Forecasting the Quality of Corporate and Consumer Loans

in the Thai Banking Sector: Methodology and Policy Implications

Aekkanush Nualsri, Rungporn Roengpitya, Worawut Sabborriboon

and Nongjaras Thanavibul*

15 December 2014

Abstract

This paper is among the first of its kind to provide a holistic analysis to NPL forecast—namely

both systematic framework supported by a sound statistical methodology—and results of forecasting the

quality of loans, notably the amount of special mentioned loans (SM—30 days past due) and non-

performing loans (NPL—90 days past due) for corporate and consumer loans separately. Using the

quarterly data from 1999Q4-2014Q1 and employing the time-series ARMA regression analysis, we found

that SM and NPL loans for both corporate and consumer sectors can be predicted by the movements of

important macroeconomic and bank-behavior factors, such as real GDP, inflation, oil prices, excess

liquidity, loan growth and the debt burden of borrowers. These results can then be used to forecast the

amount of SM and NPL at the end of the 4th quarter 2014 and assess the loan quality of the Thai banking

sector. Therefore, these SM/NPL models are useful tools to assess the condition of the banking industry

in a forward-looking way so as to consequently issue efficient and timely regulatory policies.

JEL Classification: C22, G17, G21

Keywords: non-performing loans, special-mentioned loans, loan quality

*Quantitative Models and Financial Engineering Team, Financial Institutions Policy Group, Bank of Thailand.

Contact rungporr@bot.or.th, worawuts@bot.or.th, nongjart@bot.or.th or aekkanun@bot.or.th. The authors are

grateful to the senior executives at the Bank of Thailand for their guidance and valuable comments and also very

much appreciate the support from our colleagues. Views expressed in this research are our own.

Working paper. Please do not quote without permission from the authors.

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Introduction

Since the 2008 financial crisis, regulators and researchers have been motivated to develop

forward-looking quantitative tools as an early warning system and for assessment of the well-

being of the financial sector. The major challenges in predicting the quality of loans in a

refined way are the lack of data in order to be able to separate the quality of corporate and

consumer loans and the deep understanding of the banking business to set a framework that

truly captures the dynamic of the lending business.

This paper provides not only the methodology for determining important banking and

macroeconomic predictors of the quality of loans for the corporate1 and consumer loans

separately—namely the special mentioned loans (SM—30 days past due) and non-performing

loans (NPL—90 days past due) — but also the underlying framework to govern the time-series

regressions so that they reflect the inherent dynamic of banks’ lending business. Instead of

predicting the NPL ratio, our method concentrates on predicting the growth rate, and hence the

amount, of SM and NPL for each loan type, since the NPL ratio, by construction, varies very

little due to a large amount of loan being a denominator. Consequently, this paper is among

the first of its kind to forecast the quality of loans using both the conceptual framework and the

empirical approach together.

Following such conceptual framework, we used the quarterly SM and NPL data for the

corporate and consumer loans in the Thai banking sector from 1999Q4 to 2014Q1 to run the

time-series regressions. We proceed in two phases. The first phase consists of narrowing down

over fifty possible determinants of the quality of loans to less than ten and testing for the lead-

lag structure, using Granger causality test. The second phase involves the full ARMA time-

series analysis, using the lending business framework, the significance level of the overall

regression and the goodness of fit as criteria to determine the best forecasting models. We

found that the important determinants for the quality of loans can be divided into four groups:

(1) factors reflecting the condition of liquidity or competition for source of fund in the banking

sector, namely excess liquidity or effective borrowing rate; (2) level of loan growth for both

corporate and consumer loans; (3) macroeconomic and price factors such as real GDP growth,

inflation and oil price; and (4) debt burden of borrowers as measured by the debt service ratio

1 Corporate loans excluded the lending between financial institutions, as they are of low-default in nature and often large in

magnitude which may dilute the predictive power of the model.

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(DSR). In addition, when comparing the in-sample forecast values with the actual data, our

SM consumer model performed extremely well, while our SM/NPL models for corporate loans

slightly overestimated the actual value. However, the NPL consumer model underestimated

the actual value because of the unprecedented increase in consumer loans due to the first car

tax rebate policy in 2012-2013, which had altered the lending dynamic on the consumer loan.

Finally, we then used the regression results to forecast the amount of SM/NPL loans at the end

of 2014 to assess the health of the banks’ lending market.

This paper is structured as follows. Section 1 discusses briefly on the existing

literatures regarding the NPL estimation models. Then, Section 2 provides an in-depth

analysis on the historical behavior of the SM/NPL in the Thai banking industry. The

conceptual framework which pins the regression and the data analysis are presented in Section

3. Section 4 presents the rationale of model selection and main estimation outcomes, followed

by Section 5 where forecast results are shown and policy implications are discussed. The paper

ends with our concluding remarks.

Section 1: Literature Review on NPL Forecasting Models

There are numerous studies aiming to pinpoint the determinants of NPL. Most of these

literatures focused on either predicting the overall NPL ratio or the overall growth of NPL

itself. Bergeand Boye (2007) found that problem loans are determined mainly by the real

interest rates and unemployment for the Nordic banking system over the period 1993–2005.

Using the data of 54 countries, Buncic and Melecky (2013) found that GDP growth, inflation

and real interest rates are significant determinants of NPL ratio while Nkusu (2011) found that

real GDP, unemployment, interest rates and housing and equity prices played an important

role in determining the NPL ratios of 26 advanced economies.

In addition, De Bock and Demyanets (2012) employed the panel data regression on the

annual data from 1996-2010 in 25 emerging market countries and discovered that real GDP,

foreign exchange rates, and capital flows are important drivers for the NPL ratios in these

countries, on average. For 75 advanced and emerging economies from 2000 to 2010, Beck,

Jakubík and Piloiu (2013) found that real GDP growth, share prices, the nominal effective

exchange rate of the local currency and the bank lending interest rate significantly affected the

changes in the NPL ratio, using the fixed-effect panel data regression.

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As for the research on predicting sectoral NPL, Louzis, Vouldis and Metaxas (2012)

used the data for Greece and found that the movement of NPL ratio in mortgages, business,

and consumer loans can be explained mainly by macroeconomic variables (GDP,

unemployment, interest rates, public debt) and bank management quality while Rinaldi and

Sanchis-Arellano (2006) determined that the behavior of the household NPLs in European

countries depended on disposable income, unemployment and monetary conditions.

Section 2: Overview of Special-mentioned Loans & Non-performing Loans

in the Thai Banking Sector 2000-2013

At the beginning of the 2000s, the high level of NPLs, or loans which are 90 days or more

overdue, in the Thai banking system was apparently due to the legacy NPLs from the 1997-

1998 Asian financial crisis. Corporate NPLs amounted to 1,727.3 billion baht at the end of

March 2000, corresponding to an extremely high corporate NPL ratio2 of 49.3%. Nonetheless,

two years later it fell sharply to 11.9% at end of September 2002, thanks to the effort on the

NPL resolution and debt restructuring process put forth by banks and government agencies.

The notable progress was the establishment of the government-owned and operated Thai Asset

Management Corporation (TAMC) in 2001 with the aim to consolidate the management of the

non-performing assets of financial institutions. Since then, the corporate NPL continued to

decline during the subsequent years, though at a slower pace, in line with the economic

recovery as well as the unrelenting and rigorous supervision, prodding banks to improve their

risk management and overall asset quality. At the end of December 2013, the corporate NPLs

amounted to 193.3 billion baht and the corporate NPL ratio was 3.2%.

The corporate special-mentioned (SM) loans, which refer to corporate loans overdue by

30-89 days, stood at 133.5 billion baht at the end of March 2000, with an SM ratio3 3.8%. This

ratio had slowly declined during the period 2000 to 2005 and then began climbing again,

reaching 5.7% at the end of March 2009 before sluggishly declining afterwards. At the end of

2013, the corporate SM loans amounted to 182.4 billion baht and the corporate SM ratio was

3.0%.

2 Corporate NPL ratio refers to the ratio of corporate NPLs to total outstanding corporate loans, and consumer NPL ratio refers

to the ratio of consumer NPLs to total outstanding consumer loans. 3 Corporate SM ratio refers to the ratio of corporate SM loans to total outstanding corporate loans, and consumer SM ratio

refers to the ratio of consumer SM loans to total outstanding consumer loans.

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Figure 1: Corporate NPLs and SM loans outstanding and ratio 2000-2013

On the consumer loan, like the corporate NPLs, the Asian financial crises had a

profound effect on the consumer NPLs in the early post-crisis years. At the end of March 2000,

the consumer NPLs were at 200 billion baht, corresponding to the consumer NPL ratio of

15.2%. A rebound of consumer lending due to the economic recovery coupled with improved

risk management practices, both in loan approval process and loan quality monitoring, were

key to the steady decline of the consumer NPLs in the past years. At the end of 2013, the

consumer NPLs in the system were brought down to 71.6 billion baht and the consumer NPL

ratio was 1.7%.

Figure 2: Consumer NPLs and SM loans outstanding and ratio 2000-2013

The consumer SM loans seemed to tell a different story. Unlike the behavior of the

consumer NPLs, the consumer SM loans were quite low and politely stable in early 2000s

before picking up in 2005. From that point on, the outstanding of consumer SM loans markedly

rose 5 times over the last decade, from 22.2 billion baht at around early 2005 to 113.2 billion

baht at the end of December 2013. Consequently, this brought the consumer SM ratio up from

1.6% at the end of March 2005 to 2.7%. In addition, it is worth noting that an increase in the

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consumer SM loans, seen particularly in recent years, was partially a result of a rise in

household indebtedness induced by populist policies implemented during 2011-2012, such as a

tax rebate for first-time car and home buyers etc. This led to an irregular surge in mortgage

and household lending, which in turn affected the behavior of both SM and NPL consumer

loans described previously.

To conclude, the behaviors of SM loans and NPL differ in general. While NPL ratio

seems to be less volatile when compared to the SM loan ratio, the SM loan ratio seems to be

correlated with the economic conditions more by construction, since the loan will be classified

as SM after just only one payment missed. In addition, not all SM loans will turn out to be

NPL. This is because banks tend to actively manage SM loans so that it will not deteriorate to

the NPL status.

Section 3: Conceptual Framework & Data

This section outlines our conceptual framework and data employed in this study. As mentioned

in the introduction section, the conceptual framework is essential in our analysis, as we aim to

construct the SM/NPL forecast models which are econometrically sound and capable of

capturing the dynamic of corporate and consumer lending practice in the Thai banking

industry.

3.1 Constructing the Conceptual Framework

The purpose of having the conceptual framework is to use it to govern the lead-lag structure of

the explanatory variables in our time-series regressions so that they mimic the lending practice

in the banking sector. To identify the determinants of corporate and household NPL/ SM loans,

we utilized the general bank lending dynamic and constructed a timeline that captures the

credit life cycle from the beginning, when banks assess their lending capacity, until the end

when outstanding loans became NPLs. Figure 3 depicts our hypothetical timeline of the

origination of SM loans and NPLs where the sequence of events can roughly be classified into

5 phases.

Our conceptual framework yields the following. First, like any lending business, it

starts from having a source of fund to lend. Hence, a bank’s liquidity position (Phase 1), which

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is characterized by factors reflecting liquidity needs such as L/D ratio, effective borrowing rate

and excess liquidity, can be used to gauge the banks’ capacity for loan expansion in the next

period. This surge in liquidity in the system will then subsequently manifest in the amount of

corporate and consumer loans granted by banks (Phase 2). After the loans are issued, then

macroeconomic factors, such as unemployment, GDP, inflation, etc., and ability to service debt

of borrowers, evaluated by means of the estimated aggregate debt service ratio (see Appendix

for the full calculation method), are crucial in determining the amount of loans going to be

classified as SM/NPL in later periods (Phase 3). Early signs of loan quality deterioration can

then be captured by variables, for instance amount of overdraft, cash advance on credit cards

and bounced checks (Phase 4). However, it is worth noting that these early symptoms of

problem loans may not necessarily occur long after the downturn macroeconomic conditions.

Indeed, they can happen in a more timely manner or, at worst, in the same quarter, especially

during a prolonged economic slump. Thus merging Phase 3 and Phase 4 together is also one

sensible choice we will consider. SM loans and NPLs are then the outcome of all these

dynamics at the end of the timeline (Phase 5) where loans are overdue by 30-89 days, and 90+

days, respectively.

With this framework in mind, we then use it as a soft structure to govern the

underlying lead-lag pattern of the explanatory variables in our time-series regression. The

rationale behind this approach can be found in Section 4.1.

Figure 3: Conceptual Framework Regarding the Origination of NPLs and SM loans

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3.2 Data Employed

This study uses the quarterly time-series data during the period 1999Q4-2014Q1. The banking

data, including the amount of NPLs and SM loans for both the corporate and household sectors

in the Thai banking system4, is collected from the Bank of Thailand (BOT) database. We also

constructed the time-series for aggregate debt service ratios (ADSR), effective loan rates, and

effective borrowing rates using widely-used methods from the existing literatures (see

Appendix). The data on macroeconomic variables is obtained from multiple sources; Ministry of

Commerce, Ministry of Finance, Office of the National Economic and Social Development Board

(NESDB), the Stock Exchange of Thailand (SET), and Ministry of Labor, etc.

We excluded interbank lending transactions from our measure of corporate loans due

to a specific nature of interbank transactions which are typically in extra-large volumes with

low probability of default. Also, due to the lack of data, this study covers corporate and

consumer loans in the banking system only, so the credit issued by Specialized Financial

Institutions (SFIs), cooperatives and other non-bank financial intermediaries are excluded.

However, our coverage for the banking industry remains relatively large, accounting for over

60-70% of the total lending in Thailand’s financial system throughout the period.

Using the framework set forth in Section 3.1, we started our analysis with over 50

variables (Table 1) for the primary variable selection, whose process is detailed in Section 4.1.

Table 1: List of the Variables for Testing Potential Lead-lag Relationship between the Variable and NPLs/SM Loans

4 Thai banking system refers to all domestic banks and foreign bank branches under the supervision of the Bank of Thailand.

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Section 4: Construction, Selection and Results of Forecasting Models

This section illustrates the rationale in creating the building blocks for our forecasting models

of SM loans and NPLs in both corporate and household sectors. In addition, the results of the

possible models will also be presented. We first introduce the method of data selection,

followed by the rationale in constructing our forecasting models and later will present the

models selected as well as the results to be used later in the forecast.

4.1 Variable Selection Process

Using the set of data presented in Section 3.2, we first tested for stationarity in the time-

series in levels and in growth rates so as to avoid spurious causality results. We performed

commonly known tests, namely the Augmented Dickey-Fuller (ADF), Phillips Perron (PP)

and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS)5 to detect a unit root in the data

series. The possibility of unit roots at seasonal frequencies was also explored using the

Hylleberg, Engle, Granger, and Yoo (HEGY) seasonal unit root test6.

Table 2 reports the stationarity test results on selected variables. Despite some

conflicting results obtained from the ADF and PP tests, the series become stationary in

growth rates for the majority of variables. We also found eleven series in levels exhibit

evidence of seasonal unit roots based on the HEGY test.

5 The ADF and PP are both under the null hypothesis of a unit root (non-stationarity). These two tests, however, have different

ways to handle heteroskedasticity and correlated errors. The ADF includes lags of the first-differenced variable in the model

specification to account for that matter, while the PP modifies the test statistics using the Newey-West (1987)

heteroskedasticity and autocorrelation-consistent covariance matrix estimator. The PP test is typically more powerful than the

ADF test since it does not have to specify lag lengths for the test regression, but it may have size distortions in the presence of

large negative moving average (MA) errors. Unlike the ADF and PP unit root tests, the KPSS tests the null hypothesis of trend

stationarity, and it has often been used to confirm the ADF and PP test results. 6 In addition to the standard unit root tests (like ADF, PP, and KPSS) which assume away the existence of unit roots at

frequencies other than the zero frequency, the HEGY tests potential seasonal unit roots at zero, biannual, and annual

frequencies simultaneously. The HEGY has the drawback of size distortions and being relatively sensitive to the inclusion of

deterministic components in the test regression.

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Table 2: Unit Root Test Results on Selected Variables

Series in Levels Series in Growths (% YoY)

ADF PP KPSS HEGY (Biannual

Freq.)

ADF PP KPSS HEGY (Biannual

Freq.)

SM_Corporate

-2.270

-1.999

†

0.114 -3.057

-2.624 -3.033 0.081 -3.613

SM_Consumer -0.526

-0.526

0.163

**

-3.372

-1.585

-1.531

0.125 -3.705

NPL_Corporate -7.246

-6.143 0.122 -4.706 -3.305 -3.061 0.081 -4.008

NPL_Consumer -2.329

-2.371

0.209 -3.228

-1.884

-2.143

0.089 -3.685

Aggregate DSR-Corporate -3.589

-4.371 0.190

-3.234

-3.393

-3.233

0.078 -5.783

Aggregate DSR-Consumer -0.960

-0.963

0.189

-4.752 -3.815

-2.955 0.047 -5.689

Cash Advance -1.216

-1.939

0.214 -2.690

-4.559 -4.549 0.156 -2.972

Corporate Loan -0.100

-0.509

0.221

-3.370

-1.564

-3.940 0.060 -3.700

Consumer Loan -0.843

-1.520

0.264

-4.593 -0.972

-1.211

0.173 -4.023

Consumer Price Index (CPI) -1.841

-2.625

0.194 -5.174 -5.554 -2.956

0.064 -5.102

Construction and land value -4.724 -4.698 0.083 -4.229 -4.163 -4.194 0.107 -5.422

Effective Borrowing Rate -2.876

-2.619

0.125 -3.540

-2.857 -2.418

0.057 -6.188

Effective Loan Rate-Corporate -2.714

-2.714

0.082 -4.832 -1.865

-3.190 0.106 -4.135

Effective Loan Rate-Consumer -3.842

-3.270

0.082 -4.873 -3.833

-3.505

0.140 -5.919

Excess Liquidity -1.414

-1.322

0.159 -4.015 -3.546 -3.533 0.159 -4.213

Exchange Rate -6.137 -4.558 0.233

-2.819

-8.228 -6.638 0.142 -4.722

L/D Ratio -3.490

-3.491

0.101 -3.272

-4.464 -2.869

0.104 -3.845

Oil Price -4.933 -3.241

0.088 -5.803 -4.031 -3.297

0.065 -5.794

Private Consumption Expenditure -1.751

-4.322 0.101 -2.863

-3.926

2.879

0.136 -4.180

Producer Price Index (PPI) -2.786

-2.361

0.189 -5.052 -1.426

-3.209 0.087 -5.055

Provisions -3.433

-3.373

0.063 -4.294 -7.778 -7.777 0.047 -4.014

Real GDP -4.123 -4.750 0.115 -3.963 -6.417 -3.428

0.143 -4.950

SET Index -2.665

-3.135

0.159 -3.486

-4.125 -3.723

0.059 -3.726

Unemployment Rate -1.601

-6.989 0.219

-5.056 -2.768 -9.648 0.109 -5.121

VAT -3.181

-2.521

0.083 -3.967 -4.199 -3.010

0.075 -4.660

Notes: i) denotes that we do not reject the presence of a unit root at the 1% confidence level. ii) The Augmented Dickey-Fuller

(ADF), Phillips Perron (PP) and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) tests are standard tests for a unit root at zero

frequency. iii) The ADF test statistics reject the presence of a unit root for the growth rates of NPL_Consumer, Aggregate DSR-

Consumer, Effective Loan Rate-Consumer, and Private Consumption Expenditure at the 5% confidence level, and the growth

rate of SM_Consumer, Aggregate DSR-Corporate, Corporate Loan, and Effective Loan Rate-Corporate at the 10% confidence

level. iv) The PP test statistics reject the presence of a unit root for the growth rates of NPL_Consumer, Consumer Price Index,

Effective Loan Rate-Consumer, Oil Price, SET Index,and VAT at the 5% confidence level, and the growth rate of Aggregate

DSR-Corporate, L/D Ratio, Private Consumption Expenditure, and Real GDP at the 10% confidence level. v) The Hylleberg,

Engle, Granger, and Yoo (HEGY) seasonal unit root test detects unit roots at zero, bi-annual, and annual frequencies. The test

statics at biannual frequency are reported, while those at other frequencies are omitted here for space saving but are also

available upon request.

Next, we conducted the pairwise Granger Causality Test (GCT)7 as a first step to

find potentially relevant determinants of SM loans and NPLs, as well as the possible

causality, for our forecasting models. We opted to use the data series in year-on-year (YoY)

7 The Granger Causality testing is the test for causal relations between two variables. For example, variable X1 is said to

Granger-cause variable X2, if the past values of X1 contain information that helps predict X2 above and beyond the information

contained in the past values of X2 alone.

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growths instead of levels in the GCT and forecasting models due to several reasons. Firstly,

the findings in Table 2 supported evidence of stationarity in favor of the YoY growth series.

Secondly, the YoY growths provide information on business cycles and long term trends.

Finally, compared with first-differencing and log-transformed data, the YoY growth rates are

more public communication-friendly. One may argue against using the YoY growth rates for

fear of overdifferencing. We took precautions concerning this matter by always paying

attention to the desired properties of residuals from regression, similar in spirit to Plosser

and Schwert (1978) who argued that “the cost of overdifferencing may not be large when care

is taken to analyze the properties of regression disturbances.” (p. 643).

We set the criteria for passing the GCT which are: (1) the variables (in percentage

change) must significantly determine the growth of SM or NPLs; and (2) the lag structure

must behave according to our conceptual framework. For example, the change in excess

liquidity cannot happen after the growth in corporate or consumer credits.

On the first criteria, any pair-wise test yielding less than 90% significance will be

dropped from the list. Table 3 presents a list of the selected variables surviving the Granger

causality tests on this criterion.

Table 3: List of the Selected Variables Passing the Granger Causality Tests Variable Description Unit Source Remarks

SM_CORP Special-mention(SM) loan growth - corporate % YoY BOT1 Corporate loans overdue by 30-89

days

SM_CONS Special-mention(SM) loan growth - consumer % YoY BOT Consumer loans overdue by 30-89

days

NPL_CORP Non-performing loans(NPLs) growth - corporate % YoY BOT Corporate loans overdue by 90 days or

more

NPL_CONS Non-performing loans(NPLs) growth - consumer % YoY BOT Consumer loans overdue by 90 days

or more

CREDIT_CORP Corporate credit growth % YoY BOT

CREDIT_CONS Consumer credit growth % YoY BOT

DSR_CORP Aggregate debt service ratio growth - corporate

(ADSR growth)

% YoY Calculated by

authors, using

formula banks

used to calculate

monthly payment

of term loans

where Dt = debt stock (corp),

it = effective loan rate (corp),

mt= average remaining maturity

(corp), and

Yt = nominal GDP

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Notes: 1BOT stands for Bank of Thailand 2MOC stands for Ministry of Commerce 3SET stands for Stock Exchange of Thailand

4NSO stands for National Statistical Office

5NESDB stands for Office of the National Economic and Social Development Board

6EIA stands for U.S. Energy Information Administration

The second criteria yielded the variables whose lag structure satisfied our conceptual

framework. The variables and their lags are summarized in Table 4 and 5. Regarding the

prediction of corporate SM loans and NPLs, the final results in Table 4 clearly show that

leading indicators for quality of loans in the corporate sector can be categorized into 3

groups, ranked by their lag structure, which are (i) bank’s liquidity: such as L/D ratio, and

effective borrowing rate; (ii) corporate credit expansion; and (iii) macro factors: real GDP, oil

price, PPI, unemployment rate, SET index, exchange rate, and effective loan rate.

It is worth noting that we did not find any causality relationship between the cost of

production (e.g. oil prices, and PPI) and corporate NPLs, while we found that it was

significantly related to corporate SM loans. This may be because the cost of production

Variable Description Unit Source Remarks

DSR_CONS Aggregate debt service ratio growth - consumer

(ADSRC growth)

% YoY Calculated by

authors, using

formula banks

used to calculate

monthly payment

of term loans

where = debt stock (cons),

= effective loan rate (cons),

= average remaining maturity

(cons), and

= disposable income

EFF_BOR_RATE Effective borrowing rate growth % YoY Calculated by

authors

EFF_LEND_RATE Effective lending rate growth % YoY Calculated by

authors

PPI Producer price index growth % YoY MOC2

FX Exchange rate growth % YoY BOT

SET SET index growth % YoY SET3

UNEMP Unemployment rate growth % YoY NSO4

CASH_AD Cash advance growth % YoY BOT

PCE Private consumption expenditure growth % YoY NESDB5

CONSTRUCT Construction and land value growth % YoY BOT

PRO Provisions growth % YoY BOT

VAT VAT revenue growth % YoY BOT

EXC_LIQ Excess liquidity growth % YoY BOT

INF Consumer price index growth % YoY MOC

RGDP Real GDP growth % YoY NESDB

WTI WTI crude oil spot price growth % YoY EIA6

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serves as the major determinant as to whether the firm will start getting into trouble in the

first place or not.

Table 4: List of Variables and Their Lags Used in SM/NPL Prediction for Corporate Loans

SM –corporate

Variable (%yoy) Lead time (Quarter) 1. Real GDP 1

2. Oil Price 2

3. Producer price index (PPI) 3

4. Effective Lending Rate—Corporate* 3

5. Provisions 4

6. Corporate Loan 4

7. L/D Ratio 7

8. Effective Borrowing Rate* 7

NPL –corporate

Variable (%yoy) Lead time (Quarter) 1. Unemployment Rate 4

2. SET Index 4 3. Exchange rate 4

4. Effective Lending Rate—Corporate* 5

5. Corporate Loan 6

6. Effective Borrowing Rate* 8

7. Excess liquidity 8

*Calculated by authors

Similar to the case for corporate loans, the determinants of consumer SM loans and

NPLs shown in Table 5 can be categorized into 3 groups: bank’s liquidity, credit expansion,

and macroeconomic factors. Indeed, many of the macroeconomic variables contributing to

the consumer loan quality deterioration are present here, such as unemployment rate, CPI,

or even VAT. An interesting observation worth highlighting is the importance of aggregate

debt service ratio (ADSR) on consumer NPLs. The high level of indebtedness increases

balance sheet vulnerabilities for households that could hold the households bank from being

able to repay the debt in 90 days and, hence, pushing the loans to NPL status.

Table 5: List of Variables and Their Lags Used in SM/NPL Prediction for Consumer Loans

SM –consumer

Variable (%yoy) Lead time (Quarter)

1. Cash Advance 1

2. Private Consumption Expenditure 1

3. Effective Lending Rate—Consumer* 1

4. Real GDP 2

5. Unemployment Rate 2

6. Consumer Price Index 3

7. SET Index 3

8. Construction and land value 3

9. Consumer Loan 4

10. Effective Borrowing Rate* 6

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4.2 Rationale of Empirical Technique

Our study aims at building the forecasting models that not only capture the dynamic of bank

lending in practice as much as possible but also remain econometrically sound. The purpose of

this model is to determine linkages between the bank-specific and macroeconomic factors with

the quality of loans in later periods. With such motivation, we decided to build multivariate

forecasting models using the OLS regression estimation technique with the autoregressive and

moving average (ARMA) approach instead of the full vector autoregressive (VAR) approach

because of the following reasons:

(i) Our goal is to come up with hybrid forecast models that satisfy both the conceptual

structure outlined in Section 3.1 and the econometric requirements. Using the full vector

autoregressive (VAR) approach may be too data-driven and hence, fail to capture the structural

bank lending concept, especially for an economy that went through the financial crisis like

Thailand where the data may contain anomalies as a result of such event. Hence, relying on

the characteristics of historical data alone without any structural concept may fail to capture

the underlying bank-specific and macroeconomic factors.

In fact, in our case, we found that the lagged SM loans and NPLs are highly correlated

with the current SM/NPL and, therefore, including this variable in the regression means most

of the movement in the dependent variable will be explained by this one variable and

consequently downplays the role of bank-specific and macroeconomic factors whose

relationships to the SM/NPL we try to determine. So, we decided to omit the lagged dependent

variables from our regression.

(ii) When it comes to choosing between the ARMA regression vs. the full VAR

technique, ARMA method has been widely used in the literature and is suitable for this study,

as it can capture the relationship between past and current time-series values and at the same

NPL –consumer

Variable (%yoy) Lead time (Quarter)

1. Effective Lending Rate—Consumer* 1

2. SET Index 2

3. Aggregate DSR – Consumer* 3

4. VAT 3

5. Unemployment Rate 3

6. Provisions 4

7. Oil Price 4

8. Consumer Loan 6

9. L/D Ratio 7

*Calculated by authors

15

time can handle multiple independent variables (for further disadvantages using VAR, see

Brännström (1995)). As seen in the conceptual framework in Section 3.1, there are quite a few

factors we take an interest in using as explanatory variables so as to determine the important

bank-specific variables and macroeconomic conditions that can affect the quality of loans,

whereas using VAR will restrict the number of variables which can be used in the regression.

Hence, using ARMA enables us to obtain these relationship characteristics.

(iii) Regarding the dynamic of SM loans or NPL itself, we place less importance on the

lag of NPLs being an independent variable. First, it is less clear that the growth in SM loans

and NPLs, are always of dynamic in nature. In other words, past NPL growth may not

necessarily determine the NPL growth in latter periods. Louzis, et al (2012) found that the

lagged NPL was insignificant in predicting the current NPL growth for business and mortgage

loans, although it was significant for consumer loans. Jakubík and Reininger (2013) found that

the lagged NPL ratio was generally not significant8 in determining the current period NPL

In addition, from our conceptual framework, one of the main drivers for the growth

of NPLs is the bank lending growth. Generally, bank credit issuance depends on the outlook

of the economy in the subsequent period, the liquidity position of the banks, the profitability

of the current lending business, bank size and, in some cases, same-period loan loss or non-

performing loans (see Juks (2004), Rodríguez and Carbó, Olokoyo (2011), Djiogap and

Ngomsi (2012), Louie (2013)).9 Our model allows the effect of NPL in previous periods to

feed through the outcome in bank lending decision via loan growth variables. In addition, by

introducing the autoregressive (AR) part, we capture the essence of lagged dependent

variables by addressing the correlation between the current error term and its lags.

4.3 Selecting a Forecast Model and Results from ARMA Estimation

We chose selected time-series models in which the growth of the dependent variable is a

linear function of a set of the growth of explanatory variables plus the error terms that

follow an ARMA (p,q) process:

8 Jakubík and Reininger (2013) found that the first lagged NPL was quite insignificant (with respect to barely making the 90%

confidence level for the variable’s significance) for the main NPL prediction model with real GDP, credit-to-GDP, stock index

and exchange rate as explanatory factors and also insignificant (less than 90% confidence level) when another lagged credit-to-

GDP ratio was added to the main model. 9 Djiogap and Ngomsi (2012) found that NPL does not play a role in the overall business loans but may play a role in long-term

business lending for banks with high NPL operating in a downturn period. Rodríguez and Carbó found that loan loss is not

statistically significant in determining the loan to asset ratio under the fixed-effect regression but is important under the

random-effect regression.

16

,

with the following criteria imposed upon the structure and results of the model: (1) the lead-lag

structure must satisfy our conceptual framework; (2) the expected sign of the relationship

between each independent variable and the dependent variable must confirm the common

rationale outlined in Table 6; (3) the model must be econometrically-sound in terms of F-stats,

stationarity, variable significance and model’s predictive power in-sample and out-of-sample; (4)

the model must have sufficiently high adjusted R2 to be used for forecasting; and (5) the

equation should have a macroeconomic factor in order to capture the condition of the economy,

such as real GDP growth at the least.

Table 6: Expected Signs of the Relationship between the Selected Variables and NPLs/ SM Loans

Variable Expected Sign Rationale

Growth of corporate loans

Growth of consumer loans

+ Loan expansions may lead to looser lending standards and less stringent

risk management practices resulting in higher SM loans and NPLs.

Corporate debt service ratio

Consumer debt service ratio

+ Higher debt burdens are associated with higher SM loans and NPLs

because they have an adverse impact on debtors’ repayment ability for the

debtors become more vulnerable to withstand negative income shocks.

Effective borrowing rate + Higher effective borrowing rates reflect a rise in banks’ funding cost due to

a fierce competition to raise fund so as to be able to issue more credit in

subsequent periods, leading to more SM loans/NPLs

Excess liquidity + Increasing excess liquidity encourages banks’ excessive lending behavior

which could cause a jump in SM loans and NPLs.

Inflation

Oil price index + Rising prices may lead to higher SM loans and NPLs as they restrain a

borrower’s purchasing power and ability to repay the loan.

Real GDP growth - Growing economy helps decrease the likelihood of loan default, resulting in

lower SM loans and NPLs.

Results of selected qualified regressions can be seen in Table 7-10. Table 7 displays

the results of the estimation while Table 8 reports the in-sample fit and out-of-sample

forecast evaluation for corporate loan quality. Model 1 and 2 are different model

specifications while Model 3 presents the robustness test of Model 1, removing the real GDP.

Results in Table 9-10 show the estimation and the in-sample fit and out-of-sample

forecast evaluation for consumer loan quality. Similar to the case of corporate loan quality

forecast, Model 1 and 2 are different model specifications while Model 3 presents the

robustness test of Model 1, removing the real GDP.

17

Table 7: Regression Results for Corporate SM Loans and NPLs

Notes: i) t-statistics are reported in parentheses. ii) ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. iii) The Breusch-Godfrey

serial correlation LM test statistics reported is under the null hypothesis of no serial correlation of any order up to 4.

Table 8: Forecast Model Evaluation for Corporate SM Loans and NPLs

Variable SM_CORP NPL_CORP

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

CREDIT_CORP (t-4) 0.744

(2.228)**

CREDIT_CORP (t-5) 0.948 1.414 1.161 0.855 1.144

(2.554)*** (4.010)*** (3.225)*** (2.435)*** (3.195)***

DSR_CORP 1.075 0.494 1.007

(3.697)*** (1.602)* (3.946)***

DSR_CORP (t-2) 0.820

(4.551)***

EFF_BOR_RATE(t-8) 0.469 0.429 0.510

(9.968)*** (8.088)*** (10.758)***

EXC_LIQ (t-8) 0.361 0.372 0.362

(4.305)*** (4.380)*** (4.299)***

RGDP (t-1) -0.445

(-0.705)

RGDP (t-3) -1.460

(-2.507)***

WTI (t-2) 0.113

(1.876)*

WTI (t-3) 0.350 0.213

(4.619)*** (2.529)***

Constant -0.013 -0.022 -0.067 -0.136 -0.161 -0.155

ARMA (4,5) (6,5) (4,5) (1,1) (6,1) (1,1)

Adj. R2 0.793 0.832 0.753 0.763 0.672 0.766

DW- stat 1.090 1.270 0.930 2.020 1.197 2.015

F-stat 24.626 29.815 23.541 26.192 18.231 31.741

Prob (F-stat) 0.000 0.000 0.000 0.000 0.000 0.000

LM Test--Prob (F-stat) 0.160 0.383 0.039 0.174 0.0543 0.295

# of Obs 38 36 38 48 43 48

SM_CORP NPL_CORP

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

In-sample Fit (2000Q4-2012Q4) Root Mean Squared Error 0.099 0.100 0.103 0.205 0.197 0.115

Mean Absolute Error 0.080 0.081 0.086 0.152 0.145 0.084

Mean Absolute Percentage Error 106.980 131.409 112.300 163.000 154.395 86.089

Theil Inequality Coefficient 0.231 0.243 0.238 0.416 0.405 0.220

Bias Proportion 0.000 0.009 0.000 0.000 0.000 0.000

Variance Proportion 0.045 0.145 0.021 0.069 0.092 0.035

Covariance Proportion 0.955 0.846 0.978 0.931 0.908 0.965

Out-of-sample Forecast (2013Q1-2013Q4) Root Mean Squared Error 0.188 0.187 0.332 0.101 0.102 0.097

Mean Absolute Error 0.160 0.158 0.293 0.090 0.091 0.086

Mean Absolute Percentage Error 366.390 318.639 619.622 246.949 233.051 194.795

Theil Inequality Coefficient 0.376 0.416 0.518 0.891 0.946 0.783

Bias Proportion 0.657 0.262 0.777 0.802 0.696 0.033

Variance Proportion 0.007 0.000 0.000 0.024 0.029 0.087

Covariance Proportion 0.336 0.737 0.223 0.174 0.275 0.881

18

Table 9: Regression Results for Consumer SM Loans and NPLs

Notes: i) t-statistics are reported in parentheses. ii) ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. iii) The Breusch-Godfrey

serial correlation LM test statistics reported is under the null hypothesis of no serial correlation of any order up to 4.

Table 10: Forecast Model Evaluation for Consumer SM Loans and NPLs

Variable SM_CONS NPL_CONS

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

CREDIT_CONS (t-3) 1.206 1.588 1.558

(1.905)* (1.596) (2.293)**

CREDIT_CONS (t-5) 1.256 1.238 1.245

(3.084)*** (2.234)** (2.233)**

DSR_CONS(t-1) 0.446 0.534 0.498 0.143 0.291 0.089

(1.944)* (1.700)* (2.169)** (1.208) (1.678)* (0.550)

EFF_BOR_RATE(t-6) 0.142 0.125 0.093

(2.156)** (1.771)* (1.484)

EFF_BOR_RATE(t-7) 0.125

(2.371)**

EFF_BOR_RATE(t-8) 0.084 0.102

(2.821)*** (2.521)***

INF (t-1) 6.458 4.638 5.219

(4.712)*** (4.911)*** (3.874)***

RGDP (t-1) -1.397

(-2.073)**

RGDP (t-2) -1.149

(-4.510)***

Constant -0.110 -0.135 -0.164 -0.168 -0.182 -0.202

ARMA (2,3) (4,4) (2,3) (6,8) (4,2) (6,8)

Adj. R2 0.683 0.566 0.653 0.667 0.616 0.432

DW- stat 1.456 1.118 1.137 1.029 1.006 0.420

F-stat 14.835 9.489 15.139 12.677 13.204 6.319

Prob (F-stat) 0.000 0.000 0.000 0.000 0.000 0.000

LM Test--Prob (F-stat) 0.056 0.048 0.012 0.168 0.000 0.000

# of Obs 46 40 46 36 39 36

SM_CONS NPL_CONS

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

In-sample Fit (2000Q4-2012Q4) Root Mean Squared Error 0.138 0.128 0.125 0.059 0.117 0.078

Mean Absolute Error 0.114 0.104 0.105 0.047 0.090 0.067

Mean Absolute Percentage Error 177.74 195.25 169.297 75.080 100.880 100.431

Theil Inequality Coefficient 0.276 0.250 0.251 0.226 0.296 0.318

Bias Proportion 0.004 0.000 0.001 0.000 0.000 0.003

Variance Proportion 0.147 0.147 0.210 0.082 0.229 0.176

Covariance Proportion 0.849 0.853 0.789 0.919 0.771 0.822

Out-of-sample Forecast (2013Q1-2013Q4) Root Mean Squared Error 0.057 0.162 0.088 0.173 0.052 0.176

Mean Absolute Error 0.046 0.157 0.062 0.163 0.045 0.174

Mean Absolute Percentage Error 15.493 50.607 23.450 79.992 120.467 84.604

Theil Inequality Coefficient 0.077 0.201 0.122 0.563 0.602 0.636

Bias Proportion 0.006 0.321 0.057 0.894 0.079 0.981

Variance Proportion 0.345 0.053 0.275 0.066 0.055 0.009

Covariance Proportion 0.649 0.626 0.668 0.040 0.866 0.010

19

All of the selected regressions in Table 7 and 9 have an essential characteristic.

These regressions possess two key factors which should be present in the results; namely the

factors reflecting the liquidity position and credit growth, which are essential in capturing

the dynamic of phases 1 and 2 in our conceptual framework. The Model 1 regressions shown

in both tables are selected because they satisfy the requirements previously mentioned in

Section 4.3, which requires real GDP factor to be present, and consequently become our

choices of forecast models as summarized in Table 11. Model 2 regressions represent

another specification which does not have real GDP but still captures the dynamic of the

loan quality. Figure 4-7 provide supporting evidence that the residuals from our forecast

models behave like a white noise process. We undertook two residual tests, namely the

Jarque-Bera normality test10 and the Bartlett’s periodogram-based test.11 The residual series

of corporate SM and NPL recorded one pass out of the two tests. The residual series of

consumer SM and NPL, even better, scored both tests. Note that we do not strictly rely on

information criteria such as AIC or BIC, which often give conflicting results, for lag length

selection. The lag length of variables in our regression is rather set such that it coincides

with the conceptual framework delineated in Section 3.1, while at the same time the

parameter estimates are statistically significant and the regression residuals are white

noise.

Table 11: The Selected Equations for SM/NPL Estimation

10 The Jarque-Bera test statistic is under the null hypothesis of normality. 11 The Bartlett’s periodogram-based test statistic is under the null hypothesis of white noise process.

20

Figure4: Residual test from Equation (1)--- Corporate SM Loans

Jarque-Bera (J) statistic = 0.17 Prob>J = 0.92 Bartlett’s (B) statistic = 1.52 Prob>B = 0.02

Figure5: Residual test from Equation (2)--- Corporate NPLs

Jarque-Bera (J) statistic = 8.99 Prob>J = 0.01 Bartlett’s (B) statistic = 0.56 Prob>B = 0.91

Figure6: Residual test from Equation (3)--- Consumer SM Loans

Jarque-Bera (J) statistic = 0.17 Prob>J = 0.92 Bartlett’s (B) statistic = 1.19 Prob>B = 0.12

Figure7: Residual test from Equation (4)--- Consumer NPLs

Jarque-Bera (J) statistic = 4.85 Prob>J = 0.09 Bartlett’s (B) statistic = 1.03 Prob>B = 0.24

0

1

2

3

4

5

6

7

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20

Series: Residuals

Sample 2003Q3 2012Q4

Observations 38

Mean 0.001360

Median 0.012191

Maximum 0.193792

Minimum -0.222747

Std. Dev. 0.094231

Skewness -0.048479

Kurtosis 2.688101

Jarque-Bera 0.168913

Probability 0.919012

0

2

4

6

8

10

12

14

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

Series: Residuals

Sample 2001Q1 2012Q4

Observations 48

Mean -0.000513

Median -0.000870

Maximum 0.389698

Minimum -0.294344

Std. Dev. 0.115102

Skewness 0.465014

Kurtosis 4.905604

Jarque-Bera 8.992561

Probability 0.011150

0

1

2

3

4

5

6

7

-0.2 -0.1 0.0 0.1 0.2

Series: Residuals

Sample 2002Q3 2013Q4

Observations 46

Mean 0.002492

Median 0.008272

Maximum 0.264349

Minimum -0.256506

Std. Dev. 0.116937

Skewness 0.009738

Kurtosis 2.698916

Jarque-Bera 0.174476

Probability 0.916459

0

2

4

6

8

10

-0.10 -0.05 0.00 0.05 0.10 0.15

Series: Residuals

Sample 2004Q1 2012Q4

Observations 36

Mean 0.000533

Median -0.014757

Maximum 0.167951

Minimum -0.107061

Std. Dev. 0.059561

Skewness 0.868047

Kurtosis 3.468898

Jarque-Bera 4.850831

Probability 0.088441

0.00

0.20

0.40

0.60

0.80

1.00

Cum

ulat

ive

perio

dogr

am fo

r re

sid_

sm_c

orp

0.00 0.10 0.20 0.30 0.40 0.50Frequency

Bartlett's (B) statistic = 1.52 Prob > B = 0.0201

Cumulative Periodogram White Noise Test

0.00

0.20

0.40

0.60

0.80

1.00

Cum

ulat

ive

perio

dogr

am fo

r re

sid_

npl_

corp

0.00 0.10 0.20 0.30 0.40 0.50Frequency

Bartlett's (B) statistic = 0.56 Prob > B = 0.9090

Cumulative Periodogram White Noise Test

0.00

0.20

0.40

0.60

0.80

1.00

Cum

ulat

ive

perio

dogr

am fo

r re

sid_

sm_c

on

0.00 0.10 0.20 0.30 0.40 0.50Frequency

Bartlett's (B) statistic = 1.19 Prob > B = 0.1177

Cumulative Periodogram White Noise Test

0.00

0.20

0.40

0.60

0.80

1.00

Cum

ulat

ive

perio

dogr

am fo

r re

sid_

npl_

con

0.00 0.10 0.20 0.30 0.40 0.50Frequency

Bartlett's (B) statistic = 1.03 Prob > B = 0.2382

Cumulative Periodogram White Noise Test

21

Considering the SM corporate loan equation, a rise in the growth of effective

borrowing rate, reflecting the fund raising to finance credit growth in subsequent periods, by

10% from last year will lead to an ascent in SM corporate loan of 4.7% in the next 8 quarters.

Then, an increase in the corporate credit growth of 1% will raise growth of SM corporate loan

in the next 5 quarters by 0.95%, as an expansion in corporate credit should result in an

increased opportunity for corporate loans going bad. As for the macroeconomic factors, if

real GDP growth decreases by 1%, SM for corporate credit will grow 1.46% on average

relative to same period of last year. Reduction in the growth of real GDP affects the revenue

of the corporate sector and consequently leads to delayed payments. Similar to real GDP

growth, increasing in oil price index by 1% brings the year-on-year growth of SM loans up

slightly by 0.35% in the next 3 quarters also.

For the case of NPL corporate results, there are four significant explanatory

variables in the forecast equation. First, a rise in the excess liquidity growth by 1% will lead

to a jump in the growth NPL for corporate credit of about 0.36% in the next 2 years (or 8

quarters). Second, due to abundant liquidity in the banking sector, an increase in corporate

credit growth of 1% will lead to a year-on-year growth in NPL of 1.16% in the next 5

quarters. Third, the model implies that the growth in the corporate NPL of 0.45% is a result

of a drop in real GDP growth by 1% in the previous quarter. This can significantly affects

the ability to pay of corporate borrowers, which is confirmed by a 1% growth of aggregate

debt service ratio (ADSR) for the corporate sector leading to an increase in NPL of 1.08% in

the same period.

In addition, the selected model of SM consumer credit consists of the effects of the

growth in aggregate debt service ratio-consumer, real GDP growth, consumer price index,

the consumer credit growth and the liquidity position (via the rise in effective borrowing

rate). To start, SM consumer loan growth will increase by 1.42% in the next 6 quarters if the

effective borrowing rate grows by 10% from last year. Then, when consumer credit growth

rises by 1%, SM consumer credit will move upward by 1.21% in the next 3 quarters. Later, if

the macroeconomic conditions deteriorate, the amount of loans in arrear will edge up.

Clearly, a 1% increase in consumer price index, such as the inflation rate rising from 2 to

3%, will raise SM consumer credit in the next quarter by 6.46%. This is because an upward

movement of CPI indicates that household expenditure will later increase from rising in

aggregate price level, so consumers tend to struggle more to meet the debt payment. In

22

addition, rising in CPI translates to an increase interest rate trend, which consequently puts

more pressure on the debt serviceability. This is confirmed by the fact that the growth of SM

consumer credit in the next consecutive quarter will go up by 0.45% if the growth of

consumer’s aggregate debt service ratio (ADSR) rises by 1%. Finally, a decrease in real GDP

growth of 1% will increase SM consumer loan further by 1.40% also in the following quarter.

Last but not least, the growth of NPL consumer loans depends on four significant

variables. First and foremost, a rise in effective borrowing rate growth of 10% will increase

NPL consumer credit growth by 0.84% in the next 8 quarters. Then with more liquidity, the

model entails that NPL consumer loan will raise by 1.26% in the next 5 quarters if consumer

credit grows by 1% year-on-year. Then, a decline in real GDP growth by 1% will lead to an

increase in the consumer NPL in the next 2 quarters by 1.15%, while the 1% growth of

consumer’s ADSR will lead to a 0.14% increase in the NPL in the subsequent quarter.

Section 5: Forecasting the Corporate and Consumer Loan Quality

This section presents the results of the forecast in corporate and consumer loan quality,

using the scenario of real GDP growth of 1.4% in 2014 on the back of weaker domestic

demand and political uncertainty during the first half of the year while the overall loan

growth in the banking industry is assumed to be around 7% in the same time period

(detailed scenario is presented in the Appendix). Policy implications will also be discussed.

5.1 Forecasting Results

As mentioned above, we used the models that had specification similar to Table 11 in order

to forecast SM and NPL for both the corporate and consumer loans at the end of 2014

(Figure 8-11). The result of our forecast on the growth of the amount SM loans and NPL for

both sectors is indicated by a thick dotted green line in each of the figure12 and the thick

solid blue line represents the actual growth rate of SM loans and NPL of corporate and

consumer loans.13 A green shaded area represents the 95% confidence-interval ban of

forecasting.

12 This is an accuracy testing via out-of-sample test by using data from first quarter of 2013 to fourth quarter of 2014. 13 The actual data starts from fourth quarter of 2000 to first quarter of 2014.

23

1. Forecast results of the growth of SM corporate loans From the first equation in

Table 11, the forecast value is slightly higher than the actual data on the growth of SM

corporate loans, which may be a result of the precautionary risk management policies of the

banks that take charge on the borrowers before they become SM loans. In essence, the

forecast yields a decreasing trend throughout the projection period (Figure 8). The SM ratio

of corporate loans is expected to be 3.03% at the end of 2014, increasing slightly from the end

of 2013. This may be a consequence of a reduction of effective borrowing rate that reflects

the waning competition for deposits and shows the reluctance of financial institutions to lend

in time of the economic and political uncertainty.

Figure 8: The Forecast Growth of SM Corporate Loans Overtime (Left) and in 2013/2014 (Right)

2. Forecast results of the growth corporate NPLs Using the second equation in

Table 11, our forecast growth in corporate NPLs is quite in line with the existing growth

rate, though slightly overestimating it. This comes as no surprise since banks generally try

to manage corporate loans before they deteriorate to be NPLs. Our overestimation indicates

that, given the lending practices and macroeconomic conditions in previous quarters, the

NPL growth should be higher than it actually is but, with good risk management, the

equilibrium comes out lower than what it should have been. As for the trend, corporate

credit NPL growth is likely to increase at a slower pace in the first half of 2014 before rising

during the remaining months (Figure 9) owing to a tightening in liquidity in the first half of

2012. However, in the second half of 2014, it should pick up significantly due to

subsequently worsen debt servicing ability in the business sector. However, the expansion of

corporate loans, which outpaces the growth in corporate NPLs, should lead to a slightly

lower NPL ratio of 3.16% at the end of 2014 when compared to the same period in 2013.

Actua l Foreca s t Actua l Foreca s t

Q1 20.43% 18.95%

Q2 27.63% 40.99%

Q3 2.30% 30.71%

Q4 11.99% 32.66%

Q1 9.52% 26.39%

Q2 11.14% 17.13%

Q3 16.80% 11.10%

Q4 7.18%

2 0 1 4

3.03%

(End of

2014)

SM-

Corpora te

Growth Ra tio

2 0 1 3

3.02%

(End of

2013)

3.56%

(End of

2013)

24

Figure 9: The Forecast Growth of Corporate NPL Overtime (Left) and in 2013/2014 (Right)

3. Forecast results of the growth SM consumer loans Our prediction using the third

equation in Table 11 closely mimics the real value (Figure 10). From the figure, the growth

of SM consumer loan is expected to slow down in 2014, after a sharp increase throughout

2013. This slower pace of SM consumer loan growth in 2014 stems from the dampened

competition for deposits and slower consumer credit expansion in late 2013 and early 2014.

Hence, the SM consumer credit ratio at the end of 2014 is expected to stand at 3.06%, edging

up quite significantly from the same period last year.

Figure 10: The Forecast Growth of SM Consumer Loans Overtime (Left) and in 2013/2014 (Right)

4. Forecast results of the growth of consumer NPLs Though sharing the same

trend, the result of our prediction is much lower than the actual data (Figure 11), owing to

sharp surge of consumer NPLs in recent years because of the populist policy that boosted the

consumer credit in the past few years. Going forward, we predicted that consumer NPL

growth would dampen slightly in the second quarter of 2014 but would surge again in the

third and fourth quarter of the year. This surge is consistent with the slowdown of the

economy and the high amount of household debt accumulated over the past years. At the

Actua l Foreca s t Actua l Foreca s t

Q1 -9.68% 3.50%

Q2 -3.73% 3.83%

Q3 -4.78% 7.89%

Q4 -1.83% 8.76%

Q1 2.38% 4.38%

Q2 1.32% -3.90%

Q3 3.05% 1.29%

Q4 5.44%

2 0 1 3

3.20%

(End of

2013)

3.54%

(End of

2013)

2 0 1 4

3.16%

(End of

2014)

NPL-

Corpora te

Growth Ra tio

Actua l Foreca s t Actua l Foreca s t

Q1 22.50% 30.12%

Q2 39.82% 38.63%

Q3 36.52% 38.98%

Q4 39.48% 32.21%

Q1 28.86% 31.95%

Q2 22.31% 24.51%

Q3 20.62% 23.39%

Q4 21.28%

2 0 1 4

3.06%

(End of

2014)

SM-

Consumer

Growth Ra tio

2 0 1 3

2.72%

(End of

2013)

2.57%

(End of

2013)

25

end of 2014, the NPL ratio of consumer loan should be at 1.88%, up from 1.72% at the end of

2013.

Figure 11: The Forecast Growth of Consumer NPL Overtime (Left) and in 2013/2014 (Right)

Overall, our models yield a decent forecast when compared to the actual values.

However, there are a few points worth noting about the possible shortfall of the model

development method we employed. First, the conceptual framework we constructed reflects

a common banking practice and bank behavior in Thailand. The practice in other countries

may be similar or different than this framework. Hence, it is the duty of the researcher to

come up with the appropriate framework to be used in conjunction with the econometric

approach so as to achieve the best results. Second, many of the variables we encountered did

not have sufficiently long-enough time-series to be used in the model development and so one

should always re-test and re-estimate the model to search for better predictors once the data

collection is deemed sufficient. Third, one should always be aware of the ever-changing

nature of banking business and use it to re-develop the model so that the model will always

possess the ability to capture the true dynamic in the banking sector.

5.2 Financial Stability and Bank Supervisory Policy Implications

The forecast models developed can be used not only to supervise banks more effectively but also

to promote financial stability. The essential benefit of this forecast model development is to

provide a systematic numerical method which links the macroeconomic and bank-specific

factors to the future loan quality.

Actua l Foreca s t Actua l Foreca s t

Q1 17.61% 2.76%

Q2 19.88% -5.83%

Q3 21.01% 7.45%

Q4 26.43% 15.38%

Q1 31.26% 20.41%

Q2 30.26% 8.73%

Q3 31.63% 16.08%

Q4 17.72%

Growth Ra tio NPL-

Consumer

1.57%

(End of

2013)

2 0 1 4

1.88%

(End of

2014)

2 0 1 3

1.72%

(End of

2013)

26

For the purpose of enhancing financial stability, these models can be used at least, but

not limited to, two ways. First, it provides a way to forecast the loan quality in the banking

system. This forecast can then be used to assess whether the banking industry has sufficient

risk absorption capability, namely loan loss provision and, to some extent, the capital adequacy.

In addition, these models also identify the key determinants of the loan quality in each part of

the real sector, corporate or consumer, in the future period, such as credit growth, real GDP

growth, price indices, and debt serviceability. This feature of the models provides policy

makers with early-warning indicators of loan quality deterioration should they witness a

decrease in GDP growth, an acceleration of oil and consumer price index, or a sharp increase in

debt service ratio, for example. Once these negative signals are registered, then banks can

then be warned to brace for impact; for instance, tighten the credit standard or increase

provision. Using these models as possible warning tools can enhance the financial stability in

the banking sector.

In addition, these models can also be used as effective stress-testing tools for bank

supervision. Since these models offer a method to link the bank-specific and macroeconomic

factors to the future loan quality, they can also be used to forecast the loan quality in an

adverse situation, depending on the macroeconomic factors input into the model. For a severe

downturn scenario, such as negative GDP or significant worsening in borrower’s debt

serviceability or extreme price increase, the models can offer the estimation of what the loan

portfolio will look like and whether banks have enough provision and capital to cushion for the

worst case. In addition, these models tend to yield conservative estimates of the quality of

loans (or the loan characteristics that are worse than they actually are) because the predicted

SM/NPL growth is driven by the bank-specific and macroeconomic factors and has not yet

taken into account banks’ risk management actions that tend to improve the loan quality,

unless there is an unprecedented structural shift in bank lending behavior, such as the populist

policy we see in the Thai banking sector last year.

Concluding Remarks

This paper provides the systematic way of forecasting the quality of loans in the corporate and

consumer sectors. Using the data in the Thai banking sector from the past 15 years, we were

able to pinpoint the main determinants of both the special-mentioned (SM) loans and the non-

27

performing loans (NPL) for both sectors. Although our findings share similarities to the results

from the existing literatures on this subject, we were able to determine the important drivers

and to tailor-made our prediction for the SM/NPL in each specific sector, corporate and

consumer. Our results showed that, although the quality of each type of loans—corporate and

consumer—share similar drivers such as factors reflecting excess liquidity and loan growth as

well as real GDP growth, there are differences in determinants as well. Price variables such as

oil price and CPI seem to only affect SM and not NPL, for example. These particular

characteristics are key to understanding the dynamics of the SM and NPL in each real sector

and how they are linked to bank-specific and macroeconomic factors. With these forecasting

models, bank supervisors can use the determinants of the quality of loans as an early warning

signal to gauge the health of the bank lending business in a forward-looking manner, and, in

addition, use them as potential stress-testing tools that link the possible adverse scenarios to

the loan quality of the banking sector so as to prepare the appropriate policy in an effective and

timely fashion.

28

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30

Appendix

A. Estimating the Effective Lending Rate in the Thai Banking System

We compute a time series of effective lending rates to track the movement in the

true cost of bank loans. The standard formula reflects the amount of interest receipts from 1

monetary unit amount of lending:

However, to calculate the effective lending rates facing the corporate and household

sectors separately is a little tricky because the BOT database does not have the

disaggregated corporate and household sector data on interest receipts. We need to make

additional assumptions to construct a reasonable time-series for corporate and consumer

effective lending rates as follows:

1. The effective lending rate calculated at aggregate level is the weighted average of

corporate and consumer effective lending rates.

2. The effective lending rate in the corporate sector is arbitrary set at the risk-free

rate of return given the same remaining maturity as the average remaining

maturity of corporate loans. In the Thai banking sector, the observed maturity of

corporate loans is about 5 years, we therefore use the 5-year government bond

yield as a proxy for the corporate effective lending rate.

The corporate interest receipt is then approximately calculated by the multiplication

of the 5-year government bond yield and the corporate loan outstanding amount. We next

calculate the household interest receipts by subtracting the amount of corporate interest

receipts from the total interest receipts retrieved from the BOT database. Finally, the

calculated household interest receipts will be used to calculate the effective lending rates of

consumer loans.

31

B. Estimating the Effective Borrowing Rate in the Thai Banking System

The effective borrowing rates measure banks’ funding cost in terms of the amount of

interest expense per 1 monetary unit amount of liabilities outstanding. The standard

formula is

C. Estimating the Aggregate Debt Service Ratio

The Aggregate Debt Service Ratio (ADSR) is calculated following the formulas

suggested by Drehmann and Juselius (2012) which broadly based on the formula banks use

for calculating monthly payment of term loans.

The ADSR in the corporate sector is computed as follows:

where Dt = corporate debt stock, it = effective lending rate in corporate sector,

mt = average remaining maturity of corporate loans, and Yt = nominal GDP.

The ADSR in the household sector is computed as follows:

where = household debt stock,

= consumer effective lending rate, = average

remaining maturity of consumer loans, and = disposable income.

It is worth pointing out that our measure of ADSR described above is likely to deliver

a smaller value compared with the conventional measure of debt service coverage ratio

(DSCR) that banks normally use when they assess the default risk of individual borrowers.

This is because the aggregate income, which appears in the denominator of our ADSR

formulas, technically represents income receivable by all resident institutional units

whether or not they are bank loans borrowers.

32

D. Details on the Scenario Used in the 2014 Forecast

Q1 Q2 Q3 Q4

CREDIT_CONS 10.69% 4.41% 4.56% 4.95%

CREDIT_CORP 9.41% 5.68% 7.92% 8.45%

DSR_CONS 7.66% 0.39% 4.13% 6.10%

DSR_CORP 8.48% 3.68% 4.67% 3.68%

INF 2.00% 2.67% 2.82% 2.67%

RGDP 1.15% 0.78% 1.55% 2.02%

WTI 3.78% 4.04% -7.50% 0.58%

2014(%yoy)