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Macro economic determinants of credit risks in the asean banking system

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

ERASMUS UNVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS

VIETNAM – THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

MACROECONOMIC DETERMINANTS OF
CREDIT RISK IN THE ASEAN BANKING
SYSTEM

BY

NGUYEN CHI THANH

MASTER OF ARTS IN DEVELOPMENT ECONOMICS


HO CHI MINH CITY, DECEMBER 2016


UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

MACRO ECONOMIC DETERMINANTS OF
CREDIT RISK IN THE ASEAN BANKING
SYSTEM
A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

NGUYEN CHI THANH

Academic Supervisor:
DR. NGUYEN VU HONG THAI

HO CHI MINH CITY, DECEMBER 2016


DECLARATION
I declare that the wholly and mainly contents and the work presented in this thesis
(Macro Economic Determinants of Credit risk in the ASEAN Banking System) are
conducted by myself. The work is based on my academic knowledge as well as my
review of others’ works and resources, which is always given and mentioned in the
reference lists. This thesis has not been previously submitted for any degree or presented
to any academic board and has not been published to any sources. I am hereby
responsible for this thesis, the work and the results of my own original research.

NGUYEN CHI THANH



i


ACKNOWLEDGEMENT
Here I would like to show my sincere expression of gratitude to thank my supervisor,
Dr. Nguyen Vu Hong Thai for his dedicated guideline, understanding and supports
during the making of this thesis. His precious academic knowledge and ideas has
motivated me for completing this thesis.
Besides, I would like to express my appreciation to the lecturers and staff of the Vietnam
– Netherlands Program at University of Economics Ho Chi Minh city for their
willingness and priceless time to assist and give me opportunity for this thesis
completion.
Next, I would like to thank all of my classmates for their encouragement and their hard
work, which become a good example for me to do the thesis. I wish all of us will
graduate at the same date.
Lastly, I would like to express my love to my families for their unlimited supports which
has led to the completion of this course research project.

ii


ABBREVIATION
ASEAN: Association of Southeast Asian Nations
DGMM: the difference generalized method of the moments estimator
FE & RE: Fixed-effect and Random-effect estimator
GDP: Gross domestic product
NPLs: Non-performing loans
OECD: Organization for Economic Cooperation and Development
OLS: Ordinary Least Square
SGMM: the system generalized method of the moments estimator

iii


ABSTRACT
The impact of credit risk, which is caused by the increase in the non-performing loans
(NPLs), on the performance and stability of banking system as well as economic
activities have recently raised many interests from researchers and policy makers.
Motivated by the close connection between the NPLs and macroeconomic
environments as proposed by many researchers, this paper will empirically examine the
determinants of non-performing loans in commercial banking systems of the five
ASEAN countries in the period of 2002 to 2015. The research uses a sample of 162
banks in these countries with 11 variables of macroeconomic and bank-specific factors
and applies the System Generalized Method of Moments estimator (SGMM) for
dynamic panel models.
The empirical results in this paper indicate that the movement of NPLs in the
commercial banks of the five studied countries is associated with both macroeconomic
variables and bank-specific factors. For the macroeconomic condition, an increase in
unemployment rate and the appreciation of domestic currency are found to significantly
increase the NPLs. In addition, bank with higher returns on asset and leverage ratio and
low ratio of equity to total assets will have lower rate of NPLs. Moreover, with the
application of additional statistical analyses, the results indicate that the findings of the
main model of this paper are consistent and robust.

iv


CONTENTS
DECLARATION....................................................................................................................... i
ACKNOWLEDGEMENT .......................................................................................................ii
ABBREVIATION .................................................................................................................. iii
CONTENTS.............................................................................................................................. v
APPENDIX ............................................................................................................................... 1
LIST OF TABLES ................................................................................................................... 2
CHAPTER 1: OVERVIEW OF RESEARCH ...................................................................... 3
1.

Introduction: ..................................................................................................................... 3

1.1

Backgrounds:................................................................................................................. 3

1.2

Problem statements: ..................................................................................................... 4

1.3

Research objectives:...................................................................................................... 5

1.4

Research questions: ...................................................................................................... 6

1.5

Hypothesis of the study: ............................................................................................... 6

1.6

The importance of research: ........................................................................................ 6

1.7

Structure of Research: .................................................................................................. 8

CHAPTER 2: LITERATURE REVIEWS ............................................................................ 9
2.1

Theoretical reviews: ...................................................................................................... 9

2.2

Empirical reviews: ...................................................................................................... 13

2.3

Conclusion: .................................................................................................................. 22

2.4

Research Hypothesis:.................................................................................................. 23

CHAPTER 3: DATA AND METHODOLOGY.................................................................. 27
3.1

Data collection: ............................................................................................................ 27

3.2

Econometric methodology – The NPLs measurement: ........................................... 28

3.3

The variables definition and measurement: ............................................................. 32
v


3.3.1

The dependent variable – the Non-performing loans: .............................................. 32

3.3.2

Macroeconomic variables: ........................................................................................ 32

3.3.3

Microeconomic variables – bank-specific determinants: ......................................... 34

3.4

Econometric strategy – The system GMM estimator:............................................ 38

CHAPTER 4: RESULTS AND DISCUSSIONs .................................................................. 40
4.1

Summary statistics: ..................................................................................................... 40

4.2

Unit root tests: ............................................................................................................. 41

4.3

Empirical results: ........................................................................................................ 41

CHAPTER 5: OTHER ANALYSIS AND ROBUSTNESS CHECK ................................ 51
CHAPTER 6: CONCLUSION, POLICY IMPLICATIONS & LIMITATIONS OF THE
REASEARCH ........................................................................................................................ 56
6.1

Main findings: ............................................................................................................. 56

6.2

Policy implications: ..................................................................................................... 57

6.3

Limitations: ................................................................................................................. 58

6.4

Future research recommendation: ............................................................................ 58

REFERENCES ....................................................................................................................... 59
APPENDIX ............................................................................................................................. 66

vi


APPENDIX
Appendix 1: Number of banks in each country
Appendix 2: xtabond2 model selection criteria
Appendix 3: Correlation of variables
Appendix 4: Additional analyses and Robustness checks
Appendix 5: Additional analyses and Robustness checks

AP

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LIST OF TABLES
Table 1: Description of variables
Table 2: Summary statistics
Table 3: Unit root tests for NPLs estimations variables
Table 4: Results with SGMM and fixed-effect estimations

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CHAPTER 1: OVERVIEW OF RESEARCH
1. Introduction:
Banks are the financial intermediaries who play an important role in the development
of a country. In the financial sector, a commercial bank is a funding channel, which can
allocate the cash flows in the economy through their financial services as well as
traditional services (taking deposits and make business loans). Whenever a loan is
approved, banks gain profits from the borrowers by loan interest rate and services fees.
However, banks would expose to credit risk from this service because borrowers could
suddenly lost their abilities to pay the loan in time, namely the non-performing loans
(NPLs). The main reason for that comes from the movement of the macroeconomic
environment, which directly impacts to the revenues and business activities of bank
borrowers.
Therefore, this paper will conduct an examination about how the economics
determinants affect the bank credit risk. In this chapter, the backgrounds, problem
statements, research objectives, research questions, significance of the research and the
layouts will be discuss around this issue.
1.1

Backgrounds:

Along with the expansion of the economy as well as financial liberalization process in
developing countries, the financial sector have been grown with surprising rate.
Besides, the improvements of technology and management procedures help banks
making decisions to grow in financial markets. However, the occurrences of two big
economic recessions in 1997 and 2007 have significantly affected the banking systems
in developing countries. It associated with the deteriorated quality of bank assets due to
a massive increase in the NPLs, which has a close connection to the economic cycle.
When borrowers are unable to fulfill their obligations to the loans, it would become
credit risk of banks, which is one of the significant risks among many kinds of risks that
most of the commercial banks are exposed. Credit risk is distinguished by two
components which are systematic and unsystematic credit risk (Castro, 2013) and in
fact, it is very hard to set an efficient credit risk management policy and procedure for
the banking system. This is because of the unpredictable natures of economic
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environment that have the impacts to banking-specific factors as well as risks in banking
industry. Therefore, this impact has raised many serious concerns to researchers and
policy makers to understand the relation between credit risk and the business cycle in
order to ensure the stability of a banking system.
1.2

Problem statements:

The beginning of recent crisis exploded since the collapse of the Lehman Brothers, the
fourth-largest U.S. investment bank. It is because of the subprime mortgage crisis, many
loan defaults makes the bank illiquidity to prevent from the crisis. Moreover, the
depositors do a massive withdraw their money out of the bank as they lost their
confidence in the banks. As a result, the bank do not have enough money to do business
and indirectly cause the Washington Mutual bankruptcy. Since the Lehman Brother do
business around the world, it also leads banks in many countries face the credit risk.
Making loan is the traditional function provided by the bank but it also causes the credit
risk, which come from the borrowers who are inability to pay back the loans as they
promised. Following to Castro (2013), the increase of bad loans in banks’ balance sheet
leads to the problem of liquidity and insolvency, which is the signal for banking crisis.
In the case of illiquidity and insolvency, banks will lose their abilities to pay to their
debtors and fail to meet their obligations. As a shock have happened, banks will be
considered as loss and could be forced to shut down. From there, both banks and their
debtors will be struggled by loss and it will effect to economy. Therefore, it is crucial
to raise awareness to the credit risk in order to determine the cause of risks and prevent
banks from illiquidity and insolvency problems.
Consequently, if banks need to control the credit risk efficiently, they must understand
the factors that cause the credit risk. However, as suggestion of Garr (2013), the nature
of macroeconomic environment is unforetold and also associates with various
microeconomic factors, which makes banks’ credit risk management become a very
complicated and tough objective in order to manage the credit risk. Lack of knowledge
and experience in credit risk management can leads banks to more serious risks.
Besides, Ratnovski (2013) points a view that credit risk management may become a
burden rather than a solution for banks because it could drain a certain amount of
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resources and time of banks. For more specific, the managers also have to put many
effort in knowledge and experiences to deal with it and it could raise the administrative
cost while a low return on highly liquid assets cannot be compensated the cost. A credit
risk program requires time to take effect and resources (such as capital and labors) to
be employed and managed for a long time in order to prevent banks from a sudden
attack of credit risk. Therefore, if the credit risk policy and procedure are not based on
the real situation of the factors that impact to credit risk, they will be loss because their
money and time for the costly program are wasted, but also they will suffers a
significant raise of the credit risk problems.
As a result, it has led to many interests of researchers and policy makers in finding the
factors that can lead to the bank credit risk, so that they can understand these factors
and build an effective credit risk management to limit the probability of credit risk.
1.3

Research objectives:

The paper will examine the influence of macroeconomic environment factors to the
non-performing loans ratio (NPLs) in the five countries of ASEAN (Indonesia,
Malaysia, Philippine, Thailand and Vietnam) covering a 13-year period of time from
2002 to 2015, which are in the same development rate in the area. However, due to the
lack of NPLs data at countries level, the NPLs ratio of individual commercial bank will
be examined and in order to prevent from bias and to ensure the model consistent, other
bank-specific factors will be adopted in this paper, there are 162 commercial banks’
information collected. The data for macro determinants is collected from the World
Bank data while bank-specific ones is from the Bank Scope-Fitch’s International Bank
Database. Finally, the objectives of this paper are as follows:
-

To examine the impacts of macroeconomic determinants to the NPLs ratio of the
commercial banks in the five countries of ASEAN.

-

To study the nature of the commercial banks’ specific factors toward the NPLs
in the five countries of ASEAN.

-

To find an appropriated method to measure the relationship between
macroeconomic factors and the NPLs ratio
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-

To ensure the consistent of the chosen method through the application of
robustness check and additional analytical tests.

1.4

Give recommendation to policy makers.
Research questions:

The questions of this paper will be raised to match with the objectives above, these are
as follows:
-

Which is the macroeconomic factor that significantly effects the NPLs ratio in
the commercial banks of the five ASEAN countries?

1.5

How do banks’ management in these countries affect their NPLs?
Hypothesis of the study:

This paper will examine the impacts of five macroeconomic factors to the NPLs rate,
thus the five hypotheses are as follows:
H1: Gross Domestic Product (GDP) has a significant negative relationship with
bank credit risk in the five studied ASEAN countries.
H2: Interest rate has a significant positive effect on bank credit risk in the five
studied ASEAN countries.
H3: Inflation rate has a significant impact on bank credit risk in the five studied
ASEAN countries.
H4: Exchange rate appreciation has a significant relationship with bank credit
risk in the five studied ASEAN countries.
H5: Unemployment rate has a significantly positive impact on bank credit risk
in the five studied ASEAN countries.
1.6

The importance of research:

Numerous existing papers are conducted to examine the credit risk determinants within
a country or a category of countries (such as in Europe, OECD or developed countries)
or a limit of determinant category. In this study, the potential determinants of bank
credit risk, which are applied in the model, are 11 factors (including five main
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macroeconomic determinants and six additional bank-specific factors). This is also the
first paper that examines the impacts of these variables on the NPLs of commercial
banks in five ASEAN countries (Indonesia, Malaysia, Philippine, Thailand and
Vietnam) from 2002 to 2015. In addition, due to the nature of the data sample in this
paper and the limit of related research papers, the research methodological design will
follow an extensive approach through the dynamic panel data econometric techniques
that serve as a robust cross-validation of the results as well as several additional analysis
and robustness tests.
The results of the research will assist a better understanding into the key factors of credit
risk in the commercial banks of studied countries. In addition, the paper will propose
useful information in explaining what cause the bank credit risk and in evaluating the
performance of the banks toward the NPLs. According to Demirguc-Kunt and
Detragiache (1998), banking system of a country with high inflation rate,
unemployment and interest rate seem to have higher bank credit risk and banking crisis
would be easily occur. Therefore, this study will give more understanding in the
connection of the economic developments and the credit risk as well as the information
on how the banks’ operation and the economic condition within these countries is.
For more specific, the investor and depositor will know how and when the bank
performances are in the stable and sound condition through knowing nature of the
economic and bank specific factors. With this knowledge, their banking activities are
much easier to make exact decisions to use their fund and prevent from bad investments.
In addition, the result will provide to bank managers an efficient loan and credit risk
management policy with the information of which economic and bank specific
determinants of the bank influence credit risk. Therefore, with information such as
increase in the inflation rate, interest rate or domestic currency appreciation, banks
could issue an appropriated approach to monitor, evaluate and control for bank risk
exposures with a more precise way. Consequently, an efficient credit risk management
policy will help bank management more effective in capital allocation, banking
performance, operating cost and profitability.

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1.7

Structure of Research:

This research paper is organized in six chapters. Chapter 1 is the introduction and
overview the general idea of the study context. Chapter 2 gives the literature reviews of
the previous studies in both theoretical and empirical frameworks for the effect of the
macroeconomic factors on the bank credit risk and it also describes the proposed
hypotheses development for the study. Chapter 3 consists of the data and research
methodology which includes the research methodology, data collection methods, the
model description and variable description.
Chapter 4 will present and interpret the results of the econometric analysis with respect
to the research’s theoretical and empirical analyses, which are linked to the hypotheses
of the research paper. It will show the relationship of the economic factors and the NPLs
ratio of banks. Furthermore, chapter 5 conducts additional analysis and robustness test
in order to examine the consistent of the estimator and finally chapter 6 will suggest
some policy implications, the limitations and the final conclusion of this thesis.

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CHAPTER 2: LITERATURE REVIEWS
2.1

Theoretical reviews:

Credit risk is defined as the risk from borrowers who have lost their ability to pay loans
back to lenders partially or totally. In recent years, many banks in the world experienced
substantial losses and reduction of capital provision due to rapid deterioration in assets’
quality. This not only increased banks’ exposure to economic crisis but also restricted
bank lending ability with both direct and indirect consequences to the financial stability
and economic activities. Therefore, the need for the credit risk analysis is crucial
because it is not only to ensure a stable banking system for a prosperous economic
growth but also can raise the awareness to the regulatory authorities to prevent a
possible crisis in the future. Castro (2013) identifies factors affecting systematic and
unsystematic credit risk separately. The factors influencing the systematic credit risk
are: macroeconomic factors, changes in economic policies and political changes or
changes in the goals of leading political parties. While unsystematic credit risk is
affected by specific factors: (i) individual-specific factors namely individual
personality, financial solvency, capital and credit insurance; (ii) company-specific
factors namely management, financial position and reporting, sources of funds, their
ability to pay the loan and specific factors of the industry sector.
2.1.1 Business Cycle and Risk:
The relationship between the economy and financial system has been argued in a
number of theories. Within the framework of business cycles, the connection between
macroeconomic factors and loan quality is emphasized by linking to the movement of
business cycle with financial vulnerability and banking performance. Specifically,
Messai and Jouini (2013) offers a theoretical models from Williamson (1987), which
emphasizes the nature of credit risk and proposes the impact of business cycle to the
financial sector of a country. In addition, Messai and Jouini (2013) also summarized
theoretical review for this relation, the phases of the business cycle relating to banking
performance have been studied in order to express the relationship between the
macroeconomic environment (such as the yearly GDP growth, the real interest rate, the
annual inflation rate, the exchange rate and the unemployment rate) and the quality of
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loans. During the economic expansion phase, there are only a relatively small
proportion of bad loans, borrowers are confident to have adequate income or more cash
held to repay for their loans in time of deadlines. Therefore, lenders may not pay much
attentions to the credit standards and allow more risk (Koch and McDonald, 2003) or
the increased ability of creditors to repay loans leads to reducing of credit risk for
lenders (Salas and Saurina, 2002). However, when economic conditions worsen, the
studies of Jiménez and Saurina (2006) for Spanish banks and Bohachova (2008) for
members of Organization For Economic Cooperation And Development (OECD) reach
the conclusion that banks are vulnerable to adverse selection in their financial decisions
and moral hazard behavior of their creditors so that this causes an increase in risk of
loans.
2.1.2 Interest Rate and Risk:
It is also argued that higher interest rate, mostly induced by monetary policy, associates
highly with debt burden due to higher interest payment, which leads to high rate of
NPLs. For instances, following the theory of asymmetric information, borrowers are
able to face adverse selection problem as interest rate surges, it is call “bad risk”
(Bohachova, 2008), the result of loan applicants is probably adverse with the borrowers’
selection. In order to pay for their loans, instead of using the loans on safe projects with
low returns, creditors tend to have strong motive to riskier projects with much more
higher income. In addition, when interest rate increases, banks will earn more returns
from new loans and floating interest loans while borrowers have to stand with higher
payments and then the probability of increase in credit risk would occur on banks’
balance sheets (Demirguc-Kunt and Detragiache, 1998). However, from the view of the
bank side, banks diversify their financial roles in the market, they conduct asset
transformation and they lend to a large number of borrowers as well as borrow from a
large number of depositors (Williamson, 1987). Moreover, in some countries with
interest rate liberalization, because of rises in the costs of funds and the culture of highrisk behaviors; higher rates are charged to high-risk borrowers in order to mitigate risks,
hence banks overall risk exposure increases more (Fofack, 2005).

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When the economy went down, the return on bank assets deteriorates more than the rate
that must be paid on depositors and banks would reduce profits or face losses. As bank’s
assets are composed of long-term fixed interest rate loans, thus banks cannot handle for
the return on assets quickly enough. As a result, banks would raise short-term lending
interest rate in order to deal with their liability payments (Mishkin, 1996)1. In addition,
when borrowers are likely to be exposed to debt burden, banks also face to a large risky
loan portfolio, thus a higher net interest margin is required to compensate the higher
risk of default (Ahmad and Ariff, 2007), which leads to a systematic banking sectors
problems.
2.1.3 Inflation and Risk:
Another factor that should be considered is the inflation, which is caused by the
restrained money supply growth and the disposed nominal depreciation of the domestic
currency; inflation influences to both banks’ decisions and borrowers’ behaviors to
loans. For more specific, inflation is unpredictable and an increase in inflation makes
the prices of goods and services go up, thus the volatility of firms’ profits will rises as
well as their debt obligations (Peyavali, 2015). An increased rate of inflation also have
a negative effect on real rates of return on bank assets as well as incomes of existing
borrowers thereby making the quality of previously extended loans worse and resulting
to credit rationing (Bohachova, 2008). In addition, if variable loan rates are applied,
high inflation leads borrowers to adverse selections because banks will prefer to adjust
the lending rates to keep their real returns stable or the government conducts monetary
policy to fight against inflation (Nkusu, 2011). On the other hand, disinflation also
affects loan quality because in a previously high-inflation economy, there are high real
interest rate, which makes the earnings of borrowers declined and encourages risks
similar to a rise in nominal interest rate (Mishkin, 1996).
2.1.4 Exchange Rate and Risk:
Exchange rate, which indicates the value of domestic currency in terms of another, is
also one of macroeconomic sources of economic instability as well as bank risk

1

Most of the United States banking panics follow an increase in short-term lending interest rates.

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exposure. Because of no currency matching between the income of borrowers and their
loan debts, for loans nominated in foreign currency, depreciation of domestic currency
increases debts and debtors’ incapacity to pay the loans and then banks would face to
loan defaults (Curak et al., 2013). When domestic currency depreciates, the rate of
impaired loans would increase, especially for loans nominated in foreign currency.
Credit risk for bank loans is likely to increase to importers and decrease to exporters,
thus bank’s overall risk exposure will be determined by its net vulnerability to exporting
or importing borrowers. As the foreign currency appreciates, it costs more to purchase
foreign goods and services, thereby more units of domestic currency are required to
secure the same quantity of imported goods and services than before. Accordingly, the
demand of financial support for bank credit will increase to cover the raising costs and
it would reduce the firm’s profitability, then firm will encounter the problem to serve
interest and principal of loans (Poudel, 2013). On the other side, Bochahova (2008) also
expresses two theoretical interactions of exchange rate movement on banks’ credit risk.
For more specific, banks’ volatility could increase due to the domestic currency
depreciation when banks liabilities denominated in foreign currencies are higher than
their foreign exchange assets. In addition, a great rate of domestic currency depreciation
could lead to disintermediation as depositors decide to withdraw their funds from banks
to invest directly to other “hard currency assets” with higher returns, thus banks will
face capital shortage and bank credit risks will increase.
2.1.5 Unemployment and Risk:
Another theoretical explanation of the source of banking credit risk is viewed from
unemployment as an indicator that highly correlate with the economic cycle. For
households and individuals, an increase in the unemployment rate during economic
recession reduces the incomes, resulting cash flow streams be worse and then the
probability of on loan defaults could surge. While in corporate sector, a decrease in
production due to a drop in the consumption and demand for goods, causes revenues
loss and a weak liquidity position regarding debts. Therefore, it exacerbates bank credit
risk (Castro, 2013).

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Specifically, the relation between unemployment and NPLs are proposed by Lawrence
(1995), who conducted a theoretical model about life-cycle consumption. In this model,
the probability of loan default is explicitly explained that due to an increase in
unemployment, it will induce lower level of income from borrowers and their debtservicing capacity, thus the probability of credit risk is higher. Furthermore, in order to
limit the risk and ensure the capital for banks, higher interest rate loan will be offered
to clients with higher risk rates. From the model, Rinaldi and Sanchis-Arellano (2006)
have extended their study and suggested that the possibilities of NPLs also relied on the
unemployment rate, which reflects the current income and the uncertainty regarding to
the future income of borrowers as well as the lending rates applied by bank. Besides,
this model also implies that the volume of loan taken, the amount of investment and the
time preference rate also impact the probability of default.
Berge and Boye (2007) propose that during periods of cyclical economic recession, as
unemployment rises and corporate earnings are diminishing, both NPLs and banks'
losses may surge. Higher unemployment rate also make borrowers suffer from debtservicing costs and other costs while banks have to determine their loan provisions
following to the borrowers’ expected future flows of income and expenditure. It will
deteriorate the borrower’s debt-servicing capacity as movements of these factors
diverse from expected developments, thus the credit risk will increase.
2.2

Empirical reviews:

2.2.1 Gross Domestic Product (GDP):
Gross Domestic Product (GDP) can be defined as the monetary value of all the finished
goods and services produced within a particular country's borders in a specific time
period. Following former researches, this paper will use annual growth rate of real GDP
at constant prices as an indicator for both of economic activities and business cycles,
which may have directly impact on the banking system in regard to bank risks. Most of
literatures find a significant influence and a negative relationship between GDP growth
and NPLs. Specifically, Shu (2002) executes stress testing for the Hong Kong’s banking
sector to calculate the volatilities of loan quality between 1995 and 2002. Borrowers’
ability to loan repayment and the banks’ portfolio position are influenced by changes in
Page | 13


macroeconomic determinants, which are considered as the risk factors in the paper. The
author concludes that higher economic growth or economic expansion highly associates
with higher profitability for corporate sector, reducing the default rates while banks’
exposure to risk reduces and then open more chances to lend rapidly. Moreover,
applying Merton´s methodology to analyze the relationship between Czech bank credit
risk and macroeconomic factors, Jakubik (2007) finds that decreases in real GDP
growth deteriorates the banks’ loan portfolio quality due to changes in the corporate
earnings, wage growth and high unemployment rate, which leads to higher bank credit
risk. In the case in Tunisia banking system, Zribi and Boujelbène (2011) examine a
panel model of ten commercial banks from 1995 to 2008 and use GDP growth as the
macroeconomic variable in order to ascertain the bank credit risk. They also indicate
the negative overall effect of GDP growth on the bank credit risk.
Louzis et al. (2012) conduct research with dynamic panel approach on a wider range of
loans (consumer loans, business loans, and mortgages) in Greek banking system over
the period 2003–2009. They conclude that the borrowers’ capacity of loan repayments
depends on the phase of the economic cycle. In an economic downturn or lower GDP
growth, the NPLs will increase for all loan types while in the economic expansion,
borrowers will have sufficient and enough incomes to repay their loan. Therefore, it can
be expected that NPLs is correlated negatively with the economic cycle, rising at times
when economic activity slowdown and deteriorates the quality all loan types.
Besides, for cross-country level, according to a study with dynamic panel data method
of Castro (2013), in the period 1997q1–2011q3, regard to banks of Greece, Ireland,
Portugal, Spain, and Italy, the paper demonstrates the significant interaction of GDP
development and the recent financial crisis to the movement of the bank credit risk.
Their results show that GDP growth is negatively related to the NPLs, the higher level
of GDP growth causes a higher level of income for borrowers, leads to greater cash
flows. This also raises the profitability of the bank and lowers the NPLs and bad debts.
The same conclusion is founded in the papers of Nkusu (2011) in case of 26 advanced
countries from 1998 to 2009; or Messai and Jouini (2013) in case of Italy, Greece and
Spain for the period of 2004-2008; or Klein (2013) in case of Central, Eastern and

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South-Eastern Europe (CESEE) in the period of 1998–2011; or Chaibi and Ftiti (2015)
in case of comparison between French and German economy.
On the other hand, there are several researchers found out no significant relationship
between GDP growth and bank credit risk. For example, Poudel (2013) indicates no
significant relationship between GDP and NPLs in 31 Nepalese commercial banks over
the period from 2001-2011. It can be explained that during economy downturn, when
making new loans, banks tend to carefully qualify their borrowers based on
creditworthiness and credit condition of borrowers. Besides, banks are well prepared
and will categorize their clients and debtors in order to control the amount of NPLs and
credit risk. Therefore, the volume of credit would be reduced during low GDP growth
phase. The same result is also supported by Kalirai & Scheicher (2002) in case of
Austrian banking system, Fofack (2005) in Sub-Saharan Africa banks and Aver (2008)
in case Slovenian banking system.
2.2.2 Interest Rate:
Interest rate is another significant determinant in order to investigate the correlation
between the interest rate and credit risk because it directly affects the debt burden of
borrowers. Since there are many different kinds of interest rate, this paper will choose
the real interest rate due to data availability and it is expected to be positive. In addition,
different types of interest rate usually have a strong correlation with each other: an
increases in the interbank rate addresses an increase in monetary policy interest rate and
leads to money market rates surge as well as long-term fixed-income securities yields
(Bohachova, 2008).
Fofack (2005) finds positive relationship between real interest rate and credit risk in
Sub-Sarahan Africa. The paper suggests that higher interest rate leads to an increase in
cost of borrowing that borrowers would pay to obtain loan, as well as an increase in cost
of deposits that make the commercial banks’ profit decrease. Therefore, the default rate
will increase. In addition, Jiménez and Saurina (2006) with the help of Generalized
Method of Moments (GMM) estimator for dynamic panel models also used the real
interest rate to investigate the impact of interest rate on loan loss. They found a
significant and positive relationship between interest rate and loans losses in Spanish
Page | 15


commercial or savings bank between December 1984 and December 2002. Also with
study applying the GMM estimator for banking systems in Southeastern Europe, Curak
et al. (2013) point out that the higher real interest rate is, the higher possibility of the
NPLs of variable rate loan are. The explanation is that as the real interest rate increases,
it creates an additional burden for debtors to serve their payment obligations.
From the research conducted by Castro (2013) in GIPSI countries, the paper use longterm interest rate in its regression as benchmark for analysis and it is the most
appropriate measurements because banks normally and mostly do lending long-term
loans. The study finds a significant positive relationship between interest rate and credit
risk. Also in the research, for robustness check of the model, the long-term interest rate
is replaced in turn by the real interest rate and the interest rate spreads, the results of
these variables are in same direction, In addition, long-term interest rate is more
important to measure the effect of credit risk when loan interest is either higher or lower
and higher interest rate will lead to increase the obligation for corporate borrowers and
individuals, thus it induces the banks’ credit risk. The similar results are found in the
research of Quagliariello (2007) between the long-term interest rate, measured by tenyear Italian Treasury bond, and the proxy for credit risk, the loan loss provision. In
addition, the findings of Solarin et al. (2011), which comply on the basis of Auto
regressive distributed lag (ARDL) approach on Islamic banks of Malaysia and interest
rate, indicates a significant positive long-run impact on NPLs. Also in Malaysia, with a
test on commercial banks during 2006 till 2010, Asari et al. (2011) apply the vector
error correction model to discover the effect of interested rate on NPLs. They find a
strong long-run relationship between interest rate and NPLs while in short run, interest
rate do not influence NPLs.
The paper of Ali and Daly (2010) employs credit risk logit regression for both banking
system in USA and Australia for 14 years from 1995Q1 to 2009Q2 and does not find
any significant relationship between nominal interest rate or short-term interest rate (6month) and credit risk in both USA and Australia.

Page | 16


2.2.3 Inflation rate:
Inflation rate decreases the purchasing power of currency, when the general price level
of goods and services is rising in an economy until a certain extent. Inflation rates are
generally associated with the interest rate of loan and affect the efficiency of banking
sector as well as the debt obligation of borrowers. Through literature reviews, the impact
of inflation on NPLs can be positive or negative. On one hand, as inflation rate increase,
the real value of loans in nominal rates or variable rates (as adjusted according to the
inflation) deteriorates, debtors can easily repay their loans. On the other hand, high
inflation reduces real value of the profitability while rises cost of capital and thus
weakens the debtors' ability to the loan (Curak et al., 2013).
Several studies have found inflation is positively affecting the banks credit risk. The
results of Demirguc-Kunt and Detragiache (1998), using a multivariate logit
econometric model with a large sample of developed and developing countries in 198094, indicate that high rate of inflation is one of the consequences exacerbating risk
problems of banking sector. High inflation may associate with the high nominal interest
rate and banks would find it difficult to perform a maturity transformation. In addition,
as the paper‘s empirical evidence shows, when restrictive monetary policies are
implemented to control inflation and keep banking sector stability, they lead to a sharp
increase in real interest rate; high real rates tend to increase the likelihood of a banking
crisis. Utilizing a panel data at bank level for both public and private commercial banks
in India, Thiagarajan et al. (2011) conduct an investigation in the relationship between
current inflation and one year lag inflation with bank credit risk. In the public sector
banks, the authors find a positive relationship between current inflation and credit risk
but no any relationship with one year inflation lag. However, in case of private sector
banks, the relationship between inflation and credit risk is not significant. Furthermore,
the research of Badar & Javid (2013) also suggests the significantly positive relationship
between inflation and bank credit risk, which examines the impact of macroeconomic
on NPLs of 36 Pakistani commercial banks during the period 2002 to 2011. The study
states that inflation will affect the profitability of commercial banks and increase the
bank credit risk. Because the contractionary fiscal policy from government is
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