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 [email protected], [email protected], [email protected] or [email protected]. 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 Please do not quote without permission from the authors.
2
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.
4
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.
5
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)