Basel I to the New Basel Capital Accord (Basel II),
applicable to all financial institutions in 2009. Therefore, domestic banks will be
required to be ready for Basel II compliance testing and implementation by end of
2007. The new guideline for capital requirements will not only cover all the major
11
risks faced by financial institutions, but will also be more reactive to the riskiness and
complexity the financial market environments.
PHILIPPINES
In March 2001, the Bank Sentral ng Philippines (BSP) adopted the original Basel I
framework. Initially, this circular only provided guidelines for the computation of
risk-based capital for credit risk. The BSP’s risk-based capital adequacy framework
was further improved in December 2002, which required banks to measure and apply
capital charges against their market risk, in addition to their credit risk. On the
implementation of the Basel framework, the BSP imposes a minimum CAR of 10 per
cent on both domestic and foreign banks. This minimum required ratio of 10 per cent
was set higher than the Basel I or Basel II recommended ratio of 8 percent to consider
other risks not captured in the current framework. This requirement is applied on both
a solo and a consolidated basis. The calculation of capital charge for market risk is
matching the 1996 Amendments to Basel I. The calculation of qualifying capital is
also the same as that set in Basel I. It consists of Tier 1 and Tier 2 capital, where total
Tier 2 capital should not exceed Tier 1, and lower Tier 2 should not exceed 50 percent
of Tier 1.
Towards maintaining a healthy and strong banking system, plans include asset
clean-up and capital base build-up through compliance with International Accounting
Standards by 2005, and adoption of the Basel II Capital Adequacy Framework by
2007 will be taken by the BSP. Basel II uses standardized approach for credit risk, and
basic indicator and standardized approaches for operational risk. Under the
standardized approach for credit risk, risk weights would mainly depend on the
12
external rating of the counterparty. And also, under the basic indicator approach for
operational risk, capital charge is 15% of the 3-year average of a bank’s gross income.
1.3 OBJECTIVES OF THE STUDY
There are total four objectives for this study.
The first objective of this study is to examine how East Asian Bank set their capital
ratios and the factors that influence the capital ratios. Total 238 banks from seven East
Asian will be included in the study. Out of the seven countries, four of them are South
East Asian countries, Malaysia, Thailand, Indonesia and Philippines and the
remaining three countries are Japan, China and South Korea.
Since there are seven countries in the research sample, the second objective of this
study is to examine whether the setting of capital ratios heterogeneous across the
region. It is important to identify the similarities and differences among the countries
such as the behaviour of the domestic banks’ structure and also the rules set by the
domestic regulatory authorities. The differences between the countries may affect the
effectiveness of international regulatory standards.
The third objective of this study is to examine whether the capital decisions are
related to banks’ risk taking behaviours. Evidence show that some of the banks in
developed countries will increase their exposure in risky investments to maintain the
regulatory capital level.
13
Finally, the financial institutions are heavily regulated because they play an important
role in the economy. The forth objective of this study is to examine whether the
capital requirements determined by the regulators influence the capital decision of
banks.
1.4 SCOPE OF THE STUDY
This study required the use of data consisting of annual data of banks capital
adequacy ratio, and other variables affecting the target capital for seven countries that
represent the whole of Asian Bank for the period from 2004 to 2007. Since recent
studies on the relationship between bank capital and risk are remain unclear, thus, we
aim to extend our research to examine the direction of the relationship again.
14
1.5 ORGANISATION OF THE STUDY
This study is organized into five chapters.
Chapter 1 introduces the Capital Adequacy Framework (Basel I), suggesting by the
Bank for International Settlements (BIS), followed by regulations of capital adequacy
in the sample countries. This chapter also highlights the importance of the study of
determinants of banks’ capital. Objectives and scope of this study also presented in
this chapter.
Chapter 2 describes the literature review on the banks capital determination,
relationship between banks risk and capital, surveying previous researched conducted
in this area.
Chapter 3 presents conceptual background, source of data, research instruments, panel
regression model, and statistical analysis method.
Chapter 4 provides results of the study, which includes analysis and explanations of
the major research findings
Chapter 5 summarizes the conclusion, contribution of study, limitation and
recommendations for future research.
15
CHAPTER 2: LITERATURE REVIEW
The aim of this chapter is to briefly and critically review the past theoretical and
empirical research done in the area of bank capital requirement. There has been
considerable empirical evidence in several developments countries provide some
insides about the factors that affects the banks’ target capital ratio. However, empirical
work in this subject matter is still scanty in the East Asian country and seldom
researched.
This chapter begins with reviewing the early research on capital adequacy, the
definition of bank capital and the role of bank capital according to different sources.
We review the past literature by dividing the study area into four major categories.
First the bank capital management; second the bank capital and bank behaviours; third
the determinants of bank capital and fourth empirical findings on Asian banks.
2.0 EARLY RESEARCH
Effects of capital adequacy regulations on banks’ behaviour have been actively
analysed before. The earliest research on capital adequacy can trace back to 1977.
Kahane first examines the effectiveness of capital adequacy and the regulations
imposing to the financial intermediaries. In the theory literature, Koehn and
Santomero (1980) and Kim and Santomero (1988), using a mean-variance framework,
show that increased regulatory capital standards may lead banks to choose risky
portfolios to cover the loss in utility from the decrease in leverage.
On the contrary, Furlong and Keeley (1989) demonstrate that an increase in capital
reduces the value of the deposit insurance put option, thereby reducing the incentive
16
of banks to increase portfolio risk. This shows the empirical evidence on the
effectiveness of capital adequacy requirements is also mixed.
2.1 DEFINATION AND ROLE OF BANK CAPITAL
Banks play an important role in the global economy, and are the first category of
institutions to be subject to internationally coordinated capital regulation. The failure
of a large number of banks or the failure of a small number of large banks could result
a chain reaction that may harm the stability of the financial system. As a result, lead to
a global recession as what is happening in 2008.
The typical textbook explained that there is no need to investigate banks’ financing
decisions because capital regulation determined the capital structure of a financial
institution which is different from the non-financial firms.
“Banks also hold capital because they are required to do so by regulatory authorities.
Because of the high costs of holding capital […], bank managers often want to hold
less bank capital than is required by the regulatory authorities. In this case, the
amount of bank capital is determined by the bank capital requirements (Mishkin,
2007, p.233).”
Thus, the decisions of the amount of capital that banks hold are made base on three
most common reasons. First, bank capital aids to prevent bank failure. A bank
maintains bank capital to reduce the chance of become insolvent. Banks will prefer to
have a sufficient capital to act as cushion to absorb the losses. Second, the amount of
capital affects returns for the equity holders of the bank. The higher the bank capital,
17
the lower the return that the owners of the banks. Because of there is a trade off
between the safety and the returns to equity holders, the bank managers had to set an
optimal level of bank capital. Third, a minimum amount of bank capital is required by
the regulators.
An important paper of Berger et al. (1995) discusses thoroughly about the reasons of
banks hold capital and also the role the capital of financial institutions. Different from
the others studies, the authors look at the ‘frictions consideration’ of setting bank
capital ratio. They find that taxes and the costs of financial distress, transaction costs
and asymmetric information are among the frictions consideration.
2.2 BANK CAPITAL MANAGEMENT
In the earlier section, we identify the reasons of banks hold capital and the role capital
of financial institution. The existing literature on bank capital management is based
mainly on U.S and European institutions. Moyer (1990) uses U.S Commercial banks
and examines whether banks use loan loss provisions and loan charge-offs, among
other variables, to adjust accounting numbers to improve the capital ratio. The results
find evidence that bank loan loss provisions and capital ratios are negatively related.
This is consistent with the hypothesis that use of loan loss provisions reduces
expected losses from violating capital requirements.
Chen and Daley (1996) using a simultaneous equations approach, examine regulatory
capital and earnings management effects on the loan loss provisions of Canadian
banks. The results suggest that loan loss provisions are used to manage the capital
ratio but not to manage earnings during the period 1977 to 1987. Ahmed, Takeda, and
18
Thomas (1999), similar with Chen and Daley (1996), find that U.S banks managed
regulatory capital but not earnings during the changes in the capital adequacy
regulations imposing in 1990.
In contrast with the aforementioned findings, Collins et al. (1995) find results that
contradict Moyer (1990). They do not find support that is consistent with the
regulatory capital management. However, they find that there is a positive relationship
between regulatory capital and loan loss provisions.
2.2 BANK CAPITAL AND BANK BEHAVIOURS
Regulators have increased their focus on the capital adequacy of banking institutions
to enhance the stability of the financial system in recent years after several financial
crises. In the past decades, an increasing branch of the theoretical literature has tried
to assess the effects of minimum capital requirements on capital and banks’ risk. We
review the relationship of bank capital and bank behaviours based on the two major
theories in this field; which are the moral hazard theory and the capital buffer theory
(Marcus 1984, Milne and Whalley 2002).
An increasing number of empirical papers (Shrieves and Dahl 1992; Jacques and
Nigro 1997; Aggarwal and Jacques 2001; Rime 2001) have tried to test the moral
hazard theory. The empirical literature has mainly tested the moral hazard theory,
building on a model developed by Shrieves and Dahl (1992). The majority of the
papers find a positive relationship between capital and risk adjustments, indicating
that banks that have built up higher capital, simultaneously, also increased risk. They
describe the problem of "gambling for resurrection": poorly capitalized banks select a
19
risky asset portfolio at the cost of the deposit insurance system. Therefore, their
finding has been interpreted as supporting the moral hazard theory.
Besides studies on the moral hazard theory, there are several studies on banks reaction
to capital requirements focused on the alternatives theory, the capital buffer theory.
The capital buffer is the excess capital a bank holds above the minimum capital
requirement. The capital buffer theory implicates that banks with low capital buffers
attempt to rebuild an appropriate capital buffer and banks with high capital buffers
attempt to maintain their capital buffer. Frank Heid et al (2004) access how German
savings banks adjust capital and risk under capital regulation, and they find that the
coordination of capital and risk adjustments depends on the amount of capital the
bank holds in excess of the regulatory minimum (the capital buffer).
Therefore, the moral hazard theory and the capital buffer theory have different views
for how banks adjust capital and risk under the minimum capital requirements. The
moral hazard theory expects that when capital requirements force banks to increase
capital, the reaction of the banks are to increase risk as well. By contrast, the capital
buffer theory expects that the behaviour of banks depends on the size of their capital
buffer. This means that banks with high capital buffers will aim at maintaining their
capital buffers while banks with low capital buffers will aim at rebuilding an
appropriate capital buffer. As a result, for banks with high capital buffers, capital will
be positively related to risk adjustment, whereas for banks with low capital buffer,
capital will be negatively related to risk adjustments.
20
2.4 RESEARCH ON DETERMINANTS OF BANK CAPITAL
There are several research draws from the previous studies strive to investigate the
determinants of bank capital. (Volker & Martin, 2008) analyse the determinants of
capital for German banking sector comprising of three characteristic banking groups
including savings banks, cooperative banks and other banks, which greatly differ
regarding their ownership and their access to the capital market compare to
commercial banks and investment banks. Their framework is again building on
Shrieves and Dahl (1992) with addition bank-specific effects i. The major findings
that related to this study are the authors find that; first, changes in portfolio risk are
significantly and positively affect the changes in the capital ratio for savings banks.
Second, banks’ profitability has a positive and significant impact on the target capital
ratio for savings banks and cooperative banks. Third, size has a negative impact on
the target capital ratio.
2.4.1 THE PRESENCE OF GOVERNMENT GUARANTEES
Capital injection and bailed out by the government helps bank to lessen the risky
loans at the margin (Kentaro, 2007). However in the presence of Government
guarantees and deposit insurance, the banks continue to fail and go out of business
throughout the world. The numbers of banks which need bail out are also at alarming
rates. Government guarantees provide incentives to the banks’ management to take
unnecessary risk because to some extent they are not bound to repay their depositors.
Besides, deposits insurance promise to protect depositors from the threat of deposits
run. The cost of these deposit insurance and government guarantee also reduces
incentives for depositors to monitors their banks (Freixas and Rochet 1998).
21
Berger et al. (1995) argue that government safety net guarantees reduce the incentive
to issue equity shares, causing market capital levels to be artificially reduced. Hence,
banks face a number of agency problems and associated moral hazard risks that
impose on the capital decision without and with capital regulation.
Demirguc-Kunt et al. (2002) provide evidence that explicit deposit insurance tends to
damage the bank stability, especially when bank interest rates are deregulated and the
institutional environment is weak. The impact of deposit insurance further explain by
Hovakimian et al (2003) who investigate thoroughly on how well authorities in 56
countries have control the bank risk shifting incentive in recent years. They argue that
deposit insurance clearly aggravating risk shifting. Finally, it had adverse effects in
environment that are low in political and economic freedom and high in corruption as
it is difficult to implement suitable restraints.
2.4.2 THE EFFECTS OF CAPITAL REGULATIONS
Edizt, Micheal and Perraudin (1998) assess the effect of the Basel Capital Accord
adequacy requirement on capital ratios of UK banks. By using confidential
supervisory data, they discover that when the capital ratio of the UK banks
approaches its minimum value required by the authorities, bank increase the capital
ratio in the following quarter. They observe that the increase in capital ratio of banks
is likely come from an increase in narrow capital and there is no evidence that UK
banks increase risk-taking in order to achieve and exceed the minimum target ratio.
The results also indicate that the capital requirements significantly affect the capital
ratio.
22
Jürg Blum (1999) suggests that capital adequacy rules may increase a bank's riskiness.
The intuition behind the suggestion is that under binding capital requirements an
additional unit of equity tomorrow is more valuable to a bank. Therefore when raising
equity is very costly, the only possibility to increase equity tomorrow is to increase
risk today.
Hovakimian and Kane (2000) find capital regulation did not prevent large U.S. banks
from shifting risk onto safety net during 1985-1994. They argue that deposit insurance
and poor capital supervision encourage banks increasing their risk taking behaviour as
they can extract deposit-insurance subsidies. However, Aggarwal and Jacques (2001)
reports that US banks increased their capital ratio without increases in credit risk.
They concluded that the prompt corrective action (PCA) positively and significantly
affected capital ratio in both high capital and low capital banks, with a faster speed of
adjustment in undercapitalized banks.
Similarly Rime (2001) examines the Swiss banks’ capital and risk behaviour. The
author adopts a simultaneous equations approach to examine whether Swiss banks
which close to the minimum regulatory standards tend to increase their capital ratio.
He suggests regulatory pressure has a positive and significant impact on capital ratio.
However, there is no evidence of capital requirement has significant impact on the
banks’ risk taking behaviour.
David van Hoose (2007) reviews the academic studies of bank capital regulations and
he finds previous literature are remain unclear towards the effects of capital regulation
on portfolio risk as well as the overall safety and soundness for the banking system.
23
This is because banks may make riskier asset choices in order to enlarge the “capital
cushion”. However, the literatures generally agree that the immediate effects of
constraining capital standard are likely to reduce the credit risk of the banks.
Capital regulation may influence the bank’ lending behaviors, in turn affect the banks’
portfolio risk. Kentaro (2007) finds that capital regulations do not prevent risk taking
behaviours as undercapitalized banks may issues more subordinated debts to meet the
capital requirements. However, the Kentaro doubt that, the issues of recapitalized
using subordinated debts may allow Japanese banks to swift their loan portfolio
towards more risky investments in real estate sector and worsened the non performing
loans problems.
2.4.3 SHAREHOLDERS AND MANAGERS’ RISK AVERSION
The ownership structure may also affect the target capital level. Saunders, Strock and
Travlos (1990) concentrate on the risk taking behaviours of both stockholder
controlled banks and managerially controlled banks. The limited liabilities of the
stockholders grant them incentive to increase the risk of the company. In contrast, the
banks’ manager decision on the amount of capital hold is influence by the degree to
their best interests. The results support their hypothesis where the stockholders
controlled banks show significantly higher risk taking behaviour than managerially
controlled banks. This entails that the regulators must increase examination for
stockholders controlled banks.
Apart from this, Ronald C. Anderson and Donald R. Fraser (2000) present evidence
that managerial shareholdings are an important determinant of bank risk-taking.
Managerial shareholdings are positively related to total and firm specific risk in the
24
late 1980s when banking was relatively less regulated and when the industry was
under considerable financial stress. In addition, Jeitschko and Jeung (2005) also
suggest bank risk is negatively correlated with capitalization if the shareholders’
incentives are dominating factors in determining asset risk. In contrast to this, they
also point out the possibility that bank risk is positively correlated with capitalisation
whenever managerial incentives are dominating factors in determining asset risk.
2.4.4 BANK EARNINGS OR CHARTER VALUE
Charter Value can also defined as the value of a bank being able to continue to do
business in the future, reflected as part of its share price. Demsetz et al. (1996)
suggest that franchise value plays an important role in banking because it helps
mitigate the moral hazard problem. In order to maintain the franchise value, this will
give the banks’ management additional incentive to avoid excessively involved in
risky businesses besides meeting the minimum level required by the regulator. Their
empirical analysis supports the negative relationship of franchise value and risk. That
banks having a lower franchise value (alternative term for charter value) behave more
aggressively.
Additionally, Saunders and Wilson (2001) suggest that the relationship between
charter value and capital structure decisions is procyclical. Their regression results
show that during economic booms situation, high charter value banks posses a higher
capital ratio. Nevertheless, during economic recessions, higher charter value banks
uphold higher losses of charter value. The most important finding of this paper is that
charter value may not able to lessen the amount of risky activities that banks involved.
25
2.5 EMPIRICAL FINDINGS ON ASIAN BANKS
The last section of this chapter reviewed some major empirical studies based on Asian
banks sample. Song (1998) examines Korean banks’ responses to the Basel risk
weighted capital adequacy requirements implemented in 1993. The author concludes
that the higher capital requirements were generally effective because Korean banks
generally did not much utilize “cosmetic” adjustments to increase their capital ratios.
Likewise, S. Ghosh et al. (2003) find that Indian public sector banks have not resorted
to assets substitution across the risk-weight categories by substitute low risk
government securities for high risk loans in order to meet their capital requirement.
This shows shat capital regulation does influence the banks decisions making.
Yu (2000) documents bank size; liquidity and profitability are the main determinants
of bank capital ratio in Taiwan. The author summarises that large banks in Taiwan
have much lower capital ratios than the small banks which is consistent with the
previous study where the large banks feel that they are “too big to fail”. The author
also suggests that the banks mainly use internal source of capital, this contributes that
more profitable banks tend to have higher capital ratios. The remarkable finding of
this paper is the relationship between the equity-to-asset ratio and the liquidity ratio is
significantly positive for small banks, but significantly negative for medium size
banks.
Ito and Sasaki (2002) investigate how Japanese banks responded to the introduction of
BIS based capital regulation and to the decline in stock prices. They found that
Japanese bank reduced lending and increased levels of subordinated debt to maintain
their capital adequacy ratios between 1990 and 1993. Kentaro (2007) also finds that a
26
capital adequacy requirement did not prevent risk-taking behavior of undercapitalized
banks since they then just issued more subordinated debts to meet this requirement.
Jeitschko and Jeung (2008) build a testing model that incorporates the three different
incentives of the three entities that are involved in the risk determination of a bank.
Based on empirical findings on Korean commercial banks, they propose that capital
regulation alone may not be enough to safeguard the sound banking business of banks
since high capital banks present positive relationship in bank capitalisation and
portfolio risk. Secondly, the negative relationship between risk and capitalisation for
commercial banks with low capital suggests that the closer monitoring
implementation are required to prevent those banks from gambling in excessively
risky activities.
One of the latest local empirical study by Ahmad, R. , Ariff, & Michael, (2008), as
well as the main reference of this study, reports new findings on determinants of bank
capital ratios in Malaysia. This study presents a positive relationship between
regulatory capital and banks’ risk taking behaviour. The study also observes that
capital requirement regulations introduced in 1996 was ineffective whereas those
mandated in 1997 are proved successful in the financial crises period. Also, the study
finds inconsistency with developed country literature where results shows that bank
capital ratios not to been motivated by bank profitability.
27
2.6 CHAPTER SUMMARY
This chapter begins with reviewing the early research on capital adequacy started in
1977 by Kahane who investigate the effectiveness of capital adequacy regulations
enforcing to the financial intermediaries. Next, this chapter provides the definition of
bank capital and role of bank capital according to different sources. Studies on bank
capital management are predominantly based on U.S banks. The relationship between
bank capital and bank behaviours are discussed based on both the moral hazard theory
and capital buffer theory. There is little consensus among the reviewed literature that
banks, whether adequately capitalized or not, engaged in riskier activities. The
determinants of bank capital are discussed in four subsections which are the presence
of government guarantees, the effects of capital regulations, shareholders and
managers’ risk aversion and bank earnings or charter value. The final section of this
chapter provides the major findings for the Asian banks for this research area. Similar
to the U.S banks, the evidence of the effectiveness of the capital adequacy regulation
are remain ambiguous.
28
CHAPTER 3: DATA AND RESEARCH METHODOLOGY
This chapter describes the research hypotheses and methodology used in the study.
This included a discussion on the data used in the study, data source, data collection
procedures, statistical techniques used to analyze the research data and formulation of
the hypotheses. Apart from this, the priori of the direction of the variables will also be
discussed in the later section of this chapter.
3.0 RESEARCH HYPOTHESES
According to the capital buffer theory, banks will to hold a certain amount of excess
capital above the minimum level set by the regulatory bodies. The reasons behind
their aim to maintain a certain capital buffer are the explicit and implicit regulatory
costs, which would be the result of falling very close to or below the regulatory
minimum.
With the rationale discussed in chapter 2, the hypotheses of this study can be
established as followed. The hypotheses are developed in a null and neutral form, and
the mathematical representations are also presented:
H1: Credit Risk has no statistically significant impact on banks’ target capital ratio
H1a: 1 = 0
H1b: 1 0
29
H2: Management Quality has no statistically significant impact on banks’ target
capital ratio
H2a: 2 = 0
H2b: 2 0
H3: Bank Liquidity has no statistically significant impact on banks’ target capital ratio
H3a: 3 = 0
H3b: 3 0
H4: Bank Size has no statistically significant impact on banks’ target capital ratio
H4a: 4 = 0
H4b: 4 0
H5: Bank Leverage has no statistically significant impact on banks’ target capital ratio
H5a: 5 = 0
H5b: 5 0
H6: Bank Profitability has no statistically significant impact on banks’ target capital
ratio
H6a: 6 = 0
H6b: 6 0
30
H7: Regulatory pressure has no statistically significant impact on banks’ target capital
ratio
H7a: 7 = 0
H7b: 7 0
3.1 RESEARCH METHODOLOGY
Building on Ahmad, R. , Ariff, & Michael, (2008), and slightly modified form of
models, this study formulate a multivariate panel regression model. The model is
derived to find the level of bank capital of bank i in period t as a function of a range of
bank specific variables as well as variable that measures regulatory pressure.
Thus, the panel regression model is written as:
Y i, t = β0 + β1 LLR i, t + β2NIM i, t + β3 LACSF i, t + β4
SIZE i, t + β5 EQTL i, t + β6 ROA + β7 REG i, t
+ ε i, t (1)
where:
Yi, t : capital ratio of bank i at time t,
LLR i, t: ratio of Loan Loss Reserves to gross loans of bank i at time t,
NIM i, t: net interest margin of bank i at time t,
SIZE i, t: natural log of total assets of bank i at time t, and
EQTL i, t: ratio of total equity to total liabilities of bank i at time t,
LACSF i, t: ratio of total liquid asset to total deposit of bank i at time t,
REG i, t: a dummy variable: one denotes low capital bank and 0 otherwise,
ε i, t is the residual term, included to reflect all other market imperfections and
31
regulatory restrictions affecting bank capital ratio.
Β1 –β7: parameters to be estimated
The following section discusses each of these variables and their expected impact on
bank capital as proposed in the literature
3.2 DEPENDENT VARIABLE – CAPITAL ADEQUACY RATIO (CAR)
As stated earlier in the background of the study, banks must maintain two risk-based
capital requirements which are the ‘tier 1 requirement’ as well as the ‘total capital
requirement’. The total capital requirement requires a total risk-weighted capital
adequacy ratio of 8 per cent is used as the proxy for bank capital ratio in this study.
(Jacques and Nigro, 1997; Ediz et al, 1998; De bondt and Prast, 2000; Rime, 2001).
CAR is calculated according to the 1988 Basel Accord, presented as below:
10021(%)
etseightedAssTotalRiskW
capitalTiercapitalTierCAR (2)
For banks, tier 1 capital consists primarily of ordinary paid-up share capital and share premium,
statutory reserve fund, general reserve fund and retained earnings, whereas tier 2 includes general
loan loss provisions, subordinated debt, and other hybrid capital. The amount of risk weighted
assets would be compute from different categories of assets and off-balance sheet exposures,
weighted according to broad categories of relevant riskiness. The classification of risk weights is
kept in 5 weights (0%, 10%, 20%, 50% and 100%).
32
3.3 VARIABLES AFFECTING TARGET CAPITAL
Eight explanatory variables from the literature are selected as the determinants of
bank capital; six bank specific variables (LLR, NIM, LACSF, EQTL, and SIZE), two
country macro variables (RGDP and BASE) and one regulatory factor (REG). Their
selection criteria and a priori expectations of expected relationship with bank capital
are referred to previous developed country bank studies.
Back Specific Variables:
Bank risk taking – Loan Loss Reserve
In this study, we employ accounting based model of bank risk are rather than market
based one. This is because most of the banks in the study are non-listed company. The
first accounting risk measurement is the Loan Loss Reserve (LLR). LLR defined as a
valuation reserve against a bank's total loans on the balance sheet, representing the
amount thought to be adequate to cover estimated losses in the loan portfolio. We
consider Loan Loss Reserves to gross loans ratio as a proxy of bank risk as this ratio
may indicate the banks’ financial health. A negative impact of LLR in capital could
mean that banks in financial distress have more difficulties in increasing their capital
ratio. In contrast, a positive effect could signal that banks voluntarily increase their
capital to a greater extent in order to overcome their bad financial situation.
Management quality – net interest margin
Net interest margin is defined as the ratio of net interest income to average earning
assets. It is a summary measure of banks' net interest rate of return. While it is well
known that the net interest margin is a significant element of bank profitability,
however the effects of market interest rate volatility and default risk on the margins
are not well recognized. The net interest margins are set by banks to cover the costs of
intermediation besides reflect both the volume and mix of assets and liabilities. More
33
specifically, adequate net interest margins should generate adequate income to
increase the capital base as risk exposure increases. (Angbazo, 1997). The charter
value which discussed in introduction predicts a positive relationship between bank
management quality and bank capital. However, bank management may reduce the
capital cushioning if the default risk is very low. As a result, the coefficient of NIM
can also have a negative sign.
Bank Size - SIZE
Bank size, as measured by the log of total assets because bank size may also
influences the amount of bank capital. Jackson et. al (2002) propose that the large
banks wish to keep their good ratings and therefore have considerable
market-determined excess capital reserves. However, most recently, Gropp and Heider
(2007) and earlier Shrieves and Dahl (1992) found that a banking organization’s
asset-size is an important determinant of its capital ratio in an inverse direction, which
means that larger banks have lower CARs. This may occur because firm size might
serve as a proxy for a banking organization’s asset diversifications which reduces
their risk exposure. So, the coefficient of SIZE can have either a positive or negative
sign.
Bank Liquidity – LACSF
A liquid asset to customer and short term funding are included to proxy bank liquidity.
Angbazo, 1997 states that as the proportion of funds invested in cash or cash
equivalents increases, a bank's liquidity risk declines, leading to lower liquidity
premium in the net interest margins. Therefore, an increase in bank liquidity (high
LACSF) may have a positive impact to capital ratio.
Bank Leverage – EQTL
The final bank specific variable is the bank leverage factors which proxy by the total
34
equity to total liabilities ratio. A high EQTL denotes low leverage whereas a low
EQTL indicates high leverage. Shareholder will find high leveraged banks are more
risky compared to other banks, therefore this increase required rate of return of the
shareholders. Consequently, the high leveraged banks (Low EQTL) may find raising
new equity difficult due to the high cost of equity capital. Ultimately, the high
leveraged banks may hold less equity than low leveraged banks. We expect the
coefficient of EQTL is positive.
Bank Profitability – ROA
Profitability also influences a bank’s capital ratio. Gropp and Heider (2007) find that
more profitable banks tend to have more capital relative to assets. In general banks
have to rely mainly on retained earnings to increase capital. ROA and the capital ratio
is most likely positively related, because a bank is expected to have to increase asset
risk in order to get higher returns in most cases (Jeitschko and Jeung, 2007). Hence,
the bank’s return on assets (ROA) in the capital equation is included as a measure of
profits with an expected positive sign.
Regulatory Policy Factors:
Regulatory Pressure – REG
Capital requirements create pressure on undercapitalized banks to maintain higher
capital ratios. A bank having a capital ratio close to the regulatory minimum may have
an incentive to increase its capital ratio to prevent the ratio from falling below the
regulatory minimum. The dummy variable, REG is deal for banks with CAR less than
the industry wide average calculated by the central bank, zero otherwise.
Country Macro Variables: - RGDP, BASE
A country’s growth and the extent to which its financial system is bank based are
important determinants of the capital structure of banking organizations. Economic
35
cycles may influence the level of CAR, as capital holdings may change over time to
accommodate fluctuations in risk arising from variations in the economic environment.
In an economic downturn, the possibilities of a fall in capital increases as a result of
possible increases in the write-offs and provisions. Banks may therefore take
precautionary measures by holding more capital, and those relying on credit rating to
gain access to capital markets may also need to raise their capital holdings to maintain
their ratings during a downturn. In an upturn, risks are less likely to materialise and
banks can safely hold less capital. One could then expect that during a downturn
banks would hold higher CARs than during an upturn.
A financial system which is more bank-based or more market-based could reflect the
degree of competition within the system. Schaeck and Cihak (2007) show that banks
tend to hold higher capital ratios when operating in a more competitive environment.
This is consistent with the observation that more bank-based in an economy, there is
less competition from capital markets and therefore the bank insolvency risks are
smaller.
Table 1 shows the summary of the selected bank specific variables that affect the
target bank capital. The expected relationship between the bank specific variables and
the bank capital ratio also presented.
Table 1: Variables and predicted signs Variables Predicted Signs Loan Loss Reserve (LLR) +/- Net Interest Margin (NIM) +/- Bank Size (SIZE) +/- Liquid Asset to Customer and Short Term Funding (LACSF) + Total Equity to Total Liabilities (EQTL) + Return on Asset +
36
3.4 METHOD SELECTION
Since the characteristic of data involve cross sections and time series, i.e panel data,
the most appropriate statistical method should be the pooled regression. According to
Hsiao (1986), the panel data sets have several main advantages over conventional
cross-sectional or time-series data sets. First, they contain more degrees of freedom
and more sample variability than cross-sectional data which may be viewed as a panel
with T = 1, or time series data which is a panel with N = 1, hence improving he
efficiency of econometric estimates.
Second, they allow a researcher to analyse several important economic questions that
cannot be attended using cross-sectional or time-series data sets alone. A
cross-sectional data is not able to distinguish between two possibilities, but panel data
can because the sequential observations for a sample contain information about two or
more possibilities. Similarly, a single time-series data set regularly cannot provide
precise estimates of dynamic coefficients without specifying a priori that each of them
is a function of only a very small number of parameters. Therefore panel data are
available to utilize the individual differences in explanatory variables in order to
reduce the problem of multicollinearity.
Third, the use of panel data set also provides a way of resolving or reducing the
magnitude of a key econometric problem that often arises in empirical studies, e.g.
omitted or unobserved variables that are correlated with explanatory variables. Panel
data contain information on both the intertemporal dynamics and the individuality of
the entities may allow one to control the effects of missing or unobserved variables.
37
3.5 SAMPLING AND DATA COLLECTION
To empirically test the determinants of bank capital ratio, a balanced panel of data
from Asian banks' balance sheets and income statements for fiscal years 2004-2007
were used. Annual data obtained from the Bankscope database of the Bureau van Dijk.
Standard reports available in the Bankscope database of the Bureau van Dijk were
taken to provide a standard measure of the figures and ratio.
In expanding the previous study which used Malaysian banks sample, this study adds
three neighbouring countries of Malaysia which are Thailand, Indonesia and
Philippines on top of three East Asia countries namely China, Japan and South Korea.
First, these countries are selected of their geographic location which is located in East
Asia. Secondly, the countries have a relatively comparable financial system, for this
reason, countries such as Myanmar, Laos, Cambodia and some others are excluded in
this study. Third, in order to get standardize figures for comparison purposes, data are
only obtain from one source which is the Bankscope database of the Bureau van Dijk.
Therefore, samples are subject to subscription to the database. Unfortunately there is
only one Singaporean bank which has complete data in the database so we have no
choice to omit Singapore in this study.
The study covers four years from 2004 to 2007. This time period was selected to
observe the determinants of the bank capital ratios after the Asian financial crisis.
Besides, the time period from 2004 to 2007 was determined after considering the
following two reasons. First, Philippines only adopted Basel I in 2002. Second, in
these four years, most of the countries in the sample were preparing themselves to
adopt Basel II gradually.
38
The banks were screened in two ways. First, we must ensure that each bank have
complete four years of data relating to variables to be used in regression. To be exact,
the bank must exist consistently from 2004 to 2007. Similarly, all the accounting
variables must fall within the range of -100 percent and +100 percent and therefore
extreme values of reported data due to possible reporting errors will be excluded from
the sample.
The final sample of study consists of 238 banks, with 109 Japanese Banks, 26 Chinese
Banks, 18 South Korean Banks, 32 Indonesian Banks, 28 Malaysian Banks, 13
Philippines Banks and 12 Thailand Banks.
3.6 STASTICAL ESTIMATION AND INFERENCE
The data analysis process here involved editing, coding, carrying out checks and
finally summarizing the findings. The statistical package EViews version 5.0 was used
mainly in data analysis. EViews is useful in providing powerful statistical, forecasting,
and modeling tools through an innovative, easy-to-use object-oriented interface.
The resultant statistical inference will include:
1) Test of significant relationships (F)
Based on the analysis of variance (ANOVA), the F-value of the regression shows how
well the set of explanatory variables in the model is related to the dependent variable
at a cut off level of significance (). If the regression’s F – value is greater than the
critical F – value with = 0.05, then there is a significant relationship between the
explanatory variables and the dependent variable. The probability of the F – value for
39
the regression indicates how important the independent variables are explaining the
variability of the dependent variable.
2) Test of significant parameters (t)
This test involves the estimation of each explanatory variable’s parameter
(-coefficient) and how statistically significant it is in explaining the dependent
variable. The necessary of each independent variable in the model can therefore be
reviewed. The t-value is used to test the hypothesis that there is no linear relationship
between the dependent variable and independent variables. The t-value considers
other variables in the regression model. Similar to the F – value, the test of significant
parameter is based on the t – value measured against the critical t – value with =
0.05
3.7 CHAPTER SUMMARY
This chapter started with formulating seven hypotheses of this study. This study
formulates a multivariate panel regression model building on Ahmad, R. , Ariff, &
Michael, (2008). Next, the dependent variable and the independent variables
determination and their expected impact on bank capital are discussed in the third and
forth section of this chapter respectively. Panel data sets are used as they have three
main advantages over conventional cross sectional or time series data sets. Annual
data for the selected 238 banks from 2004 to 2007 were obtained from the Bankscope
database of the Bureau van Dijk. The banks were screened by the existence of the
banks and the range of accounting variables. The statistical package EViews version
5.0 was used mainly in data analysis. Test of significant relationships and test of
significant parameters are the two main resultant statistical inferences that will be
included in the study.
40
CHAPTER 4: RESEARCH RESULTS
This chapter presents the results of the study. It starts with a description of the general
data set used, followed by an analysis and discussion of the relationship that exists
between the variables.
4.0 DESCRIPTIVE STATISTICS
Table 2 and 3 present the descriptive statistics and the pair-wise correlation matrix of
the regression variables respectively.
Table 2: Pooled-sample descriptive statistic of the selected dependent and explanatory
non-dummy variables
CAR LLR NIM ROA LASCF EQTL SIZE Mean 13.45799 3.314170 2.777574 0.749921 20.06831 8.350588 8.806542 Median 11.40000 2.255000 2.200000 0.485000 17.02000 6.440000 9.345650 Maximum 79.50000 44.93000 18.94000 5.740000 94.76000 50.63000 15.12280 Minimum -2.500000 0.270000 0.420000 -7.240000 1.100000 -14.50000 -1.024400 Std. Dev. 7.375411 3.565823 1.794236 0.980487 12.74313 6.529530 2.677424 Skewness 3.372038 5.432452 2.842429 -0.278481 1.729863 2.666552 -1.297330 Kurtosis 20.35500 50.09232 16.28226 15.37680 7.127386 13.41194 5.275581
Jarque-Bera 13751.59 92650.72 8279.859 6088.646 1150.532 5428.401 472.4512 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Sum 12812.01 3155.090 2644.250 713.9243 19105.03 7949.760 8383.828 Sum Sq. Dev. 51731.24 12092.05 3061.537 914.2491 154430.4 40545.66 6817.337
Observations 952 952 952 952 952 952 952 Cross sections 238 238 238 238 238 238 238 CAR: risk weighted capital adequacy ratio LLR: loan loss reserve/gross loan NIM: net interest margin ROA: return on average asset EQTL: total equity to total liability SIZE: Natural log of total assets
41
The results exhibit in Table 2 shows that the banks hold average capital ratio of
13.46% which is relatively higher than the 8% that set by the Basel Committee.
Table 3: The Pairwise Correlation Matrix for dependent variables and explanatory non
dummy variables
CAR LLR NIM LASCF SIZE EQTL ROA CAR 1 LLR 0.3108 1 NIM 0.3840 0.1154 1
LASCF 0.4196 0.2819 0.1320 1 SIZE -0.3757 -0.3116 -0.4075 -0.1591 1 EQTL 0.7732 0.2678 0.4124 0.3705 -0.4632 1 ROA 0.5828 0.1709 0.5897 0.2705 -0.3102 0.5635 1
4.1 TEST OF MULTICOLLINEARITY
Multicollinearity is a statistical phenomenon in which two or more independent
variables in a multiple regression model are highly correlated. In the presence of
multicollinearity, the estimate of one variable's impact on dependent variable while
controlling for the others tends to be less accurate than if independent variables were
uncorrelated with one another. In statistics, the variance inflation factor (VIF) is a
method of detecting the problem of multicollinearity. More precisely, the VIF is an
index which measures how much the variance of a coefficient (square of the standard
deviation) is increased because of collinearity. Considering the following regression
equation with k independent variables
Y = β0 + β1 X1 + β2 X 2 + ... + βk Xk + ε (3)
VIF can be calculated in three steps:
42
Step 1:
Calculate k different VIFs, one for each Xi by first running an ordinary least square
regression that has Xi as a function of all the other explanatory variables in equation
(3).
If i = 1, for example, the equation would be
X1 = c0 + 2 X2 + 3 X3 + … + k Xk+ ε (4)
where c0 is a constant and ε is the error term.
Step 2:
Then, calculate the VIF factor for i
with the following formula:
211)(
ii R
VIF
(5)
where 2iR is the coefficient of determination of the regression equation in step 1.
Step 3:
Analyze the magnitude of multicollinearity by considering the size of the )( iVIF
. A
common rule of thumb is that if )( iVIF
> 5 then multicollinearity is high.
43
To ensure no serious multicollinearity problem, step 1 to step 3 are performed. Table
4 presents the R-squared and VIF of the independent variable.
Table 4: Multicollinearity Test
Variables R-squared VIF LLR 0.159271 1.189443923 NIM 0.408340 1.690159889 LASCF 0.184429 1.226134818 SIZE 0.314398 1.458572175 EQTL 0.453462 1.829698941 ROA 0.479944 1.922869845
Table 4 shows that none of the R-squared from these equations are near to 1.0 and the
variance of inflation factor (VIF) is less than 5. Since VIF < 5, thus we can conclude
that no multicollinearity problem
4.2 ANALYSIS OF VARIANCE
The relationship between capital ratio and its determinants is examined with an
analysis of variance (ANOVA). We investigate the relationship for the determinants
stated in chapter 3.4. The ANOVA F-tests is summarized in Table 5 which presents the
probabilities at which the null hypothesis of no significant effects can be rejected.
That is, it gives the probability that all effects of the given determinants are zero.
Table 5: Test for Equality of Means Between Series
Test for Equality of Means Between Series Date: 03/31/09 Time: 19:58 Sample: 2004 2007 Included observations: 952 Method df Value Probability
44
Anova F-statistic (6, 6657) 1087.888 0.0000 Analysis of Variance Source of Variation df Sum of Sq. Mean Sq. Between 6 264341.2 44056.86 Within 6657 269592.5 40.49760 Total 6663 533933.7 80.13413 Category Statistics
Std. Err. Variable Count Mean Std. Dev. of Mean
CAR 952 13.45799 7.375411 0.239038 EQTL 952 8.350588 6.529530 0.211623
LASCF 952 20.06831 12.74313 0.413007 LLR 952 3.314170 3.565823 0.115569 NIM 952 2.777574 1.794236 0.058152 SIZE 952 8.806542 2.677424 0.086776 ROA 952 0.749921 0.980487 0.031778 All 6664 8.217871 8.951767 0.109658
Since the F – value is greater than the critical F – value with = 0.05, the results
indicate there is a strong relationship between the explanatory variables (EQTL,
LASCF, LLR, NIM, SIZE, ROA) and the dependent variable (CAR).
4.3 FINDINGS AND RESULTS
Equation (1) is estimated using panel data techniques in addition to pooled ordinary
least squared methods. The panel data model is written in matrix notation
ititit BXaY (6)
ititit v ' (7)
45
Where it is a random term which comprised of two components, it' refers to the
unobserved individual or firm-specific effects and itv refers to the remaining
disturbance.
Table 7 reports the regression results of estimating the relation between the Risk
Weighted Capital Adequacy ratio and bank specific factors. In this study, we employ
the Glesjer Lagrangian multiplier test for random effects to validate the exogeneity of
the individual effects with the explanatory variables. The result is shown as in Table
6.
Table 6: Lagrange Multiplier Tests – Glesjer Test
Dependent Variable: UHATABS Method: Panel Least Squares Date: 04/01/09 Time: 15:30 Sample: 2004 2007 Cross-sections included: 238 Total panel (balanced) observations: 952
Variable Coefficient Std. Error t-Statistic Prob.
C -0.258265 0.523881 -0.492984 0.6221 LLR 0.000780 0.028185 0.027662 0.9779 NIM -0.111932 0.066772 -1.676318 0.0940
LASCF 0.031873 0.008008 3.980308 0.0001 SIZE -0.027409 0.041568 -0.659383 0.5098 EQTL 0.308586 0.019091 16.16425 0.0000 ROA 0.067828 0.130331 0.520429 0.6029
R-squared 0.375104 Mean dependent var 2.459418 Adjusted R-squared 0.371137 S.D. dependent var 3.583648 S.E. of regression 2.841865 Akaike info criterion 4.934124 Sum squared resid 7632.008 Schwarz criterion 4.969849 Log likelihood -2341.643 F-statistic 94.54206 Durbin-Watson stat 0.640847 Prob(F-statistic) 0.000000
LM = nR2= 952 x 0.375104 = 357.099008
46
At = 0.05, 2 (6) =12.5916 , Decision: reject H0, there is heteroskedasticity.
Since the Glesjer test reject the hypothesis that the unobserved individual
heterogeneity is uncorrelated with the explanatory variable. This suggests that the
firm-specific effects and other variables in the model are correlated and so the fixed
effects model is the better choice to run.
The regression model takes account of time and firm fixed effects (ct and cf) to
consider the unobserved heterogeneity at the country level and across time that may
be correlated with the explanatory variables. Standard errors are clustered at the bank
level to account for heteroscedasticity and serial correlation of errors (Petersen, 2007)
The regression results are shown in columns (1) – (6) Table 7 for the capital adequacy
ratio. Column (1) shows the results for a basic model that specifies capital adequacy
ratio only as a function of bank-specific factors plus year-fixed effects. Next, the
model expanded to include country fixed effects in column (2) and then firm fixed
effects in column (3). Column (4) – (6) further include country macro variables. The
impact of the country macro variables can be measured by comparing the results of
the expanded specifications with the baseline models.
Table 7: Determinants of capital ratio
The dependent variable is the risk-weighted capital adequacy ratio (CAR). The explanatory variables include six bank specific variables (LLR,
NIM, LASCF, SIZE, EQTL, and ROA), one dummy variable representing regulatory pressure (REG), and 2 country macro variable (BASE and
RGDP). LLR refers to the loan loss reserves to gross loan ratios. NIM and LASCF are the net interest margin and the ratio of total liquid assets
to total deposits respectively. SIZE represents the natural logarithm of total assets. EQTL is the ratio of total equity to total liabilities; ROA is the
return on average assets. The dummy variable REG refers to regulatory pressure, denoted by 1 for low capitalized banks and zero otherwise.
Two country macro are BASE represents the financial systems of the country and also RGDP refers the real gross domestic products growth of
the country. Total number of observations is 952. Reported in parentheses are robust standard errors. * indicates significance at the 10% level, **
indicates significance at the 5% level, *** indicates significance at the 1% level.
Variables CAR (1) (2) (3) (4) (5) (6)
Constant 4.8401*** 6.2690*** 11.98769*** 5.0941*** 4.9000*** 19.17248*** (6.078098) (6.6752) (2.882364) (6.0546) (2.7910) (3.814857)
Bank Specific LLR 0.1780*** 0.1962*** 0.177985*** 0.1870*** 0.1942*** 0.152149***
(4.1250) (4.1933) (3.277964) (4.3063) (4.1431) (2.782292) NIM 0.0475 -0.6861*** -0.312744* -0.0252 -0.6901*** -0.377584**
(0.4651) (-5.1929 (-1.818824) (-0.2257) (-5.2092) (-2.177672) LASCF 0.0711*** 0.029272** 0.056081*** 0.0760*** 0.0297** 0.054706***
48
(5.7720) (2.1681) (4.51841) (6.0047) (2.1900) (4.377597) SIZE 0.0198 -0.1383* -0.447373 0.0586 -0.1418* -1.558357**
(0.3147) (-1.6713) (-0.986268) (0.8661) (-1.7100) (-2.520287) EQTL 0.6712*** 0.6379*** 0.530283*** 0.6730*** 0.6375*** 0.500944***
(23.1910) (22.5493) (14.4946) (23.1335) (22.5119) (13.30527) ROA 1.2069*** 1.2961*** 0.407843*** 1.2384*** 1.2924*** 0.385486***
(5.8030) (6.3834) (2.984583) (5.8427) (6.3600) (2.8327***) REG -3.1518*** -2.9419*** -2.498177*** -3.1137*** -2.9346*** -2.407661***
(-5.2363) (-4.9790) (-5.393705) (-5.0779) (-4.9569) (-5.184921) Country Macro
RGDP 0.1146 0.2256 0.036864 (1.0670) (0.8102) (1.457126)
BASE -0.0310* 0.0073 0.414991*** (-1.7260) (0.8504) (2.725836)
Country Fixed Effects No Yes No No Yes No Firm Fixed Effects No No Yes No No Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes
SSE (Sum squared
resid) 17480.5 15618.63 3162.287 17401.21 15604.35 3118.284
Adjusted R-square 0.6589 0.69915 0.917424 0.6593 0.6925 0.918341 Durbin-Watson stat 0.4206 0.465303 2.050998 0.4229 0.4664 2.096835
F-Statistic 184.6956*** 135.1163*** 43.7757*** 154.3757*** 120.0035*** 43.95155*** Number of
Observations 952 952 952 952 952 952
Baseline model results
Without country-binary variables,
Column (1) of Table 7 shows the impact of bank-specific factors on the capital
adequacy ratio, without country fixed effects and firm fixed effects. All the bank
specific variables are positively related to the capital adequacy ratio. However, only
LLR, LASCF, EQTL and ROA show statistically significant to the model. The Wald
test is used to provide a test of whether the bank specific variables collectively made a
significant contribution to explaining the banking institutions’ capital adequacy ratio.
As shown in Table 8, the test statistic is 272.492, statistically significant at better than
the 1% level, signifying that the bank specific variables collectively do make a
significant contribution in explaining banks’ capital adequacy ratio.
Table 8: Wald test for bank specific variables
Wald Test: Equation: Untitled
Test Statistic Value df Probability F-statistic 272.3492 (6, 944) 0.0000
Chi-square 1634.095 6 0.0000
Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(2) 0.184165 0.042715
C(3) 0.045599 0.102000 C(4) 0.068897 0.012129 C(5) 0.019024 0.062950 C(6) 0.671137 0.028912 C(7) 1.219568 0.207361
Restrictions are linear in coefficients.
The coefficient on the regulatory pressure, REG is negatively and statistically
50
significant to the model at 1% significance level. This indicates that high regulatory
requirements may have caused the low capital banks to reduce their capital adequacy
ratio.
With country-binary variables
Column (2) of Table 7 adds country fixed effects to the specifications in column (1).
The omitted country is Thailand and thus the country coefficients are measured
relative to Thailand. The results in column (2) are almost similar with column (1)
except for NIM which now has a negatively and statistically correlated with the
capital adequacy ratio. After adding country fixed effects in the model, SIZE become
statistically and positively correlated with the capital adequacy ratio. The rest of the
variables in column (2) share the results with column (1). In column (2), 4 of the 6
country binary variables are significant at 1% significance level, and all of them are
positively correlated with the capital adequacy ratio. The Wald statistic of 18.3771,
shown in Table 9 indicates that the country binary variables collectively make a
significant contribution in explaining the capital adequacy ratio in column (2).
Table 9: Wald test for country binary variable
Wald Test: Equation: Untitled
Test Statistic Value df Probability F-statistic 18.37708 (6, 938) 0.0000
Chi-square 110.2625 6 0.0000
Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(9) 2.418578 0.964762
51
C(10) 2.158586 0.942708 C(11) 1.628129 1.057190 C(12) 7.654563 0.901008 C(13) 3.102445 0.928294 C(14) 3.785390 0.869888
Restrictions are linear in coefficients.
The coefficient on the regulatory pressure, REG is negatively and statistically
significance at 0.01 level. This indicates that high regulatory requirements may have
caused the low capital banks to reduce their capital adequacy ratio.
With Firm Fixed effects
For comparison purposes, individual firm fixed effects are also included. The results
in column (3) are qualitatively similar to column (2); with all the independent
variables have the same direction of relationship with the target capital level.
Interestingly, now SIZE is not statistically significant related to the target capital level.
Hence, bank size seemly not a determinant of bank capital for the banking institutions.
However, we notice that with firm fixed effects specification, the Adjusted R-square
and also Durbin-Watson stat improve from 0.6992 to 0.9174 and 0.4653 to 2.051
Model results with country macroeconomic variables
Column (4) excludes country binary variables and includes two country
macroeconomic variables, the real GDP growth and the ratio of aggregate bank assets
to nominal GDP. The coefficient of the BASE is negative and significant at 10%
significant level. The RGDP is not statistically significant related to the capital
adequacy ratio. The Wald test statistic is 1.5995, not statistically even at 10%
significance level, shown in Table 10. The result implies that the capital adequacy
ratio is not affected by the two selected country macroeconomic variables. Moreover,
the adjusted R-squared statistics declined. This suggests tat the country effects picked
up in column (2) are more than just these two macroeconomic variables.
Table 10: Wald test on country macroeconomic variables
Wald Test: Equation: Untitled
Test Statistic Value df Probability F-statistic 1.599517 (2, 942) 0.2025
Chi-square 3.199034 2 0.2020
Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(9) 0.114586 0.107395
C(10) -0.030973 0.017945 Restrictions are linear in coefficients.
53
Column (5) and (6) present Model results with country macroeconomic variables with
added country fixed effects specification and firm fixed effects specification
respectively. These two model provide similar results with column (4) and both the
R-squared and Durbin Watson statistic are improved.
4.4 SUMMARY AND DISCUSSION OF THE FINDINGS
Only results of column (3) which the model included the firm fixed effects and period
fixed effects specification are discussed in this hypotheses testing section because the
model provides the best results. Apart from this, column (3) is selected due to the
study do not specifies the type of financial institutions and the ownership structure of
the financial institutions. Therefore it is important to include firm fixed effects.
Hypotheses testing
H1: Credit risk has no statistically significant impact on banks’ target capital
ratio
High amount of loan loss reserve is commonly signifying a high risk because the
bank expects the loans will default. This also implies that the worse the financial
health of the bank, the higher is the bank’s target capital ratio. The coefficient of
variable LLR is statistically significant at the 0.01 level, and has a positive sign. This
explains that banks increase capital when increasing credit risk and vice versa in
order to maintain their capital buffer. A positive effect also signals that banks
voluntarily increase their capital to a greater extent in order to overcome their bad
financial situation. This is consistent with the evidence from the US banking sector by
Shrieves and Dahl (1992), Jacques and Nigro (1997) and Aggarwal and Jaques (1998)
54
as well as by Rime (2001) from Switzerland seems to confirm this positive
relationship.
H2: Management Quality has no statistically significant impact on banks’ target
capital ratio
The management quality is proxied by the NIM shows a negative impact on target
capital ratio, significance at 0.1 level. The coefficient of NIM shows that a one unit
increase in net interest margin decreases the bank capital by 0.0561 unit according
column (3). This is inconsistent with Angbazo, L. (1997) which predicts a positive
relationship between bank management quality and bank capital.
H3: Bank Liquidity has no statistically significant impact on banks’ target
capital ratio
The coefficient of variable LACSF has positive sign and reject null hypothesis at 1%
significance level. This is consistent with the findings of Volker & Martin (2008) for
German Banks. Liquidity is significantly positively related to capital ratio also
implies that banks do not treat liquidity as a substitute for capital for self-insurance
purposes (Yu, 2000).
H4: Bank Size has no statistically significant impact on banks’ target capital
ratio
Table 6 shows SIZE has a negative relationship with capital ratio. However, it is
insignificant; bank size appears not a determinant of bank capital in the East Asia
region. This is inconsistent with the studies in developed countries (Shrieves and
Dhal, 1992; Ediz et al., 1998; Jacques and Nigro, 1998; and Rime, 2001) as well as in
Taiwan (Yu, 2008). On the other hand, this result is in line with our main reference
55
(Ahmad, R. , Ariff, & Michael, 2008).
H5: Bank Leverage has no statistically significant impact on banks’ target
capital ratio
EQTL has a positive coefficient and statistically significant at 0.01 level. This means
the high leverage bank which has a low EQTL will hold less equity capital. It is
consistent with our initial priori because high leveraged bank may find raising new
equity difficult and thus hold less equity than low leveraged banks. This also show
consistent with main reference (Ahmad, R. , Ariff, & Michael, 2008)
H6: Bank Profitability has no statistically significant impact on banks’ target
capital ratio
Examining the relationship between bank profitability and bank capital, column (3)
suggests that earnings is statistically and positively influence the banks’ target capital
level. The possible explanation to the result is that the bank managements reduce the
capital cushioning according to the profitability level (Yu, 2000).
H7: Regulatory pressure has no statistically significant impact on target capital
ratio
Column (3) provides us the relationship between regulatory pressure and bank capital
is negatively and statistically significant at 0.01 level. This is consistent with Jacques
and Nigro (1997) also Ahmad, R., Ariff, & Michael (2008) which means that low
capitalized banks may reduce their capital ratio due to the high capital regulatory
requirements.
Summary of Hypotheses Testing
Variables Sign Reject Null Hypothesis Significance Level LLR + Yes 0.01 NIM - Yes 0.10 LASCF + Yes 0.01 SIZE - No Nil EQTL + Yes 0.01 ROA + Yes 0.01 REG - Yes 0.01
4.5 ROBUSTNESS CHECK
We checked the robustness of the results along three additional dimensions. First, we
run the regressions separately for Japanese banks and non-Japanese banks. It is
because the test sample consists of large numbers of Japanese Banks. The results are
shown in Table 11. Secondly, we run the regressions according to the size of the bank,
shown in Table 12 and finally we run the regressions separately for each of the
country, shown in Table 13.
4.5.1 ROBUSTNESS CHECK – JAPANESE BANK EXCLUDED
Total Japanese bank included in the research sample are 109 banks, therefore, it is
interesting to analyse whether the results shown earlier in the full model are largely
influenced by the Japanese banks. To further analyse the robustness of the model, we
compare the results in Table 7 with Table 11. We notice that even we omitted Japanese
Banks; the model still provides us similar results. Referring to Column (3), LLR is
statistically significant and positively related to capital ratio for both tables. NIM is
negatively related to capital ratio significance at 0.1 level for both original model and
robust check model. For LASCF, EQTL, and ROA, all of them are having the same
positive relationship and statistically significant. Similarly, both model show SIZE is
57
insignificant. The robustness model shows no significant difference with the proposed
research model indicates the proposed model is a reliable model. The result is not
largely influence by the Japanese Banks as the sample in this study consists of almost
50 per cent of Japanese Banks.
Table 11: Determinants of capital ratio (Robustness Check, without Japanese Banks)
The dependent variable is the risk-weighted capital adequacy ratio (CAR). The explanatory variables include six bank specific variables (LLR,
NIM, LASCF, SIZE, EQTL, and ROA), one dummy variable representing regulatory pressure (REG), and 2 country macro variable (BASE and
RGDP). LLR refers to the loan loss reserves to gross loan ratios. NIM and LASCF are the net interest margin and the ratio of total liquid assets
to total deposits respectively. SIZE represents the natural logarithm of total assets. EQTL is the ratio of total equity to total liabilities; ROA is the
return on average assets. The dummy variable REG refers to regulatory pressure, denoted by 1 for low capitalized banks and zero otherwise.
Two country macro are BASE represents the financial systems of the country and also RGDP refers the real gross domestic products growth of
the country. Total number of observations is 952. Reported in parentheses are robust standard errors. * indicates significance at the 10% level, **
indicates significance at the 5% level, *** indicates significance at the 1% level.
Variables CAR (1) (2) (3) (4) (5) (6)
Constant 4.6840*** 5.9889*** 14.9764* 4.8332*** 3.5536 14.8002* (4.0180) (4.8071) (1.9426) (3.1117) (1.1237) (1.9314)
Bank Specific LLR 0.1706*** 0.2092*** 0.17456** 0.1768*** 0.2057*** 0.1358*
(2.9379) (3.3664) (2.3950) (3.0221) (3.2960) (1.8510) NIM -0.0038 -0.7100*** -0.4017* -0.0931 -0.7102*** -0.4615**
(-0.0263) (-3.9438) (-1.7389) (-0.5592) (-3.9386) (-2.005) LASCF 0.0705*** 0.0217 0.0669*** 0.0745*** 0.0224 0.0676***
59
(4.0593) (1.1362) (3.5664) (4.2147) (1.1706) (3.6385) SIZE 0.0174 -0.2218* -0.6700 0.0680 -0.2229* -1.5295
(0.1990) (-1.7961) (-0.7531) (0.7040) (-1.8022) (-1.5771) EQTL 0.6935*** 0.6586*** 0.5066*** 0.6969*** 0.6587*** 0.4780***
(17.0821) (16.58985) (9.9037) (16.9219) (16.5683) (9.2936) ROA 1.3928*** 1.5635*** 0.5412** 1.4061*** 1.5391*** 0.4524*
(4.1165) (4.8589) (2.3133) (4.1470) (4.7574) (1.9393) REG -4.3353*** -4.5229*** -5.5648*** -4.4823*** -4.3233*** -4.6753***
(-3.5902) (-3.6775) (-5.4023) (-3.5004) (-3.4144) (-4.3244) Country Macro
RGDP 0.1414 0.2860 0.5893*** (0.8276) (0.7024) (2.6787)
BANK 0.0308 0.0410 0.0944** (-1.2338) (0.5396) (2.1539)
Country Fixed Effects No Yes No No Yes No Firm Fixed Effects No No Yes No No Yes Year Fixed Effects Yes Yes Yes Yes Yes Yes
SSE (Sum squared resid) 15623.07 13875.87 2893.53 15575.40 13856.28 2818.84
Adjusted R-square 0.6114 0.6514 0.9036 0.6111 0.6505 0.9056 Durbin-Watson stat 0.4312 0.4857 2.0883 0.4341 0.4829 2.1190
F-Statistic 82.0386*** 65.1666*** 35.9807*** 68.3140*** 57.3923*** 36.28423***
Number of Observations 516 516 516 516 516 516
4.5.2 ROBUSTNESS CHECK – ANALYSIS BASED ON BANK SIZE
This study continues examines the robustness of the model by analysing the factors
which influence the bank capital ratio for different bank size. The 238 banks are
divided into three categories according to its size which is the natural logarithm of
total assets. 25 percent of the banks which have the largest figure of log of total assets
are consider large banks, 25 percent of the banks which have the smallest figure of log
of total assets are consider small banks. The remaining banks are categorised as
medium banks.
The results are shown in Table 12 and only the impact of bank-specific factors and
regulatory pressure on the capital adequacy ratio were examined. For the large banks,
only EQTL and REG show significant impact on capital adequacy ratio. EQTL is
positively related to capital adequacy ratio and REG is negatively related to capital
adequacy ratio; both variable significant at 0.01 level.
Next, for the medium banks, LLR, LASCF, EQTL, ROA and REG give significant
influence on capital adequacy ratio. LLR, LASCF, EQTL and ROA are positively
significant to the capital adequacy ratio at 1% significance level. Likewise, REG also
show a negative relationship with capital adequacy ratio at 1% significance level.
As for the small banks, LASCF, EQTL and ROA are positively related to capital
adequacy ratio; significant at 0.01, 0.01 and 0.10 level respectively. Regulatory
pressure proxied by REG variable shows significant negative relationship with capital
adequacy ratio. This is consistent with the results of the large and medium banks.
61
The regression results report that the relationship between the bank specific and
regulatory pressure variables and the dependent variable for each the three categories
of banks. After separating the bank sample into three categories, the direction of the
relationship between the bank specific variables and the capital adequacy ratio remain
the same. Especially EQTL and REG are significant for all the three categories. This
suggest that for all bank size, high leverage bank which has a low EQTL will hold less
equity capital. The results also consistent with our main reference where the high
regulatory requirements may cause the low capitalised banks reduce their capital ratio.
The consistent results obtained from all three categories of banks further justify the
reason of bank size appears not a determinant of bank capital in East Asia region.
Table 12: Determinants of capital ratio according to Bank Size
The dependent variable is the risk-weighted capital adequacy ratio (CAR). The non dummy explanatory variables include six bank specific
variables (LLR, NIM, LASCF, SIZE, EQTL, and ROA). LLR refers to the loan loss reserves to gross loan ratios. NIM and LASCF are the net
interest margin and the ratio of total liquid assets to total deposits respectively. SIZE represents the natural logarithm of total assets. EQTL is the
ratio of total equity to total liabilities; ROA is the return on average assets. Total number of observations is 952. Reported in parentheses are
robust standard errors. * indicates significance at the 10% level, ** indicates significance at the 5% level, *** indicates significance at the 1%
level.
Variables CAR
Large Medium Small Constant 4.5244 14.0378*** 13.4531
(1.0698) (2.6813) (-1.3274) Bank Specific
LLR 0.0915 0.5047*** 0.1188 (0.9064) (4.0353) (-1.2663)
NIM 0.2328 -0.4369 -0.3550 (0.9148) (-1.2765) (-1.1333)
LASCF 0.0026 0.0416*** 0.0762*** (0.1755) (2.7594) (-2.8633)
SIZE 0.1360 -0.8766 -0.3295 (0.3569) (-1.5643) (-0.2617)
63
EQTL 0.7964*** 0.6447*** 0.4457*** (10.7545) (11.0942) (-6.3571)
ROA -0.1971 0.6104*** 0.6009* (-1.7974) (3.0063) (-1.9155)
REG -2.2714*** -1.4699*** -5.2541*** (-5.1894) (-3.4973) (-3.7488)
SSE (Sum squared resid) 137.7592 527.0585 2278.7680
Adjusted R-square 0.9091 0.8644 0.9086 Durbin-Watson stat 1.7339 1.5504 2.2011
F-Statistic 36.3643*** 24.2530*** 36.1725***
Number of Observations 284 384 284
4.5.3 ROBUSTNESS CHECK – ANALYSIS BASED ON COUNTRY
Finally, we run the regression separately for each country. The final sample of study
consists of 238 banks, with 109 Japanese Banks, 26 Chinese Banks, 18 South Korean
Banks, 32 Indonesian Banks, 28 Malaysian Banks, 13 Philippines Banks and 12
Thailand Banks. It is interesting to investigate the relationship between the
independent variables and the dependent variables for each of the country.
The results are shown in Table 13 and only the impacts of bank-specific factors on the
capital adequacy ratio were examined. Next we discussed the significant of each
variable for the studied countries. LLR has significant relationship with capital ratio
for the samples in China and Malaysia. NIM is significant related to capital ratio for
the samples in Indonesia only. LASCF is positively related to capital ratio for the
samples in Japan, Malaysia and Thailand. SIZE is significant related to capital ratio
for the samples in Korea and Philippines only at 0.10 level. EQTL is significant
related to capital ratio for the samples in China, Malaysia, Philippines and Thailand.
Finally, samples in Japan, Korea, Indonesia, Malaysia and Philippines show
significant positive relationship between ROA and capital adequacy ratio.
Table 13: Determinants of capital ratio according to country
The dependent variable is the risk-weighted capital adequacy ratio (CAR). The non dummy explanatory variables include six bank
specific variables (LLR, NIM, LASCF, SIZE, EQTL, and ROA). LLR refers to the loan loss reserves to gross loan ratios. NIM and
LASCF are the net interest margin and the ratio of total liquid assets to total deposits respectively. SIZE represents the natural logarithm
of total assets. EQTL is the ratio of total equity to total liabilities; ROA is the return on average assets. Total number of observations is
952. Reported in parentheses are robust standard errors. * indicates significance at the 10% level, ** indicates significance at the 5%
level, *** indicates significance at the 1% level.
Variables CAR
China Japan Korea Indonesia Malaysia Philippines Thailand Constant 5.7311 -6.4461 40.3942** 33.6156 -11.9019 53.6260* 14.7038
(0.3178) (-0.6938) (2.5941) (-1.6460) (-0.5763) (2.0298) (1.0152) Bank Specific
LLR 0.9219** -0.0993 -0.2423 0.1875 -0.3070** 0.0978 -0.1928 (2.1604) (-1.3894) (-0.5113) (0.9665) (-2.0366) (0.6278) (-1.1482)
NIM 0.8124 0.5755 -0.2598 -0.8834** -0.5991 1.0896 0.3227 (1.0886) (1.1997) (-0.7940) (-2.3608) (-0.4268) (0.8754) (0.8430)
LASCF -0.0049 0.0330*** 0.0079 0.0699* 0.1268*** 0.0260 0.0735* (-0.1121) (3.3810) (0.3924) (1.8483) (3.0631) (0.3631) (1.9964)
SIZE -0.7094 1.4899 -2.6658* -2.6726 2.8248 -6.3075* -1.7006 (-0.3937) (1.6221) (-1.9278) (-0.9808) (1.3343) (-1.9820) (-1.1726)
EQTL 1.3010*** 0.0545 0.0859 0.6240 0.2553*** 0.5950** 1.1502*** (13.8145) (0.8984) (1.3638) (4.5631) (2.7440) (2.3888) (9.1390)
66
ROA 0.2916 0.3621*** 0.6494* 0.6132*** 0.9665*** 2.3713* -0.5877 (0.3036) (5.8408) (1.8466) (1.0179) (2.8299) (1.8284) (-0.7981)
SSE (Sum squared resid) 175.0560 113.0730 20.1591 813.6493 684.0576 259.3583 36.4334
Adjusted R-square 0.8861 0.8874 0.8324 0.9331 0.8835 0.7383 0.9053 Durbin-Watson stat 1.8832 2.1358 2.2692 2.2423 2.0467 2.4902 2.3488
F-Statistic 24.5594*** 30.2872*** 14.5645*** 45.2575*** 24.3720*** 7.8528*** 23.4748*** Number of
Observations 104 436 72 128 112 52 48
4.6 CHAPTER SUMMARY
This chapter first presents the descriptive statistics and the pair-wise correlation
matrix of the regression variables respectively. Test of multicollinearity is conducted
to avoid variables redundancy. The VIF for all the independent variables is less than 5
indicate that there is no multicollinearity problem. Next, the analysis of variance
shows there is a strong relationship between the explanatory variables (EQTL,
LASCF, LLR, NIM, SIZE, ROA) and the dependent variable (CAR).
Firm fixed effect model is a better choice to run because Glesjer test reject the
unobserved individual heterogeneity is uncorrelated with the independent variables.
We test the seven hypothesis and the statistical results reject hypothesis 1, 2, 3, 5, 6,
and 7. Hypothesis 4 which propose that bank size has no statistically significant
impact on banks’ target capital ratio is not rejected.
Before making conclusion of this study, we check the robustness of the results along
three more additional dimensions which separate the sample into first, Japanese banks
and non-Japanese banks; the size of the banks and the geographic location of the
banks. After omitting Japanese Banks the model still provides us similar results with
our baseline model. We can suggest that the model is a reliable model because the
result is not largely influence by the Japanese Banks even the sample in this study
consists of almost 50 per cent of Japanese Banks.
The important result that can draw from the second robustness check is that the
similar results for each size category rationalise the insignificance of size factor in our
68
baseline model. Robustness checks according to countries do not provide us much
information about the model reliability because only few variables are significant in
each of the model. However, we can still notice the relationship between the
independent variables and dependent variable which are at least significant at 10 %
significance level are in the same direction of relationship with our baseline model.
69
CHAPTER 5: CONCLUSION AND RECOMMENDATION
This chapter presents an overview of the study and a summary of the major findings
as well as its interpretation of the major findings. Limitations of this study are also
stated in this chapter. Finally, recommendations for future research are also discussed.
5.0 OVERVIEW OF THE STUDY
The main objective of the study is to find the determinants of bank capital ratios in
East Asia in 2004-2007. The regression results shows that LLR, NIM, LASCF, EQTL,
ROA and REG are significantly influence the decision of determining the capital
structure of banks. In this model, SIZE is insignificant. However, the negative
relationship between SIZE and capital provides economic meaning because it is
reasonable for large banks hold lesser capital.
The results of this study are consistent with the main reference, suggesting that the
banks in East Asia behave similarly. This is also further proved by the Wald test where
the country macro variables (real GDP growth and financial system) do not contribute
to the target capital level.
Although the major proportions of banks in our sample are taken from Japan, the
results are not differing after taking out Japanese Bank as shown in Table 11. The
robustness check ensures the research model is appropriate and reliable. Furthermore,
the second robustness checks on size categories consistent with the baseline model
where the size effect appears not a determinant of capital ratio in East Asia. The
regression result of each country is overall not much different with our baseline model
in terms of those selected significant variables.
70
5.1 INTERPRETATION OF MAJOR FINIDNGS
The relationship between bank capital and risk taking is positive. This suggests with
increase in bank capital, the higher tendency of risk taking behaviour. We may
conclude that the capital decisions are related to banks’ risk taking behaviours
signalling banks voluntarily increase their capital in order to maintain their capital
buffer.
Management quality and banks profitability provides insights of banks with high
earnings may reduce the amount of excess capital. Leverage factor proxied by total
equity to total liabilities, shows a positive relationship because the risk premium for
high leverages bank is higher than the low leveraged banks. So in general low
leverage bank (high EQTL) may have a higher capital since they can issue new shares
easier compare to high leverage bank. The liquidity of the banks also shows positive
relationship which is consistent with the literature.
To examine the impact of the capital regulations pressure, a dummy variable REG is
included in the model. The negative sign of the coefficient REG indicates that low
capitalized banks responded according to the intention of the regulator by reducing
their capital ratio.
As stated in Chapter 1, this study extends the regression model proposed by Ahmad.
R,, Ariff, & Michael, (2008) which completed in 2004. The significance of this
current study focuses on the subsequent period which is from 2004-2007, and
moreover the results are similar with same direction of relationship between the target
capital level and selected determinants. Furthermore, inclusive of additional six East
71
Asia countries, the relationships do not alter.
5.2 LIMITATIONS OF THE STUDY
The study, however, has its limitations. First, the study only covers 4 years, the period
of 2004-2007. Since this study employs balanced panel data method, some of the
banks which do not have complete set of data during the studied period were dropped
out from the sample. For instance, Singapore is not included in the study due to
unavailability of data in the bankscope database.
Second, this study also do not separate locally incorporated foreign banks and
domestic banks. Therefore, it is interesting to see whether foreign banks and domestic
banks share the same principals in setting their banks’ capital ratio.
5.4 RECOMMENDATIONS FOR FUTURE RESEARCH
Future studies may focus on the following criteria. First as mention in the limitation
section, the future may specify the type of banks and ownership structure of the banks
in order to investigate whether type and ownership structure influence the decision of
setting capital ratio. Second, future researchers may also interest to study the
effectiveness of the capital regulations especially in determining the minimum amount
of bank capital in East Asia region.
72
5.5 CHAPTER SUMMARY
In conclusion, based on the regression results, credit risk, bank liquidity, bank
leverage and bank profitability show a significant positive impact on target capital
ratio. Management quality and regulatory pressure show a significant negative impact
on target capital ratio. All the sign of the coefficients of each independent variable is
exactly the same with our main references.
The limitations of this study are basically the data availability and model estimation
without separating different type of banking institutions. Therefore, the future studies
may perhaps focus on investigate the determinants of capital ratio for different types
of banking institutions. Perhaps the effectiveness of capital regulation for East Asian
banks can also be included in the future studies.
73
BIBLIOGRAPHY
Aggarwal, R. and K.T. Jacques, 2001. The impact of FDICIA and prompt corrective action on bank capital and risk: Estimates using a simultaneous equations model. Journal of Banking and Finance 25: 1139-1160.
Ahmed, A. S., C. Takeda, and S. Thomas, 1999. Bank loan loss provisions: A re-examination of capital management, earnings management and signalling effects. Journal of Accounting and Economics 28, 1-25.
Angbazo, L. 1997. Commercial bank net interest margins, default risk, interest-rate risk, and off-balance sheet banking. Journal of Banking & Finance, 21(1), 55-87.
Basle Committee on Banking Supervision, 1988. International convergence of capital measurement and capital standards, Bank for International Settlements (BIS).
Berger, A.N., Herring, R.J., 1995. The role of capital in financial institutions. Journal of Banking and Finance 19, 393-430. Blum,J., 1999, Do capital adequacy requirement reduce risks in banking? Journal of Banking and Finance 23, 775-771. Brana, S., & Lahet, D. 2009. Capital requirement and financial crisis: The case of
Japan and the 1997 Asian crisis. Japan and the World Economy, 21(1), 97-104. Brewer, Elijah, Kaufman, George G. and Wall, Larry D., 2008. Bank Capital Ratios
Across Countries: Why Do They Vary? Paolo Baffi Centre Research Paper No. 2008-28. Available at SSRN: http://ssrn.com/abstract=1264914
Chen, P. and L. Daley, 1996, Regulatory capital, tax and earnings management effects on loan loss accruals in the Canadian banking industry, Contemporary Accounting Research 13, 91-128.
Collins, J. H., D. A. Shackelford, and J. M Wahlen, 1995. Bank differences in the coordination of regulatory capital, earnings, and taxes, Journal of Accounting Research 33, 263-291.
De Bondt, G.J., Prast, H.M., 2000. Bank capital ratios in the 1990s: Cross-country evidence." BNL Quarterly Review 212, 72-97. Demirgüç-Kunt, Asli and Enrica Detragiache. 2002. Does Deposit Insurance Increase Banking System Stability? An Empirical Investigation. Journal of Monetary
Economics, 49 (October): 1373-1406 Demstez, R.S., Saidenberg, M.R., Strahan, P. E., 1996. Banks with something to lose:
The disciplinary role of franchise value. Federal Reserve Bank of New York Economic Policy Review 2, 1-14.
Ediz, T., I. Michael, and W. Perraudin, 1998. The impact of capital requirements on U.K. bank behaviour. Federal Reserve Bank of New York Economic Policy Review, October 1998.
Fischer, K.P., Jean-Pierre Gueyie and Edgar Ortiz, "Risk-Taking and Charter Value of Commercial Banks' from the NAFTA Countries". The International Journal of Finance, Vol 13, No.1, 2001: 2027-2043
Furlong, F., Keeley, M., 1989. Capital regulation and bank risk-taking: a note. Journal of Banking and Finance 13, 883-891.
Garcıa-Herrero, A., S. Gavila, and D.Santabarbara, 2006. China’s Banking Reform: An Assessment of its Evolution and Possible Impact. CESifo Economic Studies, Vol. 52, 2/2006, 304–363. Ghoshi, S., Nachane, D.M., Sahoo, S., 2003. Capital requirements and bank behaviour: An empirical analysis of Indian public sector banks. Journal of International Development 15, 145-156.
74
Gropp. Reint, and Florian Heider, 2007, What can corporate finance say about banks’ capital structures? Working paper, SSRN. Gujarati, Damodar. 2003. Basic Econometrics (4th ed.). McGraw-Hill. Guttentag J., and Herring, R., "The insolvency of financial institutions: Assessment and regulatory disposition", In Crises in the economic and financial structure, edited by Paul Wachtel, Lexington: Lexington Books, (1983); 99-126. Heid, Frank & Porath, Daniel & Stolz, Stephanie, 2004. Does capital regulation matter for bank behaviour? Evidence for German savings banks, Discussion Paper Series 2: Banking and Financial Studies 2004,03, Deutsche Bundesbank, Research Centre. Hovakimian, A., Kane, E.J., 2000. Effectiveness of capital regulation at US commercial banks, 1985-1994. The Journal of Finance 55, 451-469. Hovakimian, A., Kane, E. J., Laeven, L., 2003. How country and safety-net characteristics affect bank risk-shifting? Journal of Financial Services Research 23, 177-204. Hsiao, C., 1986. Analysis of panel data. Cambridge: Cambridge University Press. Ito, T., & Sasaki, Y. N., 2002. Impacts of the Basle Capital Standard on Japanese
Banks' Behavior. Journal of the Japanese and International Economies, 16(3), 372-397.
Jacques, K., Nigro, P., 1997. Risk-based capital, portfolio risk, and bank capital: A simultaneous equations approach. Journal of Economics and Business 49, 533-547. Jackson, P., W. Perraudin, V. Sapporta, 2002. Regulatory and "economic" solvency standards for internationally active banks. Journal of Banking and Finance 26, 953-976 Jeitschko, Thomas D. & Jeung, Shin Dong, 2005. "Incentives for risk-taking in banking - A unified approach," Journal of Banking & Finance, Elsevier, vol. 29(3), pages 759-777, March Thomas D Jeitschko & Shin Dong Jeung, 2008. "The Effect of Capitalization on Bank Risk in the Presence of Regulatory and Managerial Moral Hazards," Journal of Bank Management, Icfai Press, vol. 0(2), pages 7-31, May. Jin, C. (2003). Capital adequacy of Chinese banks: Evaluation and enhancement.
Journal of International Banking Regulation, 4(4), 320. Kentaro Iwatsubo, 2007, Bank Capital Shocks and Portfolio Risk: Evidence from
Japan. Japan and the World Economy, vol. 19, issue 2, pages 166-186 Kim, D, Santomero, A.M., 1988. Risk in banking and capital regulation. The Journal
of Finance 43, 1235-1244. Koehn, M., Santomero, A.M., 1980. Regulation of bank capital and portfolio risk. The
Journal of Finance 35, 1235-1244. Konishi, M., Yasuda, Y. 2004. Factors affecting bank risk taking: Evidence from
Japan. Journal of Banking and Finance 28, 215-232 Marcus, A. J., 1984: Deregulation and Bank Financial Policy. Journal of Banking and Finance 8: 557-565. Milne, A., 2002. Bank capital regulation as an incentive mechanism: Implications for
portfolio choice. Journal of Banking and Finance 26, 1-23. Milne, A. and E. Whalley, 2001. Bank capital regulation and incentives for risk-taking.
Mimeo. Moyer, S. E., 1990, Capital adequacy ratio regulations and accounting choices in
commercial banks, Journal of Accounting and Economics 13, 123-154.
75
Rime, B., 2001. Capital requirements and bank behaviour: Empirical evidence for Switzerland. Journal of Banking and Finance 25: 789-805.
Rochet, J.-C., 1992. Capital requirements and the behaviour of commercial banks. European Economic Review 36: 1137-1178.
Ronald C. Anderson and Donald R. Fraser, 2000, Corporate control, bank risk taking, and the health of the banking industry , Journal of Banking & Finance, Volume 24, Issue 8, pages 1383-1398
Rubi, A., Ariff, M., & Michael, J. S., 2008. The Determinants of Bank Capital Ratios in a Developing Economy. Asia - Pacific Financial Markets, 15(3-4), 255.
Saunders, A., Strock, E., Travlos, N. G., 1990. Ownership structure, deregulation and bank risk-taking. The Journal of Finance 45, 643-654.
Saunders, A., Wilson B, 2001. An analysis of bank charter balue and its risk constraining incentives. Journal of Financial Services Research 19, 185-195 Schaeck, Klaus and Čihák, Martin., 2007 “Banking Competition and Capital Ratios.” (September) IMF Working Paper No. 07/216. Shrieves, R., Dahl, D., 1992. The relationship between risk and capital in commercial banks. Journal of Banking and Finance 16, 439-457. Song, I. 1998. Korean banks’ responses to the strengthening of capital adequacy
requirements. Federeal Reserve Bank of San Francisco Working Paper (PB98-01)
VanHoose, David, 2007. "Theories of bank behavior under capital regulation," Journal of Banking & Finance, Elsevier, vol. 31(12), pages 3680-3697
Volker, K., & Martin, W., 2008. How Do Banks Determine Capital? Evidence from Germany. German Economic Review, 9(3), 354.
Freixas, Xavier & Rochet, Jean-Charles, 1998. "Fair pricing of deposit insurance. Is it possible? Yes. Is it desirable? No," Research in Economics, Elsevier, vol. 52(3), pages 217-232, September.
Yu, H.-C. ,2000. Banks' Capital Structure and the Liquid Asset - Policy Implication of Taiwan. Pacific Economic Review, 5(1), 109.
76
APPENDICES
A) OLS results in EVIEWS
Bank Specific Variable only Table 14: Bank Specific Variable only - Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 04/16/09 Time: 01:22 Sample: 2004 2007 Cross-sections included: 238 Total panel (balanced) observations: 952
Variable Coefficient Std. Error t-Statistic Prob. C 4.840649 0.796409 6.078098 0.0000
LLR 0.177970 0.043145 4.124965 0.0000 NIM 0.047498 0.102118 0.465131 0.6419
LASCF 0.071055 0.012310 5.771979 0.0000 SIZE 0.019832 0.063022 0.314690 0.7531 EQTL 0.671225 0.028943 23.19095 0.0000 ROA 1.206876 0.207975 5.802978 0.0000 REG -3.151803 0.601907 -5.236366 0.0000
Effects Specification Period fixed (dummy variables) R-squared 0.662477 Mean dependent var 13.45799
Adjusted R-squared 0.658890 S.D. dependent var 7.375411 S.E. of regression 4.307581 Akaike info criterion 5.770117 Sum squared resid 17460.50 Schwarz criterion 5.826256 Log likelihood -2735.576 F-statistic 184.6956 Durbin-Watson stat 0.420634 Prob(F-statistic) 0.000000
Table 15: Bank Specific Variable only - Period Fixed and Country Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 04/16/09 Time: 01:17 Sample: 2004 2007 Cross-sections included: 238 Total panel (balanced) observations: 952
Variable Coefficient Std. Error t-Statistic Prob. C 6.268976 0.939146 6.675189 0.0000
LLR 0.196154 0.046778 4.193277 0.0000 NIM -0.686110 0.132124 -5.192915 0.0000
77
LASCF 0.029272 0.013502 2.168074 0.0304 SIZE -0.138298 0.082747 -1.671340 0.0950 EQTL 0.637936 0.028291 22.54933 0.0000 ROA 1.296084 0.203039 6.383436 0.0000 REG -2.941922 0.590864 -4.979014 0.0000 D1 2.418578 0.964762 2.506918 0.0123 D2 2.158586 0.942708 2.289773 0.0223 D3 1.628129 1.057190 1.540053 0.1239 D4 7.654563 0.901008 8.495555 0.0000 D5 3.102445 0.928294 3.342093 0.0009 D6 3.785390 0.869888 4.351584 0.0000
Effects Specification Period fixed (dummy variables) R-squared 0.698081 Mean dependent var 13.45799
Adjusted R-squared 0.692915 S.D. dependent var 7.375411 S.E. of regression 4.087103 Akaike info criterion 5.671246 Sum squared resid 15618.63 Schwarz criterion 5.758006 Log likelihood -2682.513 F-statistic 135.1163 Durbin-Watson stat 0.465303 Prob(F-statistic) 0.000000
Table 16: Bank Specific Variable only - Firm Fixed and Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 03/31/09 Time: 03:23 Sample: 2004 2007 Cross-sections included: 238 Total panel (balanced) observations: 952
Variable Coefficient Std. Error t-Statistic Prob. C 11.98769 4.158977 2.882364 0.0041
LLR 0.177985 0.054297 3.277964 0.0011 NIM -0.312744 0.171948 -1.818824 0.0694
LASCF 0.056081 0.012412 4.518410 0.0000 SIZE -0.447373 0.453602 -0.986268 0.3243 EQTL 0.530283 0.036585 14.49460 0.0000 ROA 0.407843 0.136650 2.984583 0.0029 REG -2.498177 0.463165 -5.393705 0.0000
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.938871 Mean dependent var 13.45799
Adjusted R-squared 0.917424 S.D. dependent var 7.375411 S.E. of regression 2.119407 Akaike info criterion 4.559371
78
Sum squared resid 3162.287 Schwarz criterion 5.825048 Log likelihood -1922.261 F-statistic 43.77570 Durbin-Watson stat 2.050998 Prob(F-statistic) 0.000000
Bank Specific and Country Macro Variables Table 17: Bank Specific and Country Macro Variables - Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 04/16/09 Time: 01:24 Sample: 2004 2007 Cross-sections included: 238 Total panel (balanced) observations: 952
Variable Coefficient Std. Error t-Statistic Prob. C 5.094072 0.841358 6.054587 0.0000
LLR 0.187044 0.043435 4.306272 0.0000 NIM -0.025217 0.111725 -0.225708 0.8215
LASCF 0.076000 0.012657 6.004705 0.0000 SIZE 0.058591 0.067648 0.866120 0.3866 EQTL 0.672968 0.029091 23.13347 0.0000 ROA 1.238445 0.211966 5.842667 0.0000 REG -3.113679 0.613185 -5.077874 0.0000
RGDP 0.114586 0.107395 1.066951 0.2863 BANK -0.030973 0.017945 -1.725963 0.0847
Effects Specification Period fixed (dummy variables) R-squared 0.663623 Mean dependent var 13.45799
Adjusted R-squared 0.659324 S.D. dependent var 7.375411 S.E. of regression 4.304839 Akaike info criterion 5.770918 Sum squared resid 17401.21 Schwarz criterion 5.837264 Log likelihood -2733.957 F-statistic 154.3757 Durbin-Watson stat 0.422869 Prob(F-statistic) 0.000000
Table 18: Bank Specific and Country Macro Variables - Country Fixed and Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 04/16/09 Time: 01:34 Sample: 2004 2007 Cross-sections included: 238 Total panel (balanced) observations: 952
Variable Coefficient Std. Error t-Statistic Prob. C 4.899971 1.755687 2.790914 0.0054
79
LLR 0.194247 0.046884 4.143108 0.0000 NIM -0.690112 0.132479 -5.209234 0.0000
LASCF 0.029659 0.013545 2.189659 0.0288 SIZE -0.141835 0.082949 -1.709906 0.0876 EQTL 0.637531 0.028320 22.51189 0.0000 ROA 1.292435 0.203210 6.360084 0.0000 REG -2.934625 0.592031 -4.956876 0.0000
RGDP 0.225554 0.278396 0.810190 0.4180 BANK 0.007316 0.038786 0.188616 0.8504
D1 1.203831 1.806743 0.666299 0.5054 D2 2.778989 1.307748 2.125018 0.0338 D3 1.860728 1.282839 1.450477 0.1473 D4 7.765472 0.920018 8.440570 0.0000 D5 2.763021 1.697683 1.627524 0.1040 D6 3.842869 0.918013 4.186075 0.0000
Effects Specification Period fixed (dummy variables) R-squared 0.698357 Mean dependent var 13.45799
Adjusted R-squared 0.692538 S.D. dependent var 7.375411 S.E. of regression 4.089611 Akaike info criterion 5.674533 Sum squared resid 15604.35 Schwarz criterion 5.771500 Log likelihood -2682.078 F-statistic 120.0035 Durbin-Watson stat 0.466382 Prob(F-statistic) 0.000000
Table 19: Bank Specific and Country Macro Variables - Firm Fixed and Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 03/31/09 Time: 03:24 Sample: 2004 2007 Cross-sections included: 238 Total panel (balanced) observations: 952
Variable Coefficient Std. Error t-Statistic Prob. C 19.17248 5.025740 3.814857 0.0001
LLR 0.152149 0.054685 2.782292 0.0055 NIM -0.377584 0.173389 -2.177672 0.0298
LASCF 0.054706 0.012497 4.377597 0.0000 SIZE -1.558357 0.618325 -2.520287 0.0119 EQTL 0.500944 0.037650 13.30527 0.0000 ROA 0.385486 0.136084 2.832700 0.0047 REG -2.407661 0.464358 -5.184921 0.0000
BANK 0.036864 0.025299 1.457126 0.1455 RGDP 0.414991 0.152244 2.725836 0.0066
Effects Specification
80
Cross-section fixed (dummy variables) Period fixed (dummy variables)
R-squared 0.939721 Mean dependent var 13.45799
Adjusted R-squared 0.918341 S.D. dependent var 7.375411 S.E. of regression 2.107605 Akaike info criterion 4.549560 Sum squared resid 3118.284 Schwarz criterion 5.825444 Log likelihood -1915.591 F-statistic 43.95155 Durbin-Watson stat 2.096835 Prob(F-statistic) 0.000000
Robustness check (I) – without Japanese Bank Bank Specific Variables only Table 20: Robustness check (I), Bank Specific Variables only, Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 04/17/09 Time: 04:12 Sample: 2004 2007 Cross-sections included: 129 Total panel (balanced) observations: 516
Variable Coefficient Std. Error t-Statistic Prob. C 4.683984 1.165745 4.018019 0.0001
LLR 0.170611 0.058072 2.937931 0.0035 NIM -0.003827 0.145794 -0.026250 0.9791
LASCF 0.070513 0.017371 4.059276 0.0001 SIZE 0.017398 0.087410 0.199034 0.8423 EQTL 0.693456 0.040596 17.08207 0.0000 ROA 1.392797 0.338345 4.116503 0.0000 REG -4.335442 1.207582 -3.590185 0.0004
Effects Specification Period fixed (dummy variables) R-squared 0.618979 Mean dependent var 16.33952
Adjusted R-squared 0.611434 S.D. dependent var 8.922879 S.E. of regression 5.562083 Akaike info criterion 6.290910 Sum squared resid 15623.07 Schwarz criterion 6.381428 Log likelihood -1612.055 F-statistic 82.03855 Durbin-Watson stat 0.431214 Prob(F-statistic) 0.000000
Table 21: Robustness check (I), Bank Specific Variables only, Country Fixed and Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 04/17/09 Time: 04:16 Sample: 2004 2007 Cross-sections included: 129
81
Total panel (balanced) observations: 516 Variable Coefficient Std. Error t-Statistic Prob. C 5.988904 1.245839 4.807127 0.0000
LLR 0.209203 0.062145 3.366352 0.0008 NIM -0.710025 0.180037 -3.943770 0.0001
LASCF 0.021735 0.019130 1.136172 0.2564 SIZE -0.221840 0.123509 -1.796145 0.0731 EQTL 0.658607 0.039699 16.58985 0.0000 ROA 1.563481 0.321779 4.858868 0.0000 REG -4.522919 1.229889 -3.677501 0.0003 D1 3.737307 1.340084 2.788860 0.0055 D2 2.523411 1.469872 1.716755 0.0866 D3 8.098714 1.220410 6.636058 0.0000 D4 3.808960 1.294715 2.941931 0.0034 D5 3.935707 1.135042 3.467456 0.0006
Effects Specification Period fixed (dummy variables) R-squared 0.661590 Mean dependent var 16.33952
Adjusted R-squared 0.651438 S.D. dependent var 8.922879 S.E. of regression 5.267992 Akaike info criterion 6.191692 Sum squared resid 13875.87 Schwarz criterion 6.323355 Log likelihood -1581.457 F-statistic 65.16655 Durbin-Watson stat 0.485719 Prob(F-statistic) 0.000000
Table 22: Robustness check (I), Bank Specific Variables only, Firm Fixed and Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 03/31/09 Time: 03:31 Sample: 2004 2007 Cross-sections included: 129 Total panel (balanced) observations: 516
Variable Coefficient Std. Error t-Statistic Prob. C 14.97642 7.709501 1.942593 0.0528
LLR 0.174536 0.072875 2.395005 0.0171 NIM -0.401657 0.230981 -1.738916 0.0829
LASCF 0.066862 0.018748 3.566353 0.0004 SIZE -0.699954 0.929372 -0.753147 0.4518 EQTL 0.506597 0.051152 9.903668 0.0000 ROA 0.541179 0.233939 2.313336 0.0212 REG -5.564815 1.030079 -5.402319 0.0000
Effects Specification
82
Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.929432 Mean dependent var 16.33952
Adjusted R-squared 0.903600 S.D. dependent var 8.922879 S.E. of regression 2.770403 Akaike info criterion 5.100761 Sum squared resid 2893.525 Schwarz criterion 6.244576 Log likelihood -1176.996 F-statistic 35.98071 Durbin-Watson stat 2.088283 Prob(F-statistic) 0.000000
Bank Specific Variables and Country Macro Variable Table 23: Robustness check (I), Bank Specific Variables and Country Macro Variable, Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 04/17/09 Time: 04:17 Sample: 2004 2007 Cross-sections included: 129 Total panel (balanced) observations: 516
Variable Coefficient Std. Error t-Statistic Prob. C 4.833197 1.553230 3.111707 0.0020
LLR 0.176801 0.058503 3.022106 0.0026 NIM -0.093113 0.166513 -0.559193 0.5763
LASCF 0.074514 0.017680 4.214692 0.0000 SIZE 0.067998 0.096591 0.703976 0.4818 EQTL 0.696891 0.041183 16.92192 0.0000 ROA 1.406129 0.339074 4.146965 0.0000 REG -4.482268 1.280506 -3.500387 0.0005
RGDP 0.141418 0.170881 0.827582 0.4083 BANK -0.030836 0.024992 -1.233826 0.2178
Effects Specification Period fixed (dummy variables) R-squared 0.620141 Mean dependent var 16.33952
Adjusted R-squared 0.611079 S.D. dependent var 8.922879 S.E. of regression 5.564620 Akaike info criterion 6.295606 Sum squared resid 15575.40 Schwarz criterion 6.402581 Log likelihood -1611.266 F-statistic 68.43143 Durbin-Watson stat 0.434131 Prob(F-statistic) 0.000000
Table 24: Robustness check (I), Bank Specific Variables and Country Macro Variable, Country Fixed and Period Fixed Dependent Variable: CAR Method: Panel Least Squares
83
Date: 04/17/09 Time: 04:18 Sample: 2004 2007 Cross-sections included: 129 Total panel (balanced) observations: 516
Variable Coefficient Std. Error t-Statistic Prob. C 3.553628 3.159517 1.124738 0.2612
LLR 0.205655 0.062395 3.295998 0.0011 NIM -0.710179 0.180313 -3.938595 0.0001
LASCF 0.022446 0.019175 1.170636 0.2423 SIZE -0.222891 0.123681 -1.802150 0.0721 EQTL 0.658693 0.039756 16.56825 0.0000 ROA 1.539072 0.323513 4.757377 0.0000 REG -4.323268 1.266180 -3.414418 0.0007
RGDP 0.286014 0.407196 0.702400 0.4828 BANK 0.041044 0.076068 0.539574 0.5897
D1 0.979555 3.739247 0.261966 0.7935 D2 2.352348 1.845268 1.274800 0.2030 D3 8.119179 1.251044 6.489922 0.0000 D4 2.153715 3.180446 0.677174 0.4986 D5 3.804614 1.231374 3.089730 0.0021
Effects Specification Period fixed (dummy variables) R-squared 0.662068 Mean dependent var 16.33952
Adjusted R-squared 0.650532 S.D. dependent var 8.922879 S.E. of regression 5.274833 Akaike info criterion 6.198032 Sum squared resid 13856.28 Schwarz criterion 6.346152 Log likelihood -1581.092 F-statistic 57.39226 Durbin-Watson stat 0.482927 Prob(F-statistic) 0.000000
Table 25: Robustness check (I), Bank Specific Variables and Country Macro Variable, Firm Fixed and Period Fixed Dependent Variable: CAR Method: Panel Least Squares Date: 03/31/09 Time: 03:32 Sample: 2004 2007 Cross-sections included: 129 Total panel (balanced) observations: 516
Variable Coefficient Std. Error t-Statistic Prob. C 14.80017 7.662834 1.931423 0.0542
LLR 0.135787 0.073360 1.850964 0.0650 NIM -0.461503 0.230206 -2.004740 0.0457
LASCF 0.067568 0.018570 3.638466 0.0003 SIZE -1.529485 0.969814 -1.577092 0.1156
84
EQTL 0.477996 0.051433 9.293629 0.0000 ROA 0.452428 0.233289 1.939341 0.0532 REG -4.675334 1.081143 -4.324437 0.0000
RGDP 0.589282 0.219987 2.678712 0.0077 BANK 0.094382 0.043820 2.153879 0.0319
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.931253 Mean dependent var 16.33952
Adjusted R-squared 0.905588 S.D. dependent var 8.922879 S.E. of regression 2.741697 Akaike info criterion 5.082362 Sum squared resid 2818.839 Schwarz criterion 6.242636 Log likelihood -1170.249 F-statistic 36.28423 Durbin-Watson stat 2.119026 Prob(F-statistic) 0.000000
Regression Results based on Size Table 26: Large Bank Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 01:12 Sample: 2004 2007 Cross-sections included: 71 Total panel (balanced) observations: 284
Variable Coefficient Std. Error t-Statistic Prob. C 4.524379 4.229109 1.069818 0.2860
LLR 0.091463 0.100906 0.906418 0.3658 NIM 0.232802 0.254494 0.914765 0.3614
LASCF 0.002633 0.015001 0.175505 0.8609 SIZE 0.135970 0.380993 0.356885 0.7215 EQTL 0.796362 0.074049 10.75450 0.0000 ROA -0.197102 0.109662 -1.797366 0.0738 REG -2.271375 0.437695 -5.189405 0.0000
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.934772 Mean dependent var 11.42866
Adjusted R-squared 0.909066 S.D. dependent var 2.731801 S.E. of regression 0.823782 Akaike info criterion 2.684833 Sum squared resid 137.7592 Schwarz criterion 3.725561 Log likelihood -300.2462 F-statistic 36.36431 Durbin-Watson stat 1.733927 Prob(F-statistic) 0.000000
85
Table 27: Medium Bank Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 01:36 Sample: 2004 2007 Cross-sections included: 96 Total panel (balanced) observations: 384
Variable Coefficient Std. Error t-Statistic Prob. C 14.03767 5.235368 2.681314 0.0078
LLR 0.504685 0.125066 4.035337 0.0001 NIM -0.436879 0.342256 -1.276468 0.2029
LASCF 0.041560 0.015061 2.759422 0.0062 SIZE -0.876640 0.560399 -1.564315 0.1189 EQTL 0.644667 0.058109 11.09417 0.0000 ROA 0.610400 0.203040 3.006311 0.0029 REG -1.469866 0.420286 -3.497299 0.0005
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.901578 Mean dependent var 11.37560
Adjusted R-squared 0.864404 S.D. dependent var 3.739241 S.E. of regression 1.376915 Akaike info criterion 3.706629 Sum squared resid 527.0585 Schwarz criterion 4.797171 Log likelihood -605.6728 F-statistic 24.25301 Durbin-Watson stat 1.550385 Prob(F-statistic) 0.000000
Table 28: Small Bank Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 01:20 Sample: 2004 2007 Cross-sections included: 71 Total panel (balanced) observations: 284
Variable Coefficient Std. Error t-Statistic Prob. C 13.45310 10.13511 1.327376 0.1859
LLR 0.118806 0.093818 1.266338 0.2068 NIM -0.355011 0.313244 -1.133336 0.2584
LASCF 0.076150 0.026595 2.863271 0.0046 SIZE -0.329533 1.259126 -0.261716 0.7938 EQTL 0.445653 0.070103 6.357112 0.0000 ROA 0.600918 0.313710 1.915517 0.0568 REG -5.254101 1.401546 -3.748790 0.0002
86
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.934448 Mean dependent var 18.30296
Adjusted R-squared 0.908615 S.D. dependent var 11.08320 S.E. of regression 3.350441 Akaike info criterion 5.490716 Sum squared resid 2278.768 Schwarz criterion 6.531444 Log likelihood -698.6816 F-statistic 36.17245 Durbin-Watson stat 2.201147 Prob(F-statistic) 0.000000
Regression Results Based On Country Table 29: Regression Results, China Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 03:01 Sample: 2004 2007 Cross-sections included: 26 Total panel (balanced) observations: 104
Variable Coefficient Std. Error t-Statistic Prob. C 5.731138 18.03368 0.317802 0.7516
LLR 0.921922 0.426739 2.160387 0.0342 NIM 0.812396 0.746274 1.088603 0.2801
LASCF -0.004850 0.043259 -0.112105 0.9111 SIZE -0.709365 1.801794 -0.393699 0.6950 EQTL 1.301001 0.094177 13.81446 0.0000 ROA 0.291554 0.960196 0.303640 0.7623
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.923674 Mean dependent var 10.23942
Adjusted R-squared 0.886064 S.D. dependent var 4.718828 S.E. of regression 1.592810 Akaike info criterion 4.031669 Sum squared resid 175.0560 Schwarz criterion 4.921608 Log likelihood -174.6468 F-statistic 24.55938 Durbin-Watson stat 1.883226 Prob(F-statistic) 0.000000
Table 30: Regression Results, Japan Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 03:00 Sample: 2004 2007
87
Cross-sections included: 109 Total panel (balanced) observations: 436
Variable Coefficient Std. Error t-Statistic Prob. C -6.446072 9.290613 -0.693826 0.4883
LLR -0.099272 0.071450 -1.389393 0.1657 NIM 0.575516 0.479732 1.199661 0.2312
LASCF 0.033007 0.009763 3.381025 0.0008 SIZE 1.489884 0.918475 1.622128 0.1058 EQTL 0.054470 0.060628 0.898433 0.3696 ROA 0.362106 0.061996 5.840798 0.0000
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.917651 Mean dependent var 10.04775
Adjusted R-squared 0.887353 S.D. dependent var 1.776664 S.E. of regression 0.596302 Akaike info criterion 2.029553 Sum squared resid 113.0730 Schwarz criterion 3.133135 Log likelihood -324.4426 F-statistic 30.28722 Durbin-Watson stat 2.135846 Prob(F-statistic) 0.000000
Table 31: Regression Results, Korea Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 02:42 Sample: 2004 2007 Cross-sections included: 18 Total panel (balanced) observations: 72
Variable Coefficient Std. Error t-Statistic Prob. C 40.39420 15.57148 2.594113 0.0128
LLR -0.242290 0.473888 -0.511281 0.6117 NIM -0.259819 0.327242 -0.793967 0.4314
LASCF 0.007861 0.020034 0.392379 0.6966 SIZE -2.665685 1.382805 -1.927737 0.0602 EQTL 0.085856 0.062956 1.363754 0.1794 ROA 0.649431 0.351681 1.846645 0.0714
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.893787 Mean dependent var 12.08750
Adjusted R-squared 0.832420 S.D. dependent var 1.634999
88
S.E. of regression 0.669312 Akaike info criterion 2.314865 Sum squared resid 20.15906 Schwarz criterion 3.168614 Log likelihood -56.33513 F-statistic 14.56453 Durbin-Watson stat 2.269168 Prob(F-statistic) 0.000000
Table 32: Regression Results, Indonesia Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 02:45 Sample: 2004 2007 Cross-sections included: 32 Total panel (balanced) observations: 128
Variable Coefficient Std. Error t-Statistic Prob. C 33.61557 20.42290 1.645974 0.1034
LLR 0.187517 0.194021 0.966478 0.3365 NIM -0.883440 0.374218 -2.360759 0.0205
LASCF 0.069901 0.037820 1.848259 0.0680 SIZE -2.672556 2.724926 -0.980781 0.3294 EQTL 0.624025 0.136754 4.563119 0.0000 ROA 0.613175 0.602363 1.017949 0.3115
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.954145 Mean dependent var 21.70195
Adjusted R-squared 0.933063 S.D. dependent var 11.82020 S.E. of regression 3.058151 Akaike info criterion 5.328001 Sum squared resid 813.6493 Schwarz criterion 6.241542 Log likelihood -299.9921 F-statistic 45.25747 Durbin-Watson stat 2.242310 Prob(F-statistic) 0.000000
Table 33: Regression Results, Malaysia Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 02:50 Sample: 2004 2007 Cross-sections included: 28 Total panel (balanced) observations: 112
Variable Coefficient Std. Error t-Statistic Prob. C -11.90194 20.65291 -0.576284 0.5661
LLR -0.307029 0.150755 -2.036614 0.0452 NIM -0.599086 1.403692 -0.426793 0.6708
LASCF 0.126783 0.041391 3.063068 0.0030
89
SIZE 2.824817 2.117077 1.334300 0.1861 EQTL 0.255343 0.093056 2.743966 0.0076 ROA 0.966544 0.341545 2.829921 0.0060
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.921251 Mean dependent var 17.89107
Adjusted R-squared 0.883451 S.D. dependent var 8.846301 S.E. of regression 3.020061 Akaike info criterion 5.308135 Sum squared resid 684.0576 Schwarz criterion 6.206210 Log likelihood -260.2555 F-statistic 24.37195 Durbin-Watson stat 2.046745 Prob(F-statistic) 0.000000
Table 34: Regression Results, Philippines Dependent Variable: CAR Method: Panel Least Squares Date: 05/10/09 Time: 02:55 Sample: 2004 2007 Cross-sections included: 13 Total panel (balanced) observations: 52
Variable Coefficient Std. Error t-Statistic Prob. C 53.62602 26.41877 2.029846 0.0513
LLR 0.097817 0.155821 0.627754 0.5349 NIM 1.089640 1.244712 0.875415 0.3883
LASCF 0.026018 0.071662 0.363062 0.7191 SIZE -6.307533 3.182478 -1.981957 0.0567 EQTL 0.595044 0.249097 2.388802 0.0234 ROA 2.371279 1.296926 1.828384 0.0775
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.846081 Mean dependent var 19.39154
Adjusted R-squared 0.738338 S.D. dependent var 5.748030 S.E. of regression 2.940285 Akaike info criterion 5.290998 Sum squared resid 259.3583 Schwarz criterion 6.116524 Log likelihood -115.5659 F-statistic 7.852752 Durbin-Watson stat 2.490223 Prob(F-statistic) 0.000000
Table 35: Regression Results, Thailand Dependent Variable: CAR Method: Panel Least Squares
90
Date: 05/10/09 Time: 02:58 Sample: 2004 2007 Cross-sections included: 12 Total panel (balanced) observations: 48
Variable Coefficient Std. Error t-Statistic Prob. C 14.70383 14.48386 1.015187 0.3190
LLR -0.192779 0.167899 -1.148180 0.2610 NIM 0.322706 0.382824 0.842962 0.4067
LASCF 0.073496 0.036815 1.996364 0.0561 SIZE -1.700606 1.450343 -1.172554 0.2512 EQTL 1.150237 0.125861 9.138957 0.0000 ROA -0.587712 0.736396 -0.798092 0.4318
Effects Specification Cross-section fixed (dummy variables)
Period fixed (dummy variables) R-squared 0.945619 Mean dependent var 14.70792
Adjusted R-squared 0.905336 S.D. dependent var 3.775515 S.E. of regression 1.161630 Akaike info criterion 3.437160 Sum squared resid 36.43335 Schwarz criterion 4.255811 Log likelihood -61.49185 F-statistic 23.47476 Durbin-Watson stat 2.348750 Prob(F-statistic) 0.000000
B) Banks Included In Sample
Table 36: Sample Banks of China Bank of Beijing Co Ltd Bank of China Limited Bank of Communications Co. Ltd Agricultural Development Bank of China Bank of Chongqing Bank of Nanjing Bank of Ningbo Bank of Shanghai China Construction Bank Corporation China Merchants Bank Co Ltd China Minsheng Banking Corporation China Zheshang Bank Co Ltd Commercial Bank Co Ltd of Luoyang First Sino Bank Hua Xia Bank Industrial Bank Co Ltd Jinan City Commercial Bank Laishang Bank Co Ltd
91
Nanchang City Commercial Bank Yinzhou Bank-Ningbo Yinzhou Rural Cooperative Bank Rural Credit Cooperatives Union of Shunde Shanghai Pudong Development Bank Shenzhen Development Bank Co., Ltd Tianjin City Commercial Bank Xi'an City Commercial Bank Yantai City Commercial Bank Co Ltd Table 37: Sample Banks of Japan 77 Bank (The) Aichi Bank Akita Bank Ltd Aomori Bank Ltd. (The) Awa Bank (The) Bank of Ikeda Bank of Iwate, Ltd Bank of Kochi, Ltd Bank of Kyoto Bank of Nagoya Bank of Okinawa Bank of Saga, Ltd. (The) Bank of the Ryukyus Ltd. Biwako Bank Ltd Chiba Bank Ltd. Chiba Kogyo Bank Chikuho Bank Chugoku Bank, Ltd. (The) Chukyo Bank Ltd Chuo Mitsui Trust Holdings, Inc Daisan Bank, Ltd. Daishi Bank Ltd (The) Daito Bank Ehime Bank, Ltd. (The) Eighteenth Bank (The) Fukuho Bank, Ltd. (The) Fukui Bank Ltd. (The) Fukushima Bank Gifu Bank Ltd (The) Gifu Shinkin Bank Gunma Bank Ltd. (The) Higashi-Nippon Bank Higo Bank (The) Hiroshima Bank Ltd Hokkaido Bank Hokkoku Bank Ltd. (The) Hokuetsu Bank Ltd. (The) Hokuto Bank
92
Hokuyo Bank-North Pacific Bank Howa Bank, Ltd Hyakugo Bank Ltd. Hyakujushi Bank Ltd. Ibaraki Bank, LTD. Iyo Bank Ltd Joyo Bank Ltd. Juroku Bank Ltd. (The) Kabushiki Kaisha Mitsubishi UFJ Financial Group-Mitsubishi UFJ Financial Group Inc Kagawa Bank, Ltd. Kagoshima Bank Ltd. (The) Kanagawa Bank, Ltd. Kansai Urban Banking Corporation Kanto Tsukuba Bank Ltd Keiyo Bank, Ltd. (The) Kinki Osaka Bank Ltd (The) Kita-Nippon Bank Kiyo Bank Kumamoto Family Bank, Ltd Kyoto Chuo Shinkin Bank Kyoto Shinkin Bank (The) MIE Bank Ltd (The) Minami-Nippon Bank, Ltd. Minato Bank Ltd Miyazaki Bank Miyazaki Taiyo Bank, Ltd. (The) Mizuho Bank Mizuho Corporate Bank Mizuho Financial Group Momiji Bank Musashino Bank Nagano Bank Ltd. Nanto Bank Ltd. (The) Nishi-Nippon City Bank Ltd (The) Ogaki Kyoritsu Bank Oita Bank Ltd (The) Okazaki Shinkin Bank (The) Okinawa Kaiho Bank Ltd (The) Resona Holdings, Inc Saikyo Bank San-In Godo Bank, Ltd Sapporo Bank Ltd (The) Sapporo Hokuyo Holdings, Inc Sendai Bank, Ltd. Senshu Bank Ltd. (The) Seto Shinkin Bank (The) Shiga Bank, Ltd (The)
93
Shikoku Bank Ltd. (The) Shimane Bank Ltd Shimizu Bank Ltd (The) Shinwa Bank Ltd. (The) Shizuoka Bank Shonai Bank Sumitomo Mitsui Financial Group, Inc Suruga Bank, Ltd. (The) Taiko Bank Ltd Tajima Bank Ltd (The) Tochigi Bank, Ltd. Toho Bank Ltd. (The) Tohoku Bank Tokushima Bank Tokyo Star Bank Ltd. Tokyo Tomin Bank, Ltd. (The) Tomato Bank, Ltd Tottori Bank Towa Bank Toyama Bank, Ltd, (The) Yachiyo Bank Yamagata Bank Ltd. Yamaguchi Bank Yamanashi Chuo Bank Ltd (The) Table 38: Sample Banks of Korea Jeju Bank-Cheju Bank, Ltd. Citibank Korea Inc. Daegu Bank Ltd. Export-Import Bank of Korea Hana Bank Industrial Bank of Korea Jeonbuk Bank Kookmin Bank Korea Development Bank Korea Exchange Bank Kwangju Bank Ltd. (The) Kyongnam Bank National Agricultural Cooperative Federation - NACF Pusan Bank Shinhan Bank Standard Chartered First Bank Korea Limited Woori Bank Woori Financial Group-Woori Finance Holdings Co. Ltd Table 39: Sample Banks of Indonesia Agroniaga Bank Tbk (PT) ANZ Panin Bank
94
Bank Bumi Arta Bank Bumiputera Indonesia Bank Central Asia Bank Chinatrust Indonesia Bank Commonwealth Bank Danamon Indonesia Tbk Bank DBS Indonesia Bank Ekonomi Rahardja Bank Haga Bank Internasional Indonesia Tbk Bank Jabar PT Bank Lippo Tbk. Bank Mandiri (Persero) Tbk Bank Mega TBK Bank Negara Indonesia (Persero) - Bank BNI Bank Nusantara Parahyangan Bank OCBC NISP Tbk Panin Bank-Bank Pan Indonesia Tbk PT Bank Permata Tbk Bank Rabobank International Indonesia Bank Sumitomo Mitsui Indonesia Bank Swadesi Bank Tabungan Negara (Persero) Bank Tabungan Pensiunan Nasional PT Bank UOB Buana Bank Woori Indonesia Hongkong and Shanghai Banking Corporation Limited (The) PT Bank CIMB Niaga Tbk PT Bank Mizuho Indonesia PT Bank Muamalat Indonesia Tbk Table 40: Sample Banks of Malaysia Affin Bank Affin Holdings Berhad Affin Investment Bank Berhad Alliance Bank Malaysia Berhad AmBank (M) Berhad AMMB Holdings Berhad Bangkok Bank Berhad Bank Kerjasama Rakyat Malaysia Berhad Bank of Nova Scotia Berhad Bank of Tokyo-Mitsubishi UFJ (Malaysia) Berhad BIMB Holdings Berhad Bumiputra-Commerce Holdings Berhad CIMB Bank Berhad CIMB Investment Bank Berhad Citibank Berhad Deutsche Bank (Malaysia) Bhd.
95
EON Bank Berhad Hong Leong Bank Berhad HSBC Bank Malaysia Berhad Malayan Banking Berhad - Maybank Maybank Investment Bank Berhad OCBC Bank (Malaysia) Berhad Public Bank Berhad RHB Bank Berhad RHB Investment Bank Bhd Royal Bank of Scotland Berhad (The) Standard Chartered Bank Malaysia Berhad United Overseas Bank (Malaysia) Bhd. Table 41: Sample Banks of Philippines Allied Banking Corporation Banco de Oro Unibank, Inc. Bank of The Philippine Islands China Banking Corporation - Chinabank Chinatrust (Philippines) Commercial Bank Corp Development Bank of the Philippines Land Bank of the Philippines Metropolitan Bank & Trust Company Philippine National Bank Planters Development Bank Rizal Commercial Banking Corp. Security Bank Corporation Union Bank of the Philippines Table 42: Sample Banks of Thailand Bangkok Bank Public Company Limited Bank of Ayudhya Public Company Ltd. Kasikornbank Public Company Limited Kiatnakin Bank Public Company Limited Krung Thai Bank Public Company Limited Siam City Bank Public Company Limited Siam Commercial Bank Public Company Limited Siam Industrial Credit Public Company Limited (The) Standard Chartered Bank (Thai) Public Company Limited Thanachart Bank Public Company Limited Tisco Bank Public Company Limited United Overseas Bank (Thai) PCL