Credit Risk in Banking Sectors

by Evaluating in Nonperforming Loans in European

and Asian Countries

Xiaohua Zheng

MSc International Risk Management and Finance

August 2016

Research Project Declaration

I agree that, should the University wish to retain it for reference purposes, a copy of my Research Project may be held by Bournemouth University normally for a period of 3 academic years. I understand that once the retention period has expired my Research Project will be destroyed.

Confidentiality

I confirm that this Research Project does not contain information of a commercial or confidential nature or include personal information other than that which would normally be in the public domain unless the relevant permissions have been obtained. In particular any information which identifies a particular individual’s religious or political beliefs, information relating to their health, ethnicity, criminal history or sex life has been anonymised unless permission has been granted for its publication from the person to whom it relates.

Copyright

The copyright for this Research Project remains with me.

Requests for Information

I agree that this Research Project may be made available as the result of a request for information under the Freedom of Information Act.

Signed:

Name:

Date:

Programme:

------------------------------------------------------------------------------------------------------

Originality Declaration

I declare that this Research Project is all my own work and the sources of information and material I have used (including the Internet) have been fully identified and properly acknowledged as required in the guidelines given in the Programme Handbook which I have received

Signed:

Name:

Date:

i

Abstract

The most recent financial crisis raised the public panic and raised extensive research

in the cause of a crisis. Credit risk, as a predominant risk factor in the banking crisis,

is investigated in this paper to seek the joint impact from macroeconomic movement

and banks’ internal behaviour.

This article laid emphasis on seeking for for six key systematic and unsystematic

determinants to explain the occurrence of credit risk level in ten European (Austria,

Spain, France, German, Poland) and Asian (China, Indonesia, Thailand, Philippine

and Vietnam) countries over the last decade (2005 – 2015). An econometric panel

analysis incorporating fixed-effect least square and difference General Method of

Moment was utilised in this research. The results verified a sound evidence that

unfavourable macroeconomic shocks, like economic downturns, macro

mismanagement and currency fluctuation, as well as bank disturbances, like low

bank profitability, excessive risk-taking and lending activities, challenges the

stability and credit risk level in banks across the detected regions. Changes in GDP

growth, real effective exchange rate, return on equity, loan/asset ratio and loan

growth rate depicted an inverse effect on impaired loans ratio, whereas inflation rate

displayed a positive relationship with NPLs in banking sectors.

However, the results in robustness check suggested that developing countries exist

different performance uncommon to other regions. Loan/asset ratio and loan growth

rate are found robust in both regions.

This paper found research in credit risk is substantially essential and meaningful in

predicting and evading potential banking crisis by capturing the macroeconomic

movements and institutional behaviours. Government policy makers and bank

regulators play an imperative role in providing a healthy economic and financial

environment for controlling potential crisis.

Keywords: nonperforming loans, credit risk, macroeconomic, bank-specific

ii

Acknowledgement

I would like to express my sincere gratitude to my supervisor Dr Merima Balavac,

who gave me supervision on my dissertation, as she did more than she should as a

supervisor. I appreciated as I cannot finish such a dissertation without her help.

I also sincere appreciate my family supporting me freely for my study here. Never do

I need to worry about living, the only thing I shall care is to study and live a happy

life in Bournemouth.

Lastly, thanks to my dear friends, classmates, who always be with me, always

answering and solving my endless queries and issues both in study and life. Thank

God for allowing me to meet you guys.

Special thanks to Albina Gaisina, Jay Nugent, Oluwagbenda Wise Adamolekun and

Onwuchekwa Uche that have given me great help in my dissertation.

Xiaohua Zheng (Queeni)

iii

Table of Contents

Abstract ........................................................................................................................ i

Acknowledgement ...................................................................................................... ii

Table of Contents ...................................................................................................... iii

List of Table ................................................................................................................ v

List of Figure ............................................................................................................. vi

List of Abbreviations ............................................................................................... vii

Chapter 1 Introduction ............................................................................................. 1

1.1. Background of the Study ................................................................................ 1

1.2. Research Objectives ........................................................................................ 2

1.3. Research Questions ......................................................................................... 3

1.4. Structure of the Study ..................................................................................... 4

Chapter 2 Literature Review .................................................................................... 5

2.1. Theoretical Framework .................................................................................. 5

2.2. Review of Empirical Literature ..................................................................... 6

2.2.1 Macroeconomic Indicators .......................................................................... 7

2.2.2 Bank-specific Indicators ............................................................................ 11

2.3. Research Gap ................................................................................................. 15

2.4. Hypothesis Test .............................................................................................. 16

Chapter 3 Research Methodology .......................................................................... 17

3.1. Research Design ............................................................................................. 17

3.2. Research Philosophy ..................................................................................... 18

3.3. Research Approach ....................................................................................... 18

3.4. Data Collection and Description .................................................................. 19

3.4.1 Sample Selection and Sources ................................................................... 19

3.4.2 Sample Size ............................................................................................... 20

3.4.3 Variables .................................................................................................... 20

3.5. Econometric Model and Methodology ........................................................ 22

3.5.1 Econometric Model ................................................................................... 22

3.5.2 Empirical Methodology ............................................................................. 24

3.5.3 Methodology .............................................................................................. 24

iv

Chapter 4 Empirical Findings ................................................................................ 26

4.1. Descriptive Statistics Analysis ...................................................................... 26

4.2. Main Findings ................................................................................................ 28

4.2.1 Multicollinearity ........................................................................................ 28

4.2.2 Homoscedasticity ....................................................................................... 29

4.2.3 Normal Distribution ................................................................................... 29

4.2.4 Durbin-Watson Test .................................................................................. 30

4.2.5 Empirical Results ....................................................................................... 30

4.3. Discussion of Empirical Findings ................................................................ 33

4.3.1 Inflation Rate ............................................................................................. 33

4.3.2 Growth of Real GDP ................................................................................. 34

4.3.3 Real Effective Exchange Rate ................................................................... 35

4.3.4 Return on Average Equity ......................................................................... 36

4.3.5 Growth of Gross Loan ............................................................................... 37

4.3.6 Loan to Total Asset .................................................................................... 38

4.4. Robustness Test ............................................................................................. 39

Chapter 5 Conclusion, Limitations and Recommendations ................................ 41

5.1. Conclusion ...................................................................................................... 41

5.2. Limitations ..................................................................................................... 43

5.3. Recommendations ......................................................................................... 44

5.4. Further Research ........................................................................................... 44

Reference .................................................................................................................. 45

Appendices ................................................................................................................. A

Appendix 1 Macroeconomic interlink with NPLs ........................................... A-1

Appendix 2 Normal Distribution of Independent Variables .......................... B-1

Appendix 3 Quantiles – Quantile Graph ......................................................... C-1

Appendix 4 Signs of Tested Variables .............................................................. D-1

v

List of Table Table 3.1 Observation Summary ............................................................................... 20

Table 3.2 Data Definition, Expected Signal and Sources ......................................... 22

Table 4.1 Descriptive Statistics, 2005-2015 .............................................................. 27

Table 4.2 Correlation Matrix ..................................................................................... 29

Table 4.3 NPLs: Macroeconomic and Bank-Level Determinants, 2005-2015 ......... 31

Table 4.4 Significance Period Range for Durbin-Watson Result ............................. 30

vi

List of Figure

Figure 2.1 Impact from Adverse Economic Movement ............................................. 8

Figure 2.2 Impact from Dysfunctional Management in Banks ................................. 11

Figure 2.3 Credit Risk Determinants ........................................................................ 14

Figure 3.1 Research Onion ........................................................................................ 17

Figure 3.2 Inductive and Deductive Approach ......................................................... 19

Figure 4.1 Exchange Rate Fluctuation ...................................................................... 26

Figure 4.2 Real GDP Change Performances ............................................................. 27

Figure 4.3 NPLs Ratio in Two Regions .................................................................... 28

Figure 4.4 Distribution of Logit transformed NPLs ratio ......................................... 29

vii

List of Abbreviations

NPLs Nonperforming Loans

WaMu Washington Mutual Savings Bank

IMF International Monetary Fund

OLS Ordinary Least Squares

GMM Generalized Method of Moments

GDP Gross Domestic Product

CPI Consumer Price Index

ROA Return on Asset

ROE Return on Equity

REER Real Effective Exchange Rate

ROAE Return on Average Equity

LTAR Loan-to-Asset ratio

G_LOAN Gross Loan Growth Rate

INFR Inflation Rate

Page 1 of 66

Chapter 1 Introduction

1.1. Background of the Study

The global financial crisis in 2007 – 2008 has brought a dramatic aftermath to the

world economic market, witnessed by hundreds of commercial and investment banks

destroying trillions of dollars of wealth worldwide. The crisis has brought an

enormous downgrade of credit hierarchy due to high leverage level. Meanwhile, the

exploitation of bank credit results in financial institutions failing to pay back their

massive loans and mortgages. It also brought tremendous impacts on commercial and

retail banking. For instance, Washington Mutual Savings Bank (WaMu), the biggest

saving banks in the US, was seized and sold to JP Morgan in 2008 (Sender et al.

2008). The global financial crisis has been regarded as one of the worst financial

crisis events as it gave rise to a large amount of bank failure or bankruptcy as a result

of suffering from credit risk and bad loans (Rashid et al. 2014). The aftermath of the

financial crisis caused dramatic changes in the macroeconomic environment, which

has magnified the impacts on banking sectors. Nonetheless, the internal imbalance

and mismanagement in banks were also convinced as crucial elements for financial

vulnerability. A vast amount of studies on the global financial crisis has emerged,

accompanied by research in bank profitability, credit risk exposure and loan default

probability from distinctive perspectives. Compared with other industries, it’s

apparent that banking sectors suffered immense losses during and after the global

financial crisis (El-Bannany 2015). This period makes it a worthy area to research

on in banking sectors.

Credit risk is regarded the most significant factors in banking crisis as it could

deteriorate economic environment and raise interest payments, which is commonly

found in credit risk models (Espinoza and Prasad 2010; Agnello and Sousa 2011).

Reduced credit risk management causes from a high level of speculative lending,

internal leverage and intense concentration of credit in banking sectors, accordingly,

adding up to substantial loan default probability and problems loans (El-Bannany

2015). In fact, early research found clear evidence that the level of problem loans

increases dramatically before and during the financial crisis (Gonzalez-Hermosillo

1999). Appendix 1 demonstrates the close linkage between credit quality and the

Page 2 of 66

economic downturn. The Macroeconomic factors are considered to be the

predominant factors on triggering a banking crisis. For instance, a slowdown of the

business cycle, high inflation rate, high unemployment level and huge fluctuation in

exchange rates are considerable elements for the banking crisis. Moreover, a

deterioration in bank’s financial statements can also reflect as a slowdown in the

economic conditions. Adversely, internal bank fundamentals like low profitability,

inefficiency and high leverage level also add up to potential risks on the write off in

banks’ balance sheet. Therefore, it’s essential to evaluate the credit risk exposure by

observing the macro-financial performance in banks as it plays a vital role to raise

the awareness on proper preparation to face financial vulnerability and adverse

economic movements (Castro 2013).

Nonperforming loans (NPLs), also known as impaired loans in divergent banks, is

regarded as the most common gauge to evaluate loan quality due to majority banks

have taken NPLs data as a benchmark to measure credit risk level (Ahmad and

Ariff 2007). NPLs, considered as the ‘financial pollution’, would crumble the

financial market and economic environment. Recent studies have found that failure

management of bad debts increase is the dominant cause of financial friability, which

ascertained the fact that not only did macroeconomic shocks the economic

environment but also did banks’ systematic factors matter for the credit risk. High

level of NPLs in banks exist a greater possibility for banks to face a banking crisis.

Thus, NPLs is always taken as a proxy for credit risk in measuring the financial

vulnerability of banks.

1.2. Research Objectives

Apart from the case of WaMu in the USA, several banks in Europe and Asia also

suffered from high NPLs and significant credit risks during the global regression

period, especially Italy, Indonesia and Thailand has closed down many banks. While

other banks in other countries also encountered with mergers with other banks or

injection of financial bailout from governments (Ahmad and Ariff 2007). There

exists considerable amount of studies by single countries, but they mainly

concentrate on European countries case studies (Salas and Saruina 2002; Arpa et

al. 2001; Quagliariello 2007; Cotugno et al. 2010; Zeman 2008) and regional

Page 3 of 66

analysis in Central Europe, Middle East and African countries (Williams 2004;

Mannasoo and Mayes 2009; Espinoza and Prasad 2010; Festic et al. 2011;

Castro 2013; Makri et al. 2013). Although most influenced countries in Asia are

developing countries, the aftermath and consequence in Asian countries also derive

great impacts on the global economic environment. Notably, studies in Asian

countries are comparatively less. Considerable researchers have focused on only

either the macroeconomic area (Arpa et al. 2001; Fofack 2005; Ahmad and Bashir

2013; Beck et al. 2015) or the institutional impact (Kraft and Jankov 2004; Epure

and Lafuente 2012; El-Bannany 2015), and limited studies have laid emphasis on

both perspectives including two differentiated regions.

This project intends to capture the linkage between credit risk and systematic and

unsystematic (macroeconomic-financial) aspects in ten countries that were

influenced by the global financial crisis in different levels. There include five

countries in Europe (Austria, Spain, France, German, Poland) and five countries in

Asia (China, Indonesia, Thailand, Philippine and Vietnam). This paper provides an

overview of the unfavourable macroeconomic conditions that these countries are

facing (financial crisis and economic downturns) to understand the impact from

adverse economic activities on banks’ credit risk performance, especially for the

business cycle, situations and appreciation or depreciation of local currencies. At the

same time, examinations on representative banks internal performance indicators by

tracking banks’ financial statements were also critical in this analysis. In terms of the

financial accelerator theory, it’s believed that the combination of internal institutional

mismanagement and macroeconomic disorder could magnify the impact on the asset

quality in a firm. Hence, a further study through both perspectives could be more

supportive to explain the credit risk in banking sectors. This paper also expects to

give a predictable guidance on proper policy making towards macroeconomic signals

and internal performance in financial sectors.

1.3. Research Questions

This project research explores four questions, which could assist in capturing a better

understanding of a crisis caused by negligence of credit risk in banking industry.

Page 4 of 66

How does credit risk influenced by macroeconomic shock and internal banking

dysfunctional managements in the last decade?

Is there any difference in problem loans behaviour in terms of adverse economic

activities and banking mismanagements in different regions, like Europe and Asia?

How does the impact from both perspectives magnify their effects on credit risk level

in banking sectors?

What kinds of potential measures can be taken to eliminate the impact from different

sides?

1.4. Structure of the Study

This paper intends to employ a proper econometric by a combination of panel

regression estimation, fixed-effect OLS and difference GMM estimator, to detect the

intervention across ten countries over last decades (2005 – 2015).

Five chapters structure this research. Chapter one describes the background and

objective of this study. In chapter two, it seeks a proper theoretical framework to

support the impact of macroeconomic movement and internal bank activities. At the

same time, a majority of empirical literature are reviewed to justify the

macroeconomic and financial influences on credit risk. Chapter three depicts the

methodology used in this study, accompanied by the explanation of conceived

variables, selected data, econometric models and potential problems existing in

design econometric analysis. Chapter four presents the descriptive statistics of

variables, diagnosed tests results for inherent issues, discussion on results and

findings. A robustness analysis is also carried out to underpin the results in this study.

Chapter five provides a conclusion linked with the research objectives. Limitations

and recommendations of this study are also provided for further research.

Page 5 of 66

Chapter 2 Literature Review

Studies on seeking the determinants of bank asset quality or credit risk are not new,

which has been examined by varieties of scholars through various approaches. This

chapter sheds light on the overview in the theoretical framework – the financial

accelerator and the empirical literature. The first part explains how the business cycle

magnifies the macro-financial linkage impacts on firms during the economic

downturns according to the theoretical framework. The second branch contributes to

empirical literature to identify the leading determinants of problem loans from the

macroeconomic movement as well as the bank-level fundamentals to interpret banks’

exposure to credit risk.

2.1. Theoretical Framework

The Financial Accelerator theory is the predominant and prevalent theoretical

frameworks on explaining the macro-financial linkage between macroeconomic

complementarities and financial accelerator (Bernanke and Gerlter 1989; Kiyotaki

and Moore 1997; and Bernanke et al. 1999,). Under the business cycle theoretical

framework, firm internal indicators also matter for the cyclical behaviour when

analysing the macroeconomic dynamics. As cyclical impulses in credit market

includes debt-deflationary shocks and shock to financial intermediaries.

This theory originated from the agent-principle issue, there always exists information

asymmetry, and therefore, it results in an extra cost to get the firm-level internal

information, which makes external finance costs higher than inward investment. In

this case, there occurs greater reliance on the corporate’s financial statements.

Bernanke and Gerlter (1989) identified that a firm’s financial statements are the

predominant resources of information depicting the implementation of the budget in

a corporate, which can largely influence some important decision-making like

investments and financing. That is, descent in asset price can deteriorate firms’

balance sheet and their net worth. Bernanke et al. (1999) explained that a negative

shift to the economy reduces borrowers’ net worth to different extent. Accordingly,

the initial shock on the spending and production will amplify the corresponding

effects.

Page 6 of 66

Consequently, firms’ financing ability would be destroyed to some extent, which

results in an adverse effect on their investments. Additionally, the economic

recession further downgrades the asset value, which conducts to a feedback cycle of

asset price falling, financial statements deterioration, financing conditions tightening

and economic activities declining. Vermeulen (2002) analysed the Germany, France

Italy, Spain and discovered stronger effects of the accelerator on small firms as they

have weaker balance sheets when facing the economic downturns and upturns. If

applied the financial accelerator theoretical into business cycle model, it is able to

provide a clear and precise background for NPLs modelling as they explicitly

explained the counter-cyclicality of business failure and credit risk (Williamson

1987).

Against this context, this theoretical framework is influential in the modelling of

NPL with its interaction with macroeconomic and institutional performance. Notably

in financial sectors, divergences in financial regulation and supervision affect banks’

behaviour and risk management practices and exposure, which are imperative to

explain the NPL disparities for cross countries analysis (Mensah and Adjei 2015).

Macroeconomic performance results in a direct impact on borrower’s balance sheet

and their loan capacity.

2.2. Review of Empirical Literature

Large impacts from descent or boom of global financial events on bank performance

are significant concerning asset quality and credit risk exposure through observing

the loan loss provisions, loss given default and NPLs (Beck et al. 2015). One of the

earliest studies, Keeton and Morries (1987), examined the causes of loan losses of

2470 US commercial banks and reported that commercial banks with higher risk

appetite prone to record greater loan losses. Meanwhile, they noticed that banks laid

less emphasis on the quality of borrowers during booming periods, which gave rise

to high problems loans in the banks. Their study has awakened the interests from

public and academic areas to carry out further research in credit risks regarding

problem loans in banks (Berger and DeYong 1997 and Ciccarelli et al. 2010). A

linear regression analysis was utilised by Sinkey and Greenwalt (1991) in US

banking sectors and concluded that loan losses influenced both internal and external

Page 7 of 66

factors. Similar findings have been discovered by Salas and Saurina (2002),

Fuentes and Maquieira (2003) Jimenez and Saurina (2004), Quagliariello (2007),

Cotugno et al. (2010), Louzis et al. (2012), etc.

Fernandez de Lis et al. (2000) discovered that annual growth rate of gross domestic

product (GDP) plays a vital role in explaining the NPLs fluctuation in Spanish saving

banks, ascertaining the boom of bad loans in the recession period. Vodova (2003)

investigated the banking crisis in the Czech Republic and concluded that the causes

of the banking crisis are mainly divided into macro- and microeconomic factors,

including the macroeconomic instability and inadequate preparation for financial

liberalisation, as well as non-performing loans. Quagliariello (2007) used a large

dataset of Italian intermediaries over period 1985 – 2002 and revealed that several

bank-level indicators also play a vital role in explaining the changes in the evolution

of riskiness along with macroeconomic variables. Therefore, the performance of

NPL is the most common measurement and frequently taken as the benchmark to

gauge the asset quality and banks’ credit portfolio. Overall, the empirical literature

has separated the determinants for NPLs in two categories: unsystematic conditions

and systematic bank-specific characteristics.

2.2.1 Macroeconomic Indicators

Extensive research examined the linkage between the credit risks and boom and

depression in the macroeconomic environment and discovered adverse effects of

economic conditions on NPLs. Sinkey and Greenwalt (1991) investigated the loan

loss experience of the major commercial banks in the US. They argued that the

deterioration of regional economic conditions is one of the predominant cause of

high loan loss rate in commercial banks. In addition to economic conditions, Salas

and Saurina (2002) examined the influential factors of NPLs in the Spanish

commercial and saving banks using dynamic panel model. Their results confirmed

that GDP growth shows a strong contemporaneous effect on the evolution of loan

losses in the Spanish market, which is an adverse correlation. This finding is similar

to Jesus and Gabriel’s (2006) findings on an acceleration of GDP and decline in

real interest rates brings a decrease in problem loans. At the same time, collateralised

loans are found to be a higher probability of default (Amuakwa-Mensah et al. 2015).

Hence, unfavourable macroeconomic conditions are the leading cause of problem

loans in banking sectors.

Page 8 of 66

Figure 2.1 Impact of Adverse Economic Movement

Fernandez et al. (2000) utilised panel regression analysis to examine commercial

and saving banks from macroeconomic conditions and banking indicators in Spain.

The study found a significant negative impact on the GDP annual growth rate and

bank size on NPLs. They ascertained that an enormous problem loan losses rate

increase at the time of and after the financial crisis. Arpa et al. (2001) tested credit

risk exposure by observing operation income in Austria. Their regression analysis

found that risk provisions are negatively correlated with real interest rate and real

GDP growth, while real estate inflation and consumer price index (CPI) have

positive impacts. Ahmad (2003) stated that real GDP growth negatively affects the

credit risk exposure in Malaysia banking sectors. Gerlach et al. (2005) studied the

Hong Kong commercial and saving banks and find that economic growth, CPI and

property price inflation erodes the NPLs ratio. Adversely, they believe that deflation

in economy delays the economic growth, and decrease the profitability and affecting

the debt paying ability of borrowers. A direct measure of banks’ write-off to loan

ratio is used by Hoggarth et al. (2005) to estimate the influence of adverse

macroeconomic shocks on aggregate losses in the UK banking system. They found

that loan quality and financial fragility cannot directly be impacted by dynamics of

interest rate and inflation rate. Blavy and Souto (2009) measured the credit risk and

frequencies of default rate by analysing the macro-financial linkages in Mexican

banking systems. They found that domestic and external macro-financial variables

have a strong intervention on banking soundness. Louzis et al. (2012) studied the

impact of macroeconomic fundamentals on business loan default by using dynamic

panel data methods. They found real GDP growth, unemployment rate and interest

rate have the strongest effects on NPLs volatility. Ahmad and Bashir (2013) used

nine macroeconomic variables to investigate the determinants of NPLs in Pakistani

banking sectors. The results show GDP growth, inflation rate, interest rate and

•GDP decrease

Business Cycle

•High Infation Rate

Macro-Mismanagement

•Change of Exchange Rate

Currency Fluction

•High Problem Loans

Credit Risk

Page 9 of 66

industrial production are negatively associated with NPLs, while CPI is positively

associated, which is same to Arpa et al. (2001), and no impact was found from the

unemployment rate, real effective exchange rate and foreign direct investment.

Whereas the insignificant effects from unemployment rate is opposite to Louzis et al.

(2012). However, Poposka (2015) observed the problem loans in selected developed

and developing countries and failed to find a significant relationship between GDP

growth and NPLs in Macedonia between 2004 and 2014, which is the opposite to the

other empirical literature.

Fofack (2005) used an unbalanced dataset in sixteen sub-Saharan Africa countries to

discover the impacts on NPLs. They introduced a new factor, real exchange rate

appreciation, and found that exchange rate movement, economic growth and real

interest rate played a significant role over period 1990 - 2003. Baboucek and

Jancar (2005) assessed the linkage between macroeconomic shocks and loan quality

in Czech banks from 1993-2004. They found appreciation in real effective exchange

rate has significant impact on the quality of loans as well as a significant positive

impact on the inflation rate and unemployment rate. Their study is in consensus with

Jakubik’s (2007) research on Czech Republic banking as he used a regression

analysis and found that bad loans in banking portfolio deteriorate as a result of

shocks in a form of changes in real GDP growth, interest rate and inflation rate.

Zeman and Jurea (2008) used multivariate regression analysis and demonstrated

slowdowns on the nominal interest rate and exchange rate are the most important

factors for NPLs dynamics, however, they ascertained that GDP does not have a

substantial impact on the banking performance. More recent studies like Dash and

Kabra (2010) used panel data regression and suggested that real exchange rate is a

major factor to impact NPLs performance. Khemraj and Pasha (2009) used a panel

data set and a fixed effect model in six Guyanese banks by observing real GDP,

annual inflation and real effective exchange rate. Their results showed a strong

inverse relationship between NPLs and GDP while real effective exchange rate

positively impacts the NPLs, which indicated local currency appreciation would give

rise to a higher loan portfolio in commercial banks.

Though, there is a quite common argument that economic downturn significantly

determinants credit risk because bank assets are more likely to deteriorate during

Page 10 of 66

economic downturns, which adding to default risks (Fischer et al. 2001, Ahmad

2003). A studied in commercial banks in Canada and the U.S. was carried out by

Fischer et al. (2001). They identified similar adverse effects from GDP growth on

credit risk, which is same as in Ariff and Marisetty (2001). Mannasoo and Maves

(2009) studied Central Eastern European countries using a panel logit model with a

set of explanatory variables and estimated that decline in GDP growth and changes in

banks’ internal and external environment result in deterioration of banking sectors’

performance and stability. Babihuga (2007) examined the 96 countries in Asian,

European and Sub-Saharan African regions. He found that inflation rate and real

GDP growth have an opposite impact on NPLs and capital adequacy. Meanwhile,

inflation and the real exchange rate emerge to different degrees as important

determinants. Nkusu (2011) quantified the impact of macro-financial vulnerabilities

on banks’ loan portfolio quality. His result identified that slowdown of GDP growth

and high unemployment rate are the key indicators in producing more bad loans.

Because these two variables are the leading drive of banking system distress and

deterioration in economic activity. De Bock and Demyanets (2012) studied 25

emerging countries and discovered huge impacts from GDP growth rate and

exchange rate on NPLs over their observed countries. A most recent research did by

Beck et al. (2015) has ascertained that real GDP growth is the prime drive of NPL

ratios over the past decades. They applied dynamic panel analysis and estimated

some significant determinants of asset quality over last ten years in 75 countries

worldwide. Moreover, exchange rate depreciations are found to result in an

ascendant of NPLs in countries with high level of lending in foreign currencies to

unhedged borrowers.

The review of the empirical literature demonstrated the substantial significance of

macroeconomic factors, such as GDP growth, interest rate, exchange rate,

unemployment rate, inflation rate, CPI and etc., on credit risk level and firm fragility.

In single countries studies, the results are even more distinguished between

developed and developing countries. It can be seen by different signals on the

macroeconomic factors across numerous countries. Combined with some panel

countries studies, it is more convincible to conclude that deterioration of economic

environment is the essential drive of business downturns, which significantly result

in higher level of loan losses in banking systems.

Page 11 of 66

2.2.2 Bank-specific Indicators

In addition to impacts from macroeconomic conditions, many empirical studies

suggest that fundamentals inside banks’ financial statements as well as risk profile

can explain the loan loss in banking sectors. Bercoff et al. (2002) used an

accelerated failure time model to test Argentinean banks and confirmed that bank-

level fundamentals play an essential role in interpreting the fluctuations in banks’

riskiness evolution in addition to macroeconomic indicators. Waeibrorheem and

Suriani (2015) used pooled model to study the determinants of credit risks in Islamic

Banks and Conventional Banks from macroeconomic factors and banks particular

factors, and find some indicators from both aspects have a significant effect on two

different kinds of banks, confirming Keeton and Morrie’s (1987) early research.

Therefore, efficiency, profitability (Return on Asset, ROA, and Return on Equity,

ROE), bank’s risk-taking ability (proxy by loan to total asset ratio) and lending

activities (loan growth) are identified to have significant on credit risk in banks by

using a proxy of NPLs (Figure 2.2).

Figure 2.2 Impact of Dysfunctional Management in Banks

Determinants of nonperforming loans associated with bank efficiency and

profitability can lead to efficiency problems in banking sectors. Fuentes and

Maquieira (2003) analysed banks in Chile and stated that asset growth, operation

efficiency and exposure to loan losses help explain NPLs. Godlewski (2004) took

ROA as a principal indicator to evaluate bank performance and find a negative

impact on bank’s profitability on NPLs level. While some researchers identified

some contradictory results that high levels of ROE are contributed to a greater future

risk (Garcia-Marco and Robles-Fernandez 2008), they argued that the policy of

profit maximisation is accompanied with a high level of risks. Therefore, more new

research regarding an interaction between bank profitability and bank risks were

•Low ROE or ROA

Bank Profitability

•High Loan/asset ratio

Exessive Risk-taking

•High Gross Loan Growth

Lending Activities

•High Problem Loans

Credit Risk

Page 12 of 66

conducted. Cotugno et al. (2010) used a sample of 1,927 observations in Italian

banks over the financial crisis period (2006 – 2008) and demonstrated a substantial

negative relationship with bank profitability. That is, Banks with high ROA are

associated with lower level of impaired loan losses. Similar outcomes have been

recognised by Epure and Lafuente’s (2012) study in Costa-Rico over period 1998-

2007. Louzis et al. (2012) test Greek bank-specific factors and find that bank

performance and inefficiency indicators serve as leading interpreting power in

explaining loan losses in banks. They used ROE, inefficiency index, as a proxy of

bank management and conclude a negative and statistically significance for all NPLs

catalogues. In addition to the macroeconomic perspective, Makri et al. (2013) also

recognised ROE and capital ratio appear to exert a powerful influence on bank’s loan

losses rate in their study of a panel of 14 countries in the Eurozone. Fredrick (2012)

studied the financial performance of commercial banks in Kenya and realised a

strong relationship between ROE and asset quality. Bank profitability is always

serving as a proxy of bank’s management. Hence, the previous results are consensus

to Berger and DeYong’s (1997) early ‘bad management’ hypothesis. However,

results found by Vatansever and Hepsen (2013) are contradictory to previous

research. They detected eight systematic and unsystematic fundamentals in Turkey

and reported that NPLs ratio appears to be positively influenced by ROE.

Economic boom times usually witness rapid loan growth over the world, while such

lending soars have been identified as a substantial factor in increasing the risk and

raising financial crisis (Caprio and Klingebiel, 1996). Williams (2004) studied

European banks from 1990 – 1998 and found a stable relationship between loan

quality and cost efficiency, so does recent research by De Bock and Demyanets

(2012), who analysed 25 emerging countries over period 1996-2010 and addressed

that credit indicators like loan growth are the core determinants of problem loans.

Bikker and Metzemakers (2004) investigated the intervention from business cycle

to bank provision behaviour and described that loan growth, loan to total asset and

capital to total assets significantly determinate the loan loss provision. Kraft and

Jankov (2005) also discovered that rapid loan growth increased the probability of

credit quality deterioration. However, they argued loan growth rate is not the sole

predictor of banking failure as other bad business policies combined with loan

growth rate could magnify the loan losses and result in a deadly bank failure. This

Page 13 of 66

conclusion is agreed by Espinoza and Prasad (2010) although they also found

similar positive effects from loan growth on NPLs by dynamic panel analysis on 80

banks in GCC over 1995 – 2008. They were aware that there still exists a number of

factors affect banks’ risk-taking ability, like ownership structure, agency problem

and regulatory action. Mannasoo and Mayes (2009) used a discrete-time survival

model and found it possible to take advantage of the bank-specific variable to

forecast the financial vulnerabilities in banking sectors in Europe. Their results

indicated variations in bank earning, efficiency, and relative size of credit portfolio

are one of the warning indicators. In Cotugno et al.’s (2010) study, a positive

correlation between default rate and gross loan growth rate was found in Italian

banking sectors. However, negative relationship between loan growth and impaired

loans was also found in several studies. Cavallo and Majnoni (2002) found a

negative sign for loan growth rate as they regard the increase of new loans and the

loosening of monitor tend to reinforce the risk exposure of banks portfolio, which

would decrease the loan losses rate. A similar negative signal of loan growth on

bank’s NPLs ratio was discovered by Laeven and Majnoni (2003) as well. More

recently, Bonfim (2009) also found an analogous negative sign for loan growth rate

and ascertained the firms’ financial situation has a central role in explaining default

probabilities.

Bikker and Metzemakers (2005) studied the relationship between bank loan

provision behaviour and business cycle by observing 29 OECD countries over the

past decade. They found that loan as a share of total asset ratio various across

countries, as positive significance effects are found in US, Italy and the UK, while

impacts of loan/total asset ratio in Japan, France and Luxembourg turn out to be

insignificant. However, Mannasoo and Mayes (2009) found an interesting result

that fluctuation in banks internal and external environments destroy the performance

and stability in banking sectors, loan asset ratio is negative with respect to bank

distress with respect to early warning models, which is opposite to early dominant

evidence. Cotugno et al. (2010) analysed the loan default rate and bank’s production

specialisation in lending (Loan to total asset ratio) and found a substantial weakness

of the relations. They confirmed that the deterioration of loan quality is closely

linked with bank’s lending activities. Festic et al. (2011) studied five European

countries (Bulgaria, Romania, Estonia, Latvia and Lithuania) by utilising both panel

Page 14 of 66

regression fix- and random-effects. Loan to asset ratio is demonstrated to stimulate

the growth of NPLs due to the soft loans given by the banks. However, in

Vatansever and Hepsen’s (2013) findings in Turkey, their result showed that debt

ratio, loan to asset ratio does not have a significant effect in explanation of loan

default rate on multivariate perspective, which is partly consensus with Bikker and

Metzemakers (2005). El-Bannany (2015) discovered the similar outcome of the

relationship between bank profitability and problem loans. He employed a multiple

regression analysis in UAE banks over the global financial crisis period and

identified a significant impact on the level of credit risk disclosure from foreign

ownership, bank age and bank profitability variables.

Studies in the bank-specific fundamentals cover larger ranges in the selection of

independent variables. The bank efficiency, profitability, excessive risk-taking ability,

regulatory, bank management and loan portfolio are regarded as predominant factors

in the research on credit risk level. Whereas, the results also various across countries

like the divergent sign of ROE, loan growth rate and loan to total assets. Panel

countries studies give a more precise interpretation on institutional performance’s

impact on the credit risk level from three perspectives. Hence, it can be summarised

that unsystematic factors, bank-specific variables, are able to predict vulnerabilities

in banking sectors in addition to economic activities.

Figure 2.3 Credit Risk Determinants

Credit Risk

BusinessCycle

MacroMismanage-

ment

CurrencyFluctuations

Bank Profitability

Lending Activities

Risk-taking Ability

Page 15 of 66

To sum up, the aftermath of bank credit performance from the volatility of global

economic shakes and banking internal weakness in different countries is quite

uneven (Figure 2.3). The empirical literature has proved extensive effects from

unsystematic conditions and systematic bank specific characteristics on loan losses in

banking sectors. In accordance with the financial accelerator theory, the effect on

firm performance, especially credit risk would be magnified as there exists a robust

interlink between economic downturns and upturns as well as companies’ internal

behaviour. Therefore, further studies in the credit risk behaviour in banking sectors

are necessary as it also reflects the maturity of the local economic conditions and

firm growth. This paper contributes to empirical literature to discover relative macro-

financial indicators to review their impacts in the most recent period.

2.3. Research Gap

Early research mainly concentrated on single countries studies like the U.S., Spain,

Italy, Austria, Australian, Mexican, Greek, Malaysia, etc., most of the studies

primarily focus on advanced western countries. With the evolution of research

methods and results, more literature emphasised their study in one region, like

European or part of European Countries (CESEE, CEE, SEE), the Gulf Cooperative

Council (GCC), NAFTA, Middle East and North Africa (MENA) or Sub-Saharan

African. Analysis of determinants diversified from either macroeconomic or bank

specific perspective, some of them studied both sides but in only one region. Beck et

al. (2015) covered the largest amount of countries (75), however, the study only

focuses on macroeconomic performances. As discussed above, it is clear to witness

an increasing tendency on cross countries studies instead of single countries, internal

and external indicators exposures other than only one direction in research. However,

limited studies are covering both perspectives in differentiated regions to compare

the difference. This project will shed light on five developing countries in Asia and

seek another five similar countries in European regions according to the GDP figure

in accordance to the IMF GDP ranking to debate the significance from financial

stability and institutional performance on credit risks level in banking sectors.

Page 16 of 66

2.4. Hypothesis Test

Hypothesis Test 1:

H0: Inflation rate is insignificant to NPLs ratio.

H1: Inflation rate is significant to NPLs ratio.

Hypothesis Test 2:

H0: GDP growth rate is insignificant to NPLs ratio.

H1: GDP growth rate is significant to NPLs ratio.

Hypothesis Test 3:

H0: Real effective exchange rate is insignificant to NPLs ratio.

H1: Real effective exchange rate is significant to NPLs ratio.

Hypothesis Test 4:

H0: Return on average equity is insignificant to NPLs ratio.

H1: Return on average equity is significant to NPLs ratio.

Hypothesis Test 5:

H0: Gross loan growth rate is insignificant to NPLs ratio.

H1: Gross loan growth rate is significant to NPLs ratio.

Hypothesis Test 6:

H0: Loan to total asset ratio is insignificant to NPLs ratio.

H1: Loan to total asset ratio is significant to NPLs ratio.

Page 17 of 66

Chapter 3 Research Methodology

This chapter paid attention to the research design and the implementation of this

study, which is a guideline to accomplish this research systematically. The function

of this section is to outline the methodology and related techniques or tools used in

this project, including the research design, research approach, research philosophies.

At the same time, it describes the logic behind of what data is selected, how the data

is collected, possible diagnose test for the panel data sets in econometric analysis and

how data would be analysed. By way of the purpose to detect the interference

between credit risk and bank loan portfolio, this paper would mainly overview the

macroeconomic and microeconomic perspectives under the empirical research

background and current economic performance.

3.1. Research Design

Research Design contains clear research objectives, derived from research questions,

and specify the research approach and philosophy. The research design is a

comprehensive plan for data collection and analysis in empirical research projects

(Bhattacherjee 2012), which can be depicted in the research design onion (Figure

3.1).

Figure 3.1 Research Onion

Source: Research Method of Business Students (Saunders et al. 2009)

Page 18 of 66

3.2. Research Philosophy

Four main paradigms of research methods are well known as research philosophy,

while positivism and social constructionism paradigm are more associated with

quantitative studies.

The philosophy of positivism is mainly adopted the philosophical of a stance of

natural science, which belongs to the ‘resources’ researcher. Basically, it’s to

generate a research strategy to collect relevant and credible data. Hypotheses will be

developed based on the existing theory. The whole procedure is to test and confirm,

or refute, the whole or part of the hypotheses, leading to a potential evolution of the

existing theory which may then be examined by further research (Saunders et al.

2009). This study applies the positivism paradigm as it observe six variables by

collecting a large amount of credible data from Datastream and Bankscope. In

addition, it develop six hypotheses to discuss their impacts on the credit risk in

banking sectors.

3.3. Research Approach

In the research methodology, there are two main research approaches according to

the research onion above, inductive and deductive approach. The inductive approach

is naturally interpretative as it begins with detailed observations and theories are

proposed towards the end of the research process as a result of observation. No

theories and hypothesis would have applied in inductive studies. By contrast, the

deductive strategy is associated with ‘developing a hypothesis based on existing

theory, and then designing a research strategy to test the hypothesis Silverman

(2011). Summary of inductive and deductive approach is displayed in Figure 3.2.

Page 19 of 66

Figure 3.2 Inductive and Deductive Approaches

Source: The Ultimate Guide to Writing a Dissertation in business studies: a step by step assistance (Dudovskiy 2016)

Research methodology in this paper follows the deductive strategy, which can be

justified by testing the macroeconomic and bank-level indicator by hypothesis tests

based on the theoretical framework.

3.4. Data Collection and Description

3.4.1 Sample Selection and Sources

This paper used a panel data set over ten countries in Europe and Asia over the

period 2005-2015. All analytical databases are obtained from Bankscope and

Datastream databases. In terms of the fundamental purpose of this project, the author

selected separately five countries (Austria, Germany, Spain, France and Poland) in

Europe and another five countries (China, Indonesia, Philippine, Thailand and

Vietnam) in Asia. According to the latest worldwide GDP Ranking (2015) from IMF

World Economic Outlook (International Money Fund 2016), this paper randomly

selected three countries from the top ten list over Asia and Europe. Thus, they are

China, Germany and France. Regarding the selection of the rest countries, author

targeted the countries which are ranked close to each other across the ranking

difference. Therefore, the selected samples are believed to be capable of capturing

the deficiency and gap between developed and emerging market as banks in these

countries operate under divergent banking systems, regulations and market structures.

Deductive Approach

Inductive Approach

Page 20 of 66

Consequently, cross-sectional data for the whole model is selected, comprising of

bank profitability, efficiency, and leverage rate in commercial and saving banks’

financial statements and countries specific macroeconomic performance among

selected countries. The corresponding data are collected at an annual frequency from

2005-2015 as the financial database is available annually only. Hence, the macro

data is also collected respectively in an annual regularity.

3.4.2 Sample Size

Selected sample includes ten countries in Europe and Asia. The paper initially

planned to gather around 15 banks for each country. The dataset is panel as it

comprised several banks in observed countries for the decade 2005 – 2015. However,

some countries don’t have sufficient data for commercial and saving banks over the

past ten years. Thus, there exists divergence on bank numbers across the selected

countries. Overall, 92 banks are chosen and the number of banks from each country

is displayed in Table 3.1. The sample in this paper is cross-section time-series data.

Table 3.1 Observation Summary

Country Number of Banks

Asia

China 13

Indonesia 6

Philippine 8

Thailand 12

Vietnam 3

Europe

Austria 6

France 21

Germany 5

Spain 10

Poland 8

Total 92

Source: Datastream and Bankscope

3.4.3 Variables

Credit risk was utilised as the dependent variable in this paper depicted by the

performance of NPLs ratios, while the raw data demonstrated high volatility across

the countries. Therefore, a logit transformation is utilised on NPLs ratio, which logit

ensures the applied value (𝑥), here refers to the dependent variable, to span over the

Page 21 of 66

time interval and distributed proportionally. Logit is a common transformation for

linearizing sigmoid distributions of proportions (Armitage et al. 2001), which is

defined as below:

𝑙𝑜𝑔𝑖𝑡(𝑥) = ln( +,-+

)

The definition of nonperforming loan witnesses slight difference over the world, the

majority of countries defined the NPLs as impaired loans (or problem loans) which

have high potential being unable to be fulfilled within 90 days. Though, ‘Impaired

loans’ is a common concept in accounting, which reveals the probable cases in when

the creditor would fail to gather the full amount that it is specified in the loan

agreement from the debtor. Hence, the impaired loans are quite different from the

official classification of non-performing loans. Aiming to provide a fair and clear

justification regarding the research purpose, this project selected the dependent

variable from Bankscope unitedly under the name of ‘impaired loan/gross loan’,

which helps to eliminate the difference in varies countries.

The independent variables are divided into macroeconomic indicators and bank-

specific explanatory factors, three variables for each group. With regard to the

macroeconomic fundamentals indicators, it includes real GDP growth, inflation rate

and real effective exchange rate (REER), which mainly is collected from Datastream.

This paper calculated the change of REER to represent the appreciation and

depreciation of the local currency. An increase in the real effective exchange rate

represents an appreciation of the local currency, making the good and services

produced getting comparatively expensive (Castro 2013). US dollar is taken as the

intermediate currency, thus, the exchange rate for all currency is based upon USD.

On the other hand, bank-level indicators, such as efficiency and profitability ratio

(Return on Average Equity, ROAE), and excessive lending performance, Loan-to-

Asset ratio (LTAR) and Gross Loan Growth Rate, are selected as key financial

determinants to examine the risk-taking level and credit risk. All internal bank data

come from the Bankscope. Table 3.2 summarises symbol, definition, expected signal

and sources of dependent and independent variables in this research.

Page 22 of 66

Table 3.2 Data Definition, Expected Signal and Sources

Symbol Explanation Expected Signal Source

Dep

. NPLs Aggregate non-performing loans to total gross loans

Bankscope

Mac

roec

onom

ic

G_GDP Annual growth rate of Gross Domestic Product

(-) Datastream

REER Real effective exchange rate each country to the US dollars

(-) Datastream

INFR Annual average inflation rate (+)/(-) Datastream

Ban

k –

leve

l

ROAE Return on Average Equity (-) Bankscope

G_LOAN The growth rate of gross loans in annual frequency

(+) Bankscope

LTAR The Loan to Total Asset Ratio (+) Bankscope

3.5. Econometric Model and Methodology

3.5.1 Econometric Model

In order to detect the credit risk level and identify the impact on bank credit risk, this

paper mainly seeks for predominant determinants of NPLs through macroeconomic

and bank-specific perspectives by using panel regression analysis. Panel data

controls individual heterogeneity, at the same time, it provides more informative data,

more variability, less collinearity among the variables, more degree of freedom and

more efficiency (Baltagi 2012). Meanwhile, regression analysis is commonly

regarded as a useful way to predict an outcome variable from predictor variables in

multiple regression. Thus, the following Ordinary Least Square (OLS) econometric

function applied with correspondent variables is as below:

𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3 = 𝛽6 + 𝛽,𝑅𝑂𝐴𝐸1,3 + 𝛽<𝐺_𝐿𝑂𝐴𝑁1,3 + 𝛽?𝐿𝑇𝐴𝑅1,3 + 𝛽A𝐺_𝐺𝐷𝑃1,3+ 𝛽C𝑅𝐸𝐸𝑅1,3 + 𝛽D𝐼𝑁𝐹𝑅1,3 + 𝜀1,3

The dependent variable (𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3) is the indicator of credit risk for country 𝒾 and

time 𝑡 . Where 𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3-, is the lag of 𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3 . 𝑅𝑂𝐴𝐸 correspond to the

Return on Average Equity, 𝐺_𝐿𝑂𝐴𝑁 refer to the Gross Loan Growth Rate, 𝐿𝑇𝐴𝑅

Page 23 of 66

demotes Loan-to-Asset ratio; 𝐺_𝐺𝐷𝑃 represents real GDP growth, 𝑅𝐸𝐸𝑅 means real

effective exchange rate and 𝐼𝑁𝐹𝑅 is inflation rate.

In order to capture the persistence of the dependent variable, the dynamic panel data

model – econometric specification is chartered by the presence of dependent variable

with a one-year lag, 𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3-,, included as an explanatory variable on the right-

hand side. Thus, the regression equation is displayed as follow:

𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3 = 𝛽6 + 𝛽,𝑅𝑂𝐴𝐸1,3 + 𝛽<𝐺_𝐿𝑂𝐴𝑁1,3 + 𝛽?𝐿𝑇𝐴𝑅1,3 + 𝛽A𝐺_𝐺𝐷𝑃1,3+ 𝛽C𝑅𝐸𝐸𝑅1,3 + 𝛽D𝐼𝑁𝐹𝑅1,3 + 𝛽I𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3-, + 𝜀1,3

The dependent variable is explained by its lag, 𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3-,, together with other

macroeconomic and bank-specific variables. In OLS estimation, the added lagged

dependent variable will cause the OLS estimator biased and inconsistent if the error

term is not serially correlated (Baltagi 2012). In addition, the fixed-effects model is

consistent only when the model applied a very large T (Agung 2013). In this model

is applied with large N and a comparatively short T as the data is collected yearly

and ten years only, thus, which could result in the within estimator to be inconsistent

and suffer from Nickell biases (Baltagi 2012). Generalised method of moments is

more efficient in dynamic panel data analysis (Arellano and Bond 1991). Therefore,

Difference GMM is applied to transform the data to first differences by using the

lagged levels of the right-hand side variables as instruments, which eliminates the

individual effects. Further, time dummy is added in this dynamic panel regression

model, which is introduced to minimise the potential bias of estimates that could

arise from cross-section correlation of the residuals (Balavac 2012).

Regarding expected signs summarised from the empirical literature on table 2,

nonperforming loans are expected to have a negative relationship with the economic

boom, such as GDP growth, real effective exchange rate appreciation, and strengthen

in internal bankability, for example, higher profitability. On the other hand, the

impact from bank loan portfolio on NPLs is estimated to be positive, as an increase

in banking loan tends to add up the risk of bad loans in banks. However, the impact

on inflation rate is unpredictable as its empirical results various across countries and

uncertain in multi-countries analysis.

Page 24 of 66

3.5.2 Empirical Methodology

This paper incorporates two econometric specifications in panel regression analysis;

they are panel OLS and difference GMM estimation by Eviews (Griffiths et al.

2011).

Makri et al. (2014) utilised a dynamic panel regression model to investigate the

effect of banking and macroeconomic factors on NPLs. Further, they implemented

the difference GMM estimation to provide consistent and unbiased results, and in

first and second period lagged variables were employed as instruments in their GMM

estimation. Similar dynamic panel model is implemented by Espinoza and Prasad

(2010), additionally, their methodology also includes OLS, fixed effects and system

GMM. They applied the logit transformation of the NPL ratio as well to allow the

dependent variable to distribute symmetrically. This paper mainly follows empirical

methodology as above on estimating the determinants of credit risks through

dynamic panel regression model.

3.5.3 Methodology

Before starting the estimation of the panel LS regression model, several essential pre-

tests are applied to ensure there is no related issues causing endogeneity problems. In

this model, two groups of variables, Loan to Total Asset and Gross Loan Growth

(Bonfim 2009), GDP growth rate and exchange rate (Beck et al. 2015) are estimated

to suffer from endogeneity.

Multicollinearity, this would be carried out by generating the correlation matrix and

observing independence behaviour. If the correlation level between each variable is

less than 0.8, meaning that the model would not serve from severe multicollinearity

issue.

Homoscedasticity – In the fixed-effect model, this would be carried out by doing a

heteroscedasticity test under the unstructured data. If the p-value for the test is less

than 0.5%, indicating the model don’t exist homoscedasticity issue. In the GMM

estimation, the heteroscedasticity is not considered as a problem in this method as

GMM estimation is consistent to heteroscedasticity.

Page 25 of 66

Autocorrelation, this issue would be verified by the Durbin-Watson result. The

figure should be dropped into the area that is not significant to represent the result

and model.

Normality, all observed variables will be dealt with a histogram graph individually

to see whether they distribute normally.

After the tests, this project began with the econometric estimation of bank credit risk

performance by including different macroeconomic indicators and bank-specific

variables through the Panel Least Squares estimator overall observed countries. The

methodology aims to consider the time-constant unobserved values across countries

and gain an overall view of whole observations. Additionally, under the limitation of

the precise set of countries and the entire time-varying variables, the model could

encounter with omitted or unobserved variables biases, accordingly endogeneity

problem. Thus, the related external instruments variable method would be employed

in the estimation. As the whole sample contains a large set of firms, the fixed-effect

model is a more appropriate specification (Baltagi 2012). Therefore, a Hausman

Test is utilised to identify the more suitable model in a statistical sense for a panel

dataset regression analysis, namely fixed- and random-effects model (Agung 2013).

In this case, fixed-effects (FE) estimation is in favour of due to the Hausman Test

suggest to reject the null hypothesis that random effect is suitable. The

implementation of fix-effect is utilised to eliminate the endogeneity problem as much

as possible.

With regard to capturing the persistence of NPLs ratio performance, this chapter

extends the investigation by use dynamic specification. It includes a lagged of logit

transformed dependent variable as an independent variable on the right-hand side as

well as adding the time dummy variables. The difference GMM method is necessary

and well suit to be applied here to evade unbiased and endogenous estimations issues

(Salas and Saurina 2002). For GMM estimator, using instruments in levels, i.e.

𝑦1,3-< has no singularities and much smaller variance, which is recommended as

instrument variable in GMM estimation (Baltagi 2012). Hence, LogitNPL1,3-< is

implemented as internal instruments for the explanatory variables to eliminate the

endogeneity issue and make this model more consistent.

Page 26 of 66

Chapter 4 Empirical Findings

This chapter summarised the trait of the dataset by observing the diagnostics tests of

multi-correlation, homoscedasticity test, Durbin-Watson test, normal distribution,

etc., which helps to identify the problems and modified the model into a proper one.

Moreover, this section further discussed empirical findings of this research by

focusing on the influence of each independent variables, linking with the empirical

literature to explain their signs and influences on the dependent variable. Lastly, a

robustness test is implied to capture the robustness of each independent variables

further.

4.1. Descriptive Statistics Analysis

Table 4.1 depicted the observing variables over Europe and Asia over last decade.

Overall, each variable incorporates 1011 observations, which are unevenly

distributed over the observed period. The macroeconomic variables from each

country, compared with NPLs ratio, is flat but still shows high variability across

times and countries, especially the real effective exchange rate. The exchange rate in

the observation countries witnessed a gentle appreciation tendency in general (Figure

4.1). Fluctuation of exchange rate changes in Poland experience the highest boom

during the financial crisis, it went from -12% to approximately 30% but dropped

sharply and still unsteady afterwards. Similar fluctuation happened in other countries

while these fluctuations are at a lower level.

Figure 4.1 Exchange Rate Fluctuation

-20.0

-15.0

-10.0

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Austria

German

Spain

France

Poland

China

Philippine

Indonesia

Thailand

Vietnam

Page 27 of 66

-8-6-4-202468

1012141618

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

China Indonesdia Phillippine ThailandVietnam Austria German SpainFrance Poland

Matching with the real GDP changing performance (Figure 4.2) over 2005 – 2015,

there is a noticeable depreciation of exchange rate in ten countries at different levels

after the financial crisis. The recovery of local currency appreciation varies across

the observing economics in accordance with their economy situations, which resulted

in divergent levels of impaired loans ratio. The standard deviation in the descriptive

statistics stands for the volatility of each data for difference variables.

Figure 4.2 Real GDP Change Performances

Table 4.1 Descriptive Statistics, 2005-2015

Variable Obs. Mean Median Max Min Std. Dev.

Dep

. NPLs 1011 4.92 3.44 86.49 0.04 6.27 LogitNPLs 1011 -1.48 -1.45 0.81 -3.40 0.46

Mac

ro. INFR 1011 2.80 2.19 23.12 -0.95 2.68

G_GDP 1011 4.04 3.70 16.29 -5.57 3.81 REER 1011 0.47 -0.89 29.49 -13.81 7.81

Ban

k–le

vel LTAR 1011 56.44 59.18 93.22 0.10 17.96

ROAE 1011 5.90 1.40 44.25 -277.36 12.87 G_LOAN 1011 14.49 10.46 820.62 -96.80 35.67

Table 4.1 indicates that NPLs ratios varied significantly across countries and banks

over 2005 – 2015 as its standard derivation reaches 6.27. The bank-internal data

records a higher variability in all variables, even the lowest standard derivation

Page 28 of 66

(ROAE) is approximately two times (12.87) higher than NPLs. The bank

fundamentals have demonstrated the worsening of banks’ asset quality and loan

portfolio since the burst of global financial crisis, which is clearly proven in the

financial statements in all banks. However, if it’s separated into two regions, it can

be seen that the NPLs ratio in Asia witnessed a decrease while the Europe

experienced an increasing tendency after the financial crisis. The figure 4.3 showed a

significantly improvement on banks’ NPLs ratio in 2007 for European countries,

with a downfall at the year 2010 and went back remained at a high level afterwards.

Whilst, the situation in Asia is experiencing a falling tendency over the period.

Figure 4.3 NPLs Ratio in Two Regions

4.2. Main Findings

4.2.1 Multicollinearity

Table 4.2 depicts the correlation level among different variables with each other for

the whole observed data, which is an alternative way to detect multicollinearity issue.

A correlation coefficient of 1 (-1) indicates the value is positively (negatively)

correlated with the other variable. There is a common measure of the size of the

effect that value of ±0.1 means a low-level correlation, while any value over ± 0.75

demonstrates that any estimated variables are strongly correlated with each other

(Field 2012). Obviously, all of the variables are entirely independent of each other as

the highest correlation result is 0.368, indicating that the variables don’t suffer from

multicollinearity issue.

1.52.02.53.03.54.04.55.05.56.06.57.0

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

NPl

s/G

ross

Loa

ns

Year

Europe

Asia

Page 29 of 66

Table 4.2 Correlation Matrix

LogitNPLs REER G_GDP INFR G_LOAN LTAR ROAE LogitNPLs 1.000 REER 0.067 1.000 G_GDP -0.227 -0.254 1.000 INFR -0.100 -0.109 0.368 1.000 G_LOAN -0.106 -0.107 0.150 0.137 1.000 LTAR 0.088 0.075 0.002 0.004 -0.088 1.000 ROAE -0.206 -0.019 0.325 0.273 0.118 0.015 1.000

4.2.2 Homoscedasticity

All dataset was set as unstructured data on the Eviews to allow for the standard OLS

regression. The heteroscedasticity test outcomes confirmed that OLS model doesn’t

suffer from homoscedasticity issue. In the GMM estimation provides consistent and

efficient estimates of the parameters in White weighing matrix (Arellano and Bond

1991), accordingly, the selected variables are consistent to heteroscedasticity as the

model automatically selected with the White-weighing matrix and white coefficient,

which keeps the model away from the heteroscedasticity problem.

4.2.3 Normal Distribution

It’s clear that the logit transformation of NPLs demonstrated a comparatively less

volatile distribution (Figure 4.4). It can also be identified by a relatively small

disparity on the value range from -3.40% to 0.81%.

Figure 4.4 Distribution of Logit transformed NPLs ratio

-3.5

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

-4 -3 -2 -1 0 1

Quantiles of LOGITNPL

Qua

ntile

s of

Nor

mal

0

50

100

150

200

250

-3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

Frequency

LOGITNPL

Page 30 of 66

Figure 4.4 displayed a normal distribution of the dependent variable, whose value

range is within -3.5 to 1.0. Most of the variable are normally distributed, except for

real GDP growth rate and exchange rate. Compared with other variables, real GDP

growth and exchange rate are more volatile, and their frequency is quite uneven

(Appendix 2 and 3).

4.2.4 Durbin-Watson Test

According to the Hausman Test, the first model is run under fixed-effect panel

regression. The initial R square is relatively small (64.0%), which mean the overall

goodness of the original model is comparatively small, the results of independent

variables only explain 64% of NPLs (Field 2009). Meanwhile, the Durbin-Watson

result was only 0.984, which dropped in the significant positive autocorrelation

period, which means the model suffers from Autocorrelation issue.

Table 4.3 Significance Period Range for Durbin-Watson Result

Significant Positive

Autocorrelation

No Decision

No Significant Autocorrelation

No Decision

Significant Negative

Autocorrelation

0

1.613 (1.735 2.265) 2.387 4

Therefore, a lag of the dependent variable is utilised to resolve the autocorrelation

issue, and the new result is displayed in model 1 (Table 4.3). Apparently, the new

Durbin-Watson result went up to 2.107, falling in the No Significant Autocorrelation

period range (Table 4.4). The result indicates that new model does not have the

Autocorrelation problem. At the same time, the R-square reaches 82.5%, which

means the output of Model 1 suggests that the variability of NPLs is well explained

by both macroeconomic and financial variables at 82.5%.

4.2.5 Empirical Results

Table 4.3 reports the estimated coefficients and their p-values of the fixed-effect

panel LS and dynamic panel regression by difference GMM. Overall, the designed

models are capable of interpreting the NPLs ratios fluctuation across observing

countries in Europe and Asia well. Both the fixed effect panel LS and difference

GMM reveal that real GDP growth, Inflation Rate, Loan/Asset Ratio, Return on

Page 31 of 66

Average Equity and Growth Gross Loan have an significant impact on NPLs while

the evidence on the Real Effective Exchange Rate is quite mixed (Appendix 4).

Table 4.4 NPLs: Macroeconomic and Bank-Level Determinants, 2005-2015 Fixed-Effect Panel LS Difference GMM Model 1 Model 2 LogitNPLs (-1) 0.703***

-0.024

INFR 0.010*** 0.023*** -0.004 -0.006 G_GDP -0.016*** -0.016*** -0.003 -0.004 REER 0.002*** -0.008*** 0.000 -0.001 LTAR -0.239*** -1.215*** -0.115 -0.253 ROAE -0.286*** -0.020* -0.067 -0.148 G_LOAN -0.104*** -0.116*** -0.019 -0.022 Constant -0.314***

-0.067

time dummy no yes Number of Obs. 918 826 R-square 0.824975

Adjusted R-square 0.804031

Durbin-Watson stat 2.107175 Number of banks 92 92

Number of instruments 54 NOTES: Significance level: *, **, *** denotes significance at10%, 5% and 1% respectively. The standard error of each variable is put in the bracket. An increase in REER reflects an appreciation. Dependent variable: LogitNPLs

The standard error measures the uncertainty of each estimated parameter, the larger

the standard error, the greater the uncertainty about the estimated parameter value

(Ryan 2009). Thus, ROAE and LTAR have higher risk level compared with other

variables. Therefore, the model of fixed-effect panel OLS can be interpreted as

below:

Page 32 of 66

𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3 = −0.314 + (−0.286𝑅𝑂𝐴𝐸1,3) + (−0.019𝐺]^_`1,3)

+ (−0.239𝐿𝑇𝐴𝑅1,3) + (−0.016𝐺abc1,3) + 0.002𝑁𝐸𝐸𝑅1,3

+ 0.01𝐼𝑁𝐹𝑅1,3

Where 𝐿𝑜𝑔𝑖𝑡𝑁𝑃𝐿1,3 is the indicator of credit risk for country 𝒾 and time 𝑡.

𝑅𝑂𝐴𝐸 is the Return on Average Equity,

𝐺_𝐿𝑂𝐴𝑁 refers to the Gross Loan Growth Rate,

𝐿𝑇𝐴𝑅 denotes Loan-to-Asset ratio,

𝐺_𝐺𝐷𝑃 represents real GDP growth,

𝑅𝐸𝐸𝑅 means real effective exchange rate and

𝐼𝑁𝐹𝑅 is inflation rate.

The results in Model 1 demonstrated that all independent variables have significant

impacts on problem loans, and all the significant level is at 1%. They are in line with

the majority of the empirical studies while in contradictory with minorities, which

would be explained later in the discussion part.

In dynamic panel estimation, the first period lagged dependent variable is added into

Model 2 to capture the persistence of dependent variable. As discussed before, GMM

is the most effective and efficient estimator in dynamic panel data analysis. Thus,

first difference GMM was taken in Model 2. Added the period dummy variable, the

duration in model 2 automatically excluded some periods and started from 2007-

2015. The internal instrument, LogitNPL1,3-<, was confirmed by the Sargan test to be

a valid instrument in this model.

Overall, the evidence in Table 4.3 demonstrates that both macroeconomic shocks and

bank fundamental indicators are significantly associated with problem loans in

Europe and Asia. The general outcomes indicated that nonperforming loan worsens

significantly when GDP decrease, inflation rate grows and appreciation in the real

exchange rate as well as a descendant in bank profitability (ROE) and a slowdown in

gross loan growth rate. However, the performance of Loan/Asset ratio is not in line

with the majority of empirical studies, which would be deeply analysed later.

Page 33 of 66

Observed the results in a comprehensive perspective, it can reach a common

conclusion that worsening in the economic environment and descendent in bank

performance could lead to ascendance in credit risk in banking sectors. The majority

result is in line with findings from Bercoff et al. (2002), Cotugno et al. 2010, Festi

et al. (2011), Louzis et al. (2012), Vatansever and Hepsen (2013) and Beck et al.

(2015).

4.3. Discussion of Empirical Findings

4.3.1 Inflation Rate

The inflation rate is commonly regarded as a signal of macroeconomic

mismanagement and a source of uncertainty (Quagliariello, 2003). High inflation

rate is considered to be associated with wider exposure to risky loans. The coefficient

in two models displayed a significant positive linkage between inflation rate and loan

quality, and inflation rate is significant at 1% confidence interval. In FE estimation,

an increase of one unit change in inflation rate emerges to an ascendance in NPLs

ratio of about 0.01 unit changes, ceteris paribus. The result is in consensus with Arpa

et al. (2001); Babihuga (2007) and Ahamd and Bashir (2013), although it’s not

similar with Castro’s (2013) finding that inflation is not relevant to credit risk. It is

because he included both real value of outstanding loans and borrower’s actual

income, the another one could cancel the effect. While in our case, there is no other

variable to eliminate the effect of inflation. Therefore, the impact of inflation rate on

the dependent variable in model 2 become stronger after added the time dummy in

difference GMM estimation. It’s clear that one unit increase in inflation conduct to

0.023 units increase in credit risk. It can be justified that the inflation rate is stable

and leads to less impact on the NPL before the financial crisis. When splitting away

the pre-crisis period, inflation rate was found to affect the loan losses in banks

substantially. As inflation usually increase risks and uncertainties for market

participants in general, which lead to higher problem loans, which is always a proxy

of macroeconomic mismanagement (Arpa et al. 2001). Moreover, Derbali (2011)

also reported a positive association between inflation and bank profitability, which

demonstrated that the dynamics of inflation rate and bank profitability affected

directly bank loan portfolio, which supports the financial accelerator theory that

Page 34 of 66

deterioration of bank assets value could be magnified by the combination of both

factors.

The outcome of inflation rate matches the initial prediction of its expected sign, thus,

in hypothesis test 1, the models failed to reject that null hypothesis that inflation rate

is insignificant to NPLs ratio.

4.3.2 Growth of Real GDP

As expected, a decrease in real GDP growth is associated with a rise in non-

performing loan ratios in both models as expected. The real GDP growth is

significant at 1% confidence interval. Both outputs in fixed-effect LS (Model 1) and

difference GMM estimation (Model 2), reached the same negative significant level.

The result is consistent with the majority of current studies (Fernandez et al. 2000;

Ahmad 2003; Quagliariello 2007; Khemraj and Pasha (2009); Ahmad and

Bashir (2013); Beck et al. 2015). Although some single country studies failed to

find a significant relationship with GDP growth rate, which is not representative. The

results in both models show that if other independent variables are kept fixed, one

unit change in real GDP growth is associated with 0.016 unit changes in NPLs. A

deterioration in the real economy always results in an increase in potential loan

losses in banking sectors’ loan portfolio. An increase in GDP is usually assisted in

the individual income growth, which is typically linked with a rise in profitability

(Messai and Jouini 2013). It can be proven from the later result that bank

profitability is negatively significant with problem loans. GDP growth is a signal of

economic boom, which plays an imperative role in individuals and firms’ debt-

paying ability as higher personal income and profitability add to their capability to

fulfil their financial obligations and help to decline problem loan accumulation. The

findings reveal the fact that booming periods adversely improves the loan losses,

which point out the importance of economic policies give rise to the economic

growth and avoid serious problems of credit default and banking crisis (Castro

2013).

Thus, this paper rejects H0 in hypothesis test 2 that GDP is not significant for

nonperforming loans. On the contrary, real GDP growth rate appears to have a

significant negative impact on loan losses.

Page 35 of 66

4.3.3 Real Effective Exchange Rate

The REER appears to be highly significant in the nonperforming loan ratio, which is

in line with some empirical literature reviewed in chapter two (Arpa et al. 2001;

Castro 2013 and Khemrai and Pasha 2009). A real exchange rate appreciation was

correlated with a worsening in the current account because the goods and service

become more expensive in that country. Thus, countries with currencies appreciation

experienced a larger deterioration in their local economic environment (Corsetti et

al. 1998). REER is significant at 1% confidence interval and matches the expected

positive signal. One unite increase in REER leads to 0.002 units increase in problem

loans, ceteris paribus.

However, in difference GMM estimation, the coefficient direction changed from

positive to negative. Meanwhile, one unit change in NPLs is associated with 0.008

units decrease in REER, ceteris paribus. It could be explained by the countries that

we are observed that Poland, Thailand, Philippine, and Vietnam they have substantial

fluctuation in their currency exchanges over the observed period, as the economy in

South East Asia are more fragile after the Asian financial crisis in 1997. Indonesia,

Thailand and Philippine are most affected by the crisis, they experienced a quite high

exchange rate appreciation (Corsetti et al. 1998), which is associated with a

worsening economy. Hence, when excluding the pre-crisis period (2005-2006) in the

GMM estimation, these Asian countries reflects a high volatile in their local

currencies. Beck et al. (2015) got the negative relationship between nominal

effective exchange rate and asset quality, tested with countries dummy variables and

various level of international claims. As appreciation in local currency makes the

products get higher prices than its initial value. While, massive depreciation of local

currency causes an increase in the amount of money to buy the same products,

especially foreign merchandises, which add up to the burden of the local economy.

Consequently, it’s explainable that a vast depreciation in the local currency increase

the bank loan losses. Noticeably, it can provide a more precise estimation on the

impact of exchange rate if other factors like export or foreign trading indicators could

be added in economic model. As the result in difference GMM estimation is more

robust. This paper concludes a negative relationship with regard to outputs in Model

2.

Page 36 of 66

This project rejects H0 in hypothesis test 3 that REER is insignificant for NPLs. On

the contrary, REER emerges to have a significant impact on credit risk.

4.3.4 Return on Average Equity

Economic activities are not able to fully explain the evaluation of non-performing

loans. Thus, beyond macroeconomic indicators, impressive results also appear in

bank-specific variables as well.

As far as ROAE is concerned, it is regarded as the proxy for bank profitability. The

ROAE in fixed-effect panel LS is recorded a significant negative relationship to

NPLs, which is a consensus with Godlewski (2004); Cotugno et al. (2010); Louzis

et al. (2012), Makri et al. (2013). Model 1 tells that ROAE is significant at 1% if

keep other variable consistent, one percentage increase in ROAE can result in 0.286

percentage decrease in the nonperforming loan. The negative relationship is

supported by previous studies on the bank profitability. Outputs in GMM estimation

emerges a less level significance (10% confidence interval) effect for ROAE over

NPLs. One unit change in NPLs is associated with 0.02 unit changes in ROAE,

ceteris paribus. The negative relationship proves that high bank profitability evades

the risk of liquidity and solvency issues in banks as it introduces continuous cash

flow into banks, which could reduce the bank’s problem loan ratio.

However, some studies proved an opposite relationship between ROE and NPLs

ratio, for instance, Vatansever and Hepsen (2013), which makes the signal for ROE

as profitability to determinant NPLs a bit confusing. Therefore, relevant information

was found in Sundararajan et al.’s (2002) study that an analysis of profitability by

using ROE encountered with a greater risk of ignoring high leverage level. As bank’s

leverage is often determined by regulation, while regulation is not easy to detect,

hence, ROA tends to be more representative when measuring bank profitability

(Babihuga 2010). Nevertheless, results in two models are capable of representing the

bank profitability when evaluating bank performance. As discussed in inflation rate

and GDP growth sections, dynamic of GDP growth, inflation rate and bank

profitability is related with performance of individuals and corporate’s debt payback

ability, consequently, credit risk level in banks. Hence, the finding confirmed that

high profitability is less pressured to revenue creation and, accordingly, less

constrained to engage in credit risk offerings (Haneef et al. 2012).

Page 37 of 66

Thus, hypothesis test 5 rejected the H0 as ROAE appears to be insignificant to NPLs

in difference GMM estimation.

4.3.5 Growth of Gross Loan

In accordance with the result in Table 3, Growth of Gross Loan in both models

displays a negative relationship with problem loan and significant at 1% confidence

interval. One unit change in loan growth rate is associated with -0.116 units change

in loan default ratio, ceteris paribus. Loan growth is expected to have a high potential

to bring excessive problem loans (Salas and Saruina 2002; Bikker and

Metzemakers 2005; Cotugno et al. 2010). Rapid loan growth has been regarded as

an imperative factor that increases the risk of a crisis as it takes place during

economic boom times and such lending booms (Caprio and Klingebiel, 1996).

However, the result in this paper turns out to be contradictory with the initial

expectation.

Salas and Saurina (2002) failed to find a significant relationship with loan quality.

Moreover, there still exist some current research emerge an opposite relationship

between problem loan and loan growth, such as Bonfim (2009), Cavallo and

Majnoni (2002) and Laeven and Majnoni (2003). In early research, Keeton (1990)

explained that loan growth is driven by the nature demand by individual or

corporates, that is, increasing in lending may not necessary lead to loan losses. Kraft

and Jankov (2005) found that rapid loan growth increased the probability of credit

quality deterioration. However, they pointed out that it is too simple to reply purely

on rapid loan growth rate to explain the problems loans in banks because it’s difficult

to capture the correlation between lending growth rate and default probabilities. If

combined with other destructive business policies, like heavy reliance on paying

above-average interest rates on deposits or interbank funding, rapid loan growth can

contribute to a deadly consequence for banks. The negative sign of loan growth rate

in this paper implies that there may exist a real natural demand of loans unrelated to

the creditworthiness of borrowers or potential good monitoring in place. Their

existence is to control the quality of lending among examined countries and observed

periods, which conducted to an increase in loan growth resulting in a decrease in

nonperforming loans.

Page 38 of 66

This paper rejects H0 in hypothesis test 5 that G_LOAN is insignificant to NPLs. By

contract, G_LOAN emerges to have significantly impact credit risk.

4.3.6 Loan to Total Asset

Bank’s excessive lending and banks risk-taking ability are usually proxy by

loan/total asset ratio. The impact of loan/total asset ratio decreases the NPLs

significantly at 1% confidence interval in two models. In the difference GMM

estimation, the effects is stronger. However, the results are contradictory to the main

empirical findings (Cavallo and Majnoni 2002; Männasoo and Mayes 2009;

Cotugno et al. 2010; Festic et al. 2011) as the empirical test on loan/asset ratio is

expected to have a positive correlation with problem loans in banks. The share of

banks’ loans to total banking assets counts as a proxy of excessive-risks taken in the

banks. The higher the ratio indicated, the riskier a bank would be to encounter with

higher defaults. Accordingly, loan/asset ratio is usually correlated with banking

problems and increase the NPL ratio, which could cause solvency issue due to

mismanagement in banks. However, a negative relationship between loan/asset ratio

and bank distress was found in early warning models, which means lending activities

is underdeveloped (Männasoo and Mayes 2009).

As the loan to total asset ratio measures the gross loans outstanding as a percentage

of total asset, the negative relationship indicates one-unit change occurs in loan/asset

ratio the change in the NPLs is 1.215, ceteris paribus. It can firstly be seen by the

negative sign of gross loan growth as discussed before, the loans growth in the

observed banks didn’t necessarily cause an increase in bad loans. If the increasing

speed in the total asset is larger than or no growth in the gross loan, it will result in a

decrease in bad loans ratio although the general performance in loan/asset ratio is

increasing over the period. Moreover, it can also be concluded that lending activities

is immature in some countries and is a marginal part of banks activities in developing

markets, like Thailand, Indonesia, Philippine and Vietnam. Thus, it exists the

situation that the increase in loan/Asset ratio also demonstrates an adverse movement

for the problem loans in this project.

This paper rejects H0 in hypothesis test 6 that LTAR is insignificant to NPLs. On

contradictory, LTAR emerges to have significantly opposed impact credit risk.

Page 39 of 66

Overall, the results in FE panel LS and difference GMM estimation indicated that

both the macroeconomic conditions and financial fundamentals are significant

determinant variables on banks’ credit risk, proxy by impaired loan ratio. The

dynamic of GDP growth, inflation rate and exchange rate are proven to have a

statistical significance with credit risk level in banks, though the performance of

exchange rate appears to have a different sign on determining NPLs in two models.

In the examination of REER, related foreign trading indicators are suggested to be

added in future studies. Macroeconomic deterioration only lacks a strong voice to

explain the bank loan losses. Hence, some bank-level factors also demonstrated a

substantial effect on the explanation of bad loans in banks, like ROAE, loan growth

rate and loan/asset ratio. Two variables conduct to an adverse sign, which is out of

expectation and majority of empirical studies. It also requires other factors adding

into the model so as to provide a better evaluation of bank default rate. The

combination of macroeconomic and bank internal factors is able to magnify the

deterioration of the asset value, which supported by the financial accelerator theory.

4.4. Robustness Test

A further robustness test is implied in this paper by separate the analysis into two

regions, Europe and Asia, within the observed period (2005 – 2015). In the

robustness check, this research only applied one of the specifications used before,

difference GMM estimation, to test the robustness of the coefficients among

macroeconomic and bank internal factors.

Comparing with the dynamic panel specification results in Table 4.3, the results in

Europe appears to be stronger. Six determinants demonstrated significant impacts on

the problems loans in banks. However, four factors displayed an opposite sign

towards dependent variable, like INFR, GDP growth, REER and ROAE, which is

different from the main findings. Only loan/asset ratio and loan growth rate remain

the adverse relationship with loan losses although the coefficient is smaller, as it

takes away the impact from Asian countries. The results show that loan performances

are more robustness in determining the credit risk in banks.

Page 40 of 66

Table 4.5 Robustness Test Difference GMM Europe Asia Model 2 LogitNPLs (-1) 0.623*** 0.540*** 0.703*** 0.014 0.055 -0.024 INFR -0.051*** -0.006 0.023*** 0.008 0.008 -0.006 G_GDP 0.008*** -0.000 -0.016*** 0.002 0.004 -0.004 REER 0.013*** -0.003* -0.008*** 0.002 0.002 -0.001 LTAR -0.011*** -0.005*** -1.215*** 0.001 0.002 -0.253 ROAE 0.014*** 0.004*** -0.020* 0.001 0.001 -0.148 G_LOAN -0.001*** -0.007*** -0.116*** 0.000 0.001 -0.022 Period 2007-2015 2007-2015 2007-2015 Number of Obs. 448 375 826 Number of instruments 50 42 54 Time dummy yes yes yes Number of banks 50 42 92

NOTES: Significance level: *, **, *** denotes significance at10%, 5% and 1% respectively. The standard error of each variable is put in the bracket. An increase in REER reflects an appreciation. Dependent variable: LogitNPLs By contrast, the inflation rate and GDP growth in Asia emerge to have no significant

impact on credit risk in banks as the results show an insignificant impact towards

dependent variable. As the GDP growth in China witnessed a significant growth

while other countries show different levels of fluctuation and decrease, which could

cancel the effects from GDP growth to nonperforming loans over the period.

Analogous to European countries, loan/asset ratio and loan growth rate significantly

determine loan default rate in Asian banks and keep the same sign as the initial

expectation as well. Moreover, REER appears to be a robust factor in determining

the NPLs in Asia. ROAE indicated a positive relationship with NPLs, which is the

same as that in Europe while opposite to the main findings in this paper.

In general, loan/asset ratio and loan growth rate is concluded to be more robust in

both Europe and Asia over the last ten years. Some other factors turn out to be

different from the initial findings in this paper (Appendix 4), which suggestes the

economic situations in two regions proved to be a large difference, particularly in

emerging markets.

Page 41 of 66

Chapter 5 Conclusion, Limitations and Recommendations

This chapter presents a summary of the main findings of this project research, aiming

to gain achievements matching with the initial incentives and research objectives of

this study. Apart from that, limitations are discussed in this chapter. Accordingly,

recommendation for the policy makers and potential future research based on this

study are also provided in this section.

5.1. Conclusion

The recent financial crisis has revitalised the public passion for exploring the features

triggering a banking crisis and impact of the crisis on the economy. Nonetheless,

some attentions should be given to the credit risk in banks before laying full

emphasis on the analysis of banking crisis. In fact, liquidity issue caused by problem

loans in banks’ financial statement can lead to banks solvency and consequently

resulting in a banking crisis. Hence, it’s necessary to consider the factor that

warming up credit risk as an origin of understanding the banking and financial crisis.

By using econometric panel analysis, this project incorporated six core determinants

to evaluate their impacts on banks’ credit risk behaviour through studying systematic

shocks and internal unsystematic functions among ten countries in Europe and Asia

over last decade. In order to capture a better understanding of credit risk, this paper

applied both fixed-effected and difference GMM estimation and compared results

from both sides by benchmarking the nonperforming loan ratio. The credit quality of

the portfolio has been modelled using the NPL ratio. The results in this project

proved strong evidence that adverse macroeconomic activities and dysfunctional

management significantly affect the credit risk level in banks.

Consistent with the theory as well as some earlier studies, business cycle (proxy by

GDP growth) has a significant opposite relationship with problem loan. Descent in

economic growth lowers down borrowers’ ability to pay back their debts as a worse

economy cuts income and profitability of debtors, conducting to a boom in NPLs.

The negative relation implies that control actions should be reinforced at the rest

signs of changes in the economic cycle (Salas and Saruina 2002). On the other side,

the inflation rate is positively connected with latent loan losses. Inflation is always

Page 42 of 66

accompanied by risk and uncertainty which is also associated with bank profitability.

High inflation could expand the possibility of higher loan losses. Likewise, Real

effective exchange rate also displayed a significant a positive relation in the fixed-

effect model and a negative relationship in difference GMM estimation with default

loan profitability. As the case in this project included some developing countries like

Thailand, Philippine and Indonesia who witnessed massive currency depreciation

after the Asian financial crisis (Corsetti et al. 1998). Depreciation in local currency

adds to the burden in purchasing the same products especially commodities overseas.

Hence, additional factors, like export or import, are plausible to be included to

explain the credit risk model more soundly.

In addition to adverse macroeconomic shocks, this paper also shed light on the

insight of impact from bank-specific characteristics on banking sectors by observing

bank profitability, excessive lending and risk-taking ability. Within expectation, bank

profitability verified a significant negative linkage with NPLs ratio as high

profitability assist banks escaping from severe liquidity and solvency issues.

Excessive risk-taking (proxy by loan/asset ratio and loan growth rate) was found to

be significant, but adversely, relevant to credit risk in banks in European and Asian

countries. Two coefficient of these two variables ranked top two among six variables,

confirming a substantial impact on the lending activities and loan quality on bad

loans. One unit changes in loan growth rate is associated with -0.116 unit changes in

loan default ratio, and one-unit fluctuation in loan/asset ratio are connected with -

1.215 unit changes in impaired loan ratio, ceteris paribus. The results are

contradictory to a majority of studies as lending activities only are too simple to

capture its impact on NPLs. Thus, it’s compulsory to pay extra attention to the real

demand for the loans and some bad business policies in different countries as a

combination of these factors could result in unpredictable effects on banks. Banks

with higher loan-to-assets ratios stand out as more advanced in these markets

(Männasoo and Mayes 2009).

A robustness check is implied to check the coefficient for the impact macroeconomic

and bank-level fundamentals separately in Europe and Asia. The outcomes in two

different regions depicted quite distinguished consequences. INFR, GDP growth,

REER and ROAE exhibited opposite sign from the main findings in Europe. GDP

and inflation rate turn out to be insignificant to NPLs in Asia. REER is still a

Page 43 of 66

robustness determinant in Asia. ROAE in both countries appears to be positively

associated with NPLs. Nonetheless, loan/asset ratio and gross loan growth rate are

the two most robustness factors negatively determining the problem loans both

Europe and Asia.

Findings above ascertained the fact the research by observing the factors above is

quite meaningful in predicting and controlling potential banking crisis in different

countries. Adverse business cycles contribute to banking frangibility and in return,

the dysfunctional behaviour of banks adversatively affects the economic cycle.

5.2. Limitations

As discussed in previous chapters, there exist several boundaries in this research

project, which explained some results in the main findings is not persistent in the

robustness check and contradictory to empirical studies.

1. As discussed in chapter four, usage of ROAE as a proxy for bank profitability

would exist the risk of negligence of high leverage level in banks (Sundararajan et al.

2002). Babihuga (2010) also suggested that ROA is more suitable in representing

banks’ profitability.

2. In the examination of real exchange rate, the factor itself exists limitation on

analysing its impact on loan losses as currency appreciation or depreciation has a

stronger effect on the countries who has larger or smaller foreign exchange reserves,

imports and exports, and hedging performance in currency performance. It’s

plausible to incorporate foreign trading indicators to better capture their impact on

exchange rate appreciation and depreciation.

3. Rapid loan growth rate alone is unable to explain the problems loans in banks fully.

If combined with some other devastated business policies, like heavy reliance on

paying above-average interest rates on deposits or interbank funding can magnify a

deadly consequence for banks.

4. The observation in countries and variable is insufficient, a larger range of

countries and more variable would help to interpret the credit risk more precisely.

Page 44 of 66

5.3. Recommendations

Findings in this paper on the influence of macroeconomic variables and bank-

specific factors have implications for the conduct of macroeconomic policy as well

as internal bank regulations establishment. Policymaking in both sides plays an

essential role in justifying a proper solution in economic and financial imbalance.

For an economic perspective, steady growth, without deep recessions that put the

survival of risk, and without too-rapid growth that is based on a robust expansion of

bank loans, is the best macroeconomic policy for keeping a low level of problem

loans. A pro-cyclical business circle provides a healthier economic environment for

banking sectors. Hence, the role of government in reducing NPLs level in banks is

crucial. As government can open their market to attract foreign investment to inject

cash flow into the local economy, which could also create more job and production

in the market. Free trade agreement with the neighbour countries would also assist

the export and import trading to enhance the increase of local economy.

On the other side, it has got more explicit that bank regulators should lay more

explicit emphasis on the control and scrutiny associated with loans quality to

eliminate bad loans. Lending activities are still immature in some developing

countries, while a strong focus on prudential regulation, particularly through proper

liquidity provisions and buffers, could help mitigate the impact of macroeconomic

risk on the banking system and create a stable banking system in all countries.

5.4. Further Research

The current study has used panel OLS and difference GMM estimator to test six key

macroeconomic and bank-specific variables, whereas future studies can use other

systematic, i.e. unemployment rate, lending interest rate, housing price, and

unsystematic variables, capital adequacy ratio, return on asset, to investigate the

NPLs behaviour in depth. As discussed in the limitation section, larger time span and

a wider range of countries could add in so as to capture a more precise understanding

of the cause of the credit risk in banks. Other aspects like proxies of regulations,

policies and mechanisms can also be examined to gain a deeper research on its

intervention on loan default rate as well as its potential effect on a banking crisis.

Page 45 of 66

Reference

Agnello, L. and Sousa, R. M., 2012. How do banking crises impact on income

inequality? Applied Economics Letters, 19 (15), 1425–1429.

Agung, G. N. I., 2013. Panel data analysis using EViews (1). United Kingdom:

Wiley, John & Sons.

Ahmad, F. and Bashir, T., 2013. Explanatory power of macroeconomic variables as

determinants of non-performing loans: Evidence form Pakistan. World Applied

Sciences Journal, 22 (2), 243–255.

Ahmad, N., 2003. Credit risk determinants: by institutional type. Proceedings of

Malaysian Finance Association Conferences.

Ahmad, N. H. and Ariff, M., 2007. Multi-country study of bank credit risk

determinants. International Journal of Banking and Finance, 5 (1), 125–152.

Amuakwa-Mensah, F., Marbuah, G., Sam, V. N. and Barimah, A., 2015. Credit risk

and universal banking: evidence from the banking industry in Ghana. International

Journal of Computational Economics and Econometrics (IJCEE), 5 (4).

Arellano, M. and Bond, S., 1991. Some tests of specification for panel data: Monte

Carlo evidence and an application to employment equations. The Review of

Economic Studies, 58 (2), 277.

Ariff, M. and Marisetty, V. B., 2001. A New Approach to Modelling Multi-Country

Risk Premium Using Panel Data Test Method. Proceedings of MFS Conference in

Cyprus.

Armitage, P., Berry, G. and Matthews, J. N. S., 2001. Statistical Methods in Medical

Research. 4th edition. Blackwell: Wiley.

Arpa, M., Giulini, I., Ittner, A. and Pauer, F., 2001. The influence of macroeconomic

developments on Austrian banks: Implications for banking supervision. BIS Paper

[online], 1, 91–116. Available from: http://www.bis.org/publ/bppdf/bispap01c.pdf

[Accessed 5 July 2016].

Page 46 of 66

Babihuga, R., 2007. Macroeconomic and financial soundness indicators: An

empirical investigation. IMF Working Papers, 07 (115), 1.

Babouček, I. and Jančar, M., 2005. Effects of Macroeconomic Shocks to the Quality

of the Loan Portfolio. Czech National Bank Working Paper.

Balavac, M., 2012. Determinants of export diversification at the export margins:

Reference to transition economies [online]. ETSG 2012 Annual conference paper.

Staffordshire: Business School, Staffordshire University. Available from:

http://www.etsg.org/ETSG2012/Programme/Papers/288.pdf [Accessed 24 August

2016].

Baltagi, B. H., 2012. Econometric analysis of panel data. 4th edition. Chichester,

United Kingdom: Wiley, John & Sons.

Beck, R., Jakubik, P. and Piloiu, A., 2015. Key determinants of non-performing

loans: New evidence from a global sample. Open Economies Review, 26 (3), 525–

550.

Bercoff, J., Giovanni, J. and Grimard, F., 2002. Argentinean Banks, Credit growth

and the Tequila Crisis: A duration analysis. Journal of Finance.

Berger, A. N. and DeYoung, R., 1996. Problem loans and cost efficiency in

commercial banks. Journal of Banking & Finance, 21 (6), 849–870.

Bernanke, B. and Gertler, M., 1989. Agency costs, net worth, and business

fluctuations. The American Economic Review [online], 79 (1), 14–31. Available from:

http://www.jstor.org/stable/1804770?seq=1#page_scan_tab_contents [Accessed 9

August 2016].

Bernanke, B., Gertler, M. and Gilchrist, S., 1999. The financial accelerator in a

quantitative business cycle framework in: Handbook of Macroeconomics. Elsevier

Science.

Bikker, J. A. and Metzemakers, P. A. J., 2005. Bank provisioning behaviour and

procyclicality. Journal of International Financial Markets, Institutions and Money,

15 (2), 141–157.

Page 47 of 66

Blavy, R. and Souto, M., 2009. Estimating default frequencies and macrofinancial

linkages in the Mexican. IMF Working Paper.

Bonfim, D., 2009. Credit risk drivers: Evaluating the contribution of firm level

information and of macroeconomic dynamics. Journal of Banking & Finance, 33 (2),

281–299.

Caprio, G. and Klingebiel, D., 1996. Bank insolvency: bad luck, bad policy, or bad

banking? Annual World Bank Conference on Development Economics.

Castro, V., 2013. Macroeconomic determinants of the credit risk in the banking

system: The case of the GIPSI. Economic Modelling, 31, 672–683.

Cavallo, M. and Majnoni, G., 2002. Do banks provision for bad loans in good times?

Empirical evidence and policy implications. In: Levich, R. M., Majnoni G. and

Reinhart C. M., eds. Ratings, Rating Agencies And The Global Financial System.

The New York University Salomon Center Series on Financial Markets and

Institutions: Springer US, 319–342.

Corsetti, G., Pesenti, P. and Roubini, N., 1998. What caused the Asian currency and

financial crisis? Part I: A macroeconomic overview. NBER.

Cotugno, M., Stefanelli, V. and Torluccio, G., 2010. Bank intermediation models and

portfolio default rates: What’s the relation? [online] 23rd Australasian Finance and

Banking Conference 2010 Paper. 15 August 2010. Available from:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1662888 [Accessed 9 August

2016]

Dash, M. K. and Kabra, G., 2010. The determinants of non-performing assets in

Indian commercial bank: An econometric study. Middle Eastern Finance and

Economics, 7 (7), 106–94.

De Bock, R. and Demyanets, A., 2012. Bank asset quality in emerging markets:

Determinants and Spillovers. IMF Working Papers, 12 (71), 1–26 [online]. Available

from: http://www19.iadb.org/intal/intalcdi/PE/2012/09768.pdf [Accessed 10 August

2016].

Page 48 of 66

De Veaux, R. D., Velleman, P. F. and Bock, D. E., 2011. Stats: Data and models. 3rd

edition. United States: Pearson Education (US).

Derbali, A., 2014. Determinants of banking profitability before and during the

financial crisis of 2007: The case of Tunisian banks. Interdisciplinary Journal of

Contemporary Research in Business, 3 (3).

Dudovskiy, J., 2016. The Ultimate Guide to Writing a Dissertation in Business

Studies: A Step-by-Step Assistance [E-book]. Research Methodology.

El-BannanyMagdi, 2012. Global financial crisis and the intellectual capital

performance of UAE banks. Journal of Human Resource Costing & Accounting, 16

(1), 20–36.

Epure, M. and Lafuente, E., 2012. Monitoring bank performance in the presence of

risk. Journal of Productivity Analysis, 44 (3), 265–281.

Espinoza, R. and Prasad, A., 2010. Nonperforming loans in the GCC banking system

and their macroeconomic effects. IMF Working Papers, 10 (224), 1.

Fernández, S., Pagés, J. and Saurina, J., 2000. Credit growth, problem loans and

credit risk provisioning in Spain. Working Paper, 18, Banco de Espana.

Festić, M., Kavkler, A. and Repina, S., 2011. The macroeconomic sources of

systemic risk in the banking sectors of five new EU member states. Journal of

Banking & Finance, 35 (2), 310–322.

Field, A. P., 2012. Discovering statistics using IBM SPSS statistics. 4th edition.

London: SAGE Publications.

Fischer, K. P., Gueyie, J.-P. and Ortiz, E. C., 2001. Risk-taking and charter value of

commercial banks from the NAFTA countries. The International Journal of Finance,

13 (1), 2027–2043.

Fofack, H., 2005. Nonperforming loans in Sub-Saharan Africa: Causal analysis and

macroeconomic implications. World Bank Policy Research Working Paper.

Page 49 of 66

Fredrick, O., 2012. The impact of credit risk management on financial performance

of commercial banks in Kenya. DBA Africa Management Review, 3 (1), 22–37.

Fuentes, R. and Maquieira, C., 2003. Institutional arrangements, credit market

development and loan repayment in Chile. School of Business and Economics,

Universidad de Chile [online]. Available from:

https://www.researchgate.net/publication/255601273_Institutional_arrangements_cre

dit_market_development_and_loan_repayment_in_Chile [Accessed 12 August 2016].

García-Marco, T. and Robles-Fernández, D. M., 2008. Risk-taking behaviour and

ownership in the banking industry: The Spanish evidence. Journal of Economics and

Business, 60 (4), 332–354.

Gerlach, S., Peng, W. and Shu, C., 2005. Macroeconomic conditions and banking

performance in Hong Kong SAR: a panel data study. Investigating the relationship

between the financial and real economy by bank for international settlements: SSRN,

12, 481–497.

Godlewski, C. J., 2004. Capital regulation and credit risk taking: empirical evidence

from banks in emerging market economies. Economics Working Paper Archive at

WUSTL [online]. Available from:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=588163 [Accessed 10 August

2016].

González-Hermosillo, B., 2006. Determinants of ex-ante banking system distress: A

Macro-Micro empirical exploration of some recent episodes. International Monetary

Fund, 99 (33).

Griffiths, W. E., Hill, C. R. and Lim, G. C., 2011. Using EViews: For principles of

econometrics. 4th edition. United States: John Wiley & Sons.

Haneef, S., Riaz, T., Ramzan, M., Rana, M. A., Ishaq, H. M. and Karim, Y., 2012.

Impact of risk management on non-performing loans and profitability of banking

sector of Pakistan. International Journal of Business and Social Science, 3.

Hoggarth, G., Sorensen, S. and Zicchino, L., 2005. Stress tests of UK banks using a

VAR approach. Bank of England Working Paper Series [online], 282. Available

Page 50 of 66

from: http://papers.ssrn.com/sol3/Papers.cfm?abstract_id=872693 [Accessed 11

August 2016].

International Money Fund, 2016. IMF world economic outlook database list,

information about gross domestic product (GDP) [online]. Available from:

http://www.imf.org/external/ns/cs.aspx?id=28 [Accessed 21 August 2016].

Jakubík, P., 2007. Macroeconomic Environment and Credit Risk. Czech Journal of

Economics and Finance [online], 57 (1-2), 60–78. Available from:

https://ideas.repec.org/a/fau/fauart/v57y2007i1-2p60-78.html [Accessed 12 August

2016].

Jesus, S. and Gabriel, J., 2006. Credit Cycles, Credit Risk, and Prudential Regulation.

International Journal of Central Banking: MPRA Paper.

Jiménez, G. and Saurina, J., 2006. Credit cycles, credit risk, and prudential regulation.

International Journal of Central Banking, 2, 65–98.

Keeton, W. R., 1999. Does faster loan growth lead to higher loan losses? Federal

Reserve Bank of Kansas City, Economic Review, 84 (2), 57–75.

Keeton, W. R. and Morris, C. S., 1987. Why do banks’ loan losses differ? Federal

Reserve Bank of Kansas City, Economic Review, 3–21.

Khemraj, T. and Pasha, S., 2009. The determinants of non-performing loans: an

econometric case study of Guyana. Munich Personal RePEc Archive Paper.

Kiyotaki, N. and Moore, J., 1995. Credit cycles. Journal of Political Economy

[online], 105 (2), 211–248. Available from: http://www.nber.org/papers/w5083

[Accessed 9 August 2016].

Kraft, E. and Jankov, L., 2005. Does speed kill? Lending booms and their

consequences in Croatia. Journal of Banking & Finance, 29 (1), 105–121.

Laeven, L. and Majnoni, G., 2003. Loan loss provisioning and economic slowdowns:

Too much, too late? Journal of Financial Intermediation [online], 12 (2), 178–197.

Available from:

Page 51 of 66

http://www.sciencedirect.com/science/article/pii/S1042957303000160 [Accessed 12

August 2016].

Louzis, D. P., Vouldis, A. T. and Metaxas, V. L., 2012. Macroeconomic and bank-

specific determinants of non-performing loans in Greece: A comparative study of

mortgage, business and consumer loan portfolios. Journal of Banking & Finance, 36

(4), 1012–1027.

Makri, V., Tsagkanos, A. and Bellas, A., 2014. Determinants of non-performing

loans: The case of Eurozone. Panoeconomicus [online], 61 (2), 193–206. Available

from: http://scindeks-clanci.ceon.rs/data/pdf/1452-595X/2014/1452-

595X1402193M.pdf [Accessed 10 August 2016].

Mensah, F. A. and Adjei, A. B., 2015. Determinants of non-performing loans in

Ghana banking industry. International Journal of Computational Economics and

Econometrics, 5 (1), 35.

Messai, A. S. and Jouini, F., 2013. Micro and macro determinants of non-performing

loans. International Journal of Economics and Financial Issues, 3 (4), 852–860.

Männasoo, K. and Mayes, D. G., 2009. Explaining bank distress in Eastern European

transition. Journal of Banking & Finance [online], (33), 244–253. Available from:

https://economix.fr/pdf/profs/Mayes-bank-distress.pdf [Accessed 11 July 2016].

Nkusu, M., 2011. Nonperforming loans and macrofinancial vulnerabilities in

advanced economies. IMF Working Paper, 11 (161), 1–27.

Poposka, K., 2015. Article detail. Економски Развој, 17 (1-2), 101–116.

Quagliariello, M., 2007. Banks’ riskiness over the business cycle: A panel analysis

on Italian intermediaries. Applied Financial Economics, 17 (2), 119–138.

Rashid, R. N., Azid, T. and Malik, S., 2014. Microeconomic determinants of credit

risk management in Pakistan: a case study of banking sector. Pakistan Journal of

Social Sciences (PJSS), 34 (1), 177–192.

Ryan, T. P., 2009. Modern regression methods. 2nd edition. Wiley-Blackwell.

Page 52 of 66

Salas, V. and Saurina, J., 2002. Credit risk in two institutional regimes: Spanish

Commercial and Saving Banks. Journal of Financial Services Research, 22 (3), 203–

224.

Saunders, M., Lewis, P. and Thornhill, A., 2009. Research methods for business

students. 5th edition. Harlow, England: Pearson Education.

Sender, H., Guerrera, F., MacIntosh, J., Chung, J. and Scholtes, S., 2008. WaMu

seized and sold to JPMorgan. Financial Times [online], 26 September 2008.

Available from: https://next.ft.com/content/7647d4d4-8b33-11dd-b634-

0000779fd18c [Accessed 4 August 2016].

Silverman, D., 2015. Interpreting qualitative data: A guide to the principles of

qualitative research. 4th edition. Los Angeles: SAGE Publications.

Sinkey, J. F. and Greenawalt, M. B., 1991. Loan-loss experience and risk-taking

behavior at large commercial banks. Journal of Financial Services Research, 5 (1),

43–59.

Sundararajan, V., Enoch, C., José, A. S., Hilbers, P., Krueger, R., Moretti, M. and

Slack, G., 2002. Financial Soundness Indicators: Analytical Aspects and Country

Practices. International Monetary Fund, 212.

Vatansever, M. and Hepsen, A., 2013. Determining impacts on non-performing loan

ratio in Turkey. Journal of Finance and Investment Analysis, 2 (4), 119–129.

Vermeulen, P., 2002. Business fixed investment: Evidence of a financial accelerator

in Europe. ECB Working Paper [online], 37. Available from:

http://papers.ssrn.com/sol3/papers.cfm?abstract_id=355985 [Accessed 3 July 2016].

Vodova, P. K., 2003. Credit risk as a cause of banking crises [online]. The Fifth

International Conference. Aidea Giovani, Milan. Available from:

https://www.researchgate.net/publication/228420636_Credit_Risk_as_a_Cause_of_

Banking_Crises?enrichId=rgreq-e5c106f6825f71189d75aa18c0255304-

XXX&enrichSource=Y292ZXJQYWdlOzIyODQyMDYzNjtBUzo5NzY5MTYzNT

gxNDQxMUAxNDAwMzAyOTA5MzE1&el=1_x_2 [Accessed 1 July 2016].

Page 53 of 66

Waeibrorheem, W. and Suriani, S., 2015. Bank specific and macroeconomics

dynamic determinants of credit risk in Islamic banks and conventional banks.

International Journal of Economics and Financial Issues, 5 (2).

Williams, J., 2004. Determining management behaviour in European banking.

Journal of Banking & Finance [online], 28 (10), 2427–2460. Available from:

http://www.sciencedirect.com/science/article/pii/S0378426603002735 [Accessed 10

August 2016].

Williamson, S. D., 1987. Financial Intermediation, business failures, and real

business cycles. Journal of Political Economy, 95 (6), 1196–1216.

Zeman, J. and Jurča, P., 2008. National bank of Slovakia macro stress testing of the

Slovak banking sector. National bank of Slovakia working paper [online]. Available

from: https://www.nbs.sk/_img/Documents/PUBLIK/08_kol1a.pdf [Accessed 12

August 2016].

A-1

Appendices Appendix 1 Macroeconomic interlink with NPLs

-20

0

20

40

60

80

100

120

2,004 2,006 2,008 2,010 2,012 2,014 2,016

year

INFR GDP EXR NPL

B-1

Appendix 2 Normal Distribution of Independent Variables

0

100

200

300

400

500

600

700

-300 -250 -200 -150 -100 -50 0 50

Fre

quency

ROAE

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100

Fre

quency

LTAR

0

40

80

120

160

200

240

-4 0 4 8 12 16 20 24

Frequency

INFR

0

40

80

120

160

200

-8 -4 0 4 8 12 16 20

Frequency

GDP

0

100

200

300

400

500

600

700

-100 0 100 200 300 400 500 600 700 800 900

Fre

quency

G_LOAN

0

50

100

150

200

250

-15 -10 -5 0 5 10 15 20 25 30

Fre

quency

EXR

C-1

Appendix 3 Quantiles – Quantile Graph

-40

-20

0

20

40

60

-300 -200 -100 0 100

Quantiles of ROAE

Quantile

s o

f Norm

al

ROAE

-20

0

20

40

60

80

100

120

0 20 40 60 80 100

Quantiles of LTAR

Quantile

s o

f Norm

al

LTAR

-4

0

4

8

12

-5 0 5 10 15 20 25

Quantiles of INFR

Quantile

s o

f Norm

al

INFR

-10

-5

0

5

10

15

20

-10 -5 0 5 10 15 20

Quantiles of GDP

Quantile

s o

f Norm

al

GDP

-150

-100

-50

0

50

100

150

-200 0 200 400 600 800 1,000

Quantiles of G_LOAN

Quantiles

of Norm

al

G_LOAN

-20

-10

0

10

20

30

-20 -10 0 10 20 30

Quantiles of EXR

Quantiles

of Norm

al

EXR

D-1

Appendix 4 Signs of Tested Variables

Symbol Expected Empirical Robustness Test

(GMM) FE GMM Europe Asia

Dep

.

NPLs

Mac

roec

onom

ic

G_GDP (-) (-) (-) (+) (-)

REER (-) (+) (-) (+) (-)

INFR (+)/(-) (-) (+) (-) (-)

Ban

k –

leve

l ROAE (-) (-) (-) (+) (+)

G_LOAN (+) (-) (-) (-) (-)

LTAR (+) (-) (-) (-) (-)