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

MACROECONO
MIC
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
DETERMINA
NTS 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

Page | 1


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

Page | 2


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
Page | 3


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
Page | 4


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
Page | 5


-

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
Page | 6


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.

Page | 7


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.

Page | 8


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
Page | 9


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 high-risk behaviors; higher rates are charged to high-risk borrowers in order
to mitigate risks, hence banks overall risk exposure increases more (Fofack, 2005).

Page | 10


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 shortterm lending interest rate in order to deal with their liability payments (Mishkin,
1

1996) . 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.

Page | 11


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).

Page | 12


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
Page | 14


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 SubSarahan 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