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Determinants of non performing loans in vietnamese banking system

UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

ERASMUS UNVERSITY ROTTERDAM
INSTITUTE OF SOCIAL STUDIES
THE NETHERLANDS

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

DETERMINANTS OF NON-PERFORMING LOANS
IN VIETNAMESE BANKING SYSTEM

BY

NGUYEN THI HONG THUONG

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, DECEMBER 2017



UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

DETERMINANTS OF NON-PERFORMING LOANS
IN VIETNAMESE BANKING SYSTEM

A thesis submitted in partial fulfilment of the requirements for the degree of
MASTER OF ARTS IN DEVELOPMENT ECONOMICS

By

NGUYEN THI HONG THUONG

Academic Supervisor:
A/PROF. NGUYEN VAN NGAI

HO CHI MINH CITY, DECEMBER 2017


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

NGUYEN THI HONG THUONG



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ACKNOWLEDGEMENT
Here I would like to show my sincere expression of gratitude to thank my supervisor,
Ass. Professor Nguyen Van Ngai for his dedicated guideline, understanding and
supports during the making of this thesis. His precious academic knowledge and ideas
has motivated me for completing this thesis.

Besides, I would like to express my appreciation to the lecturers and staff of the
Vietnam – Netherlands Program at University of Economics Ho Chi Minh city for
their willingness and priceless time to assist and give me opportunity for this thesis
completion.

Next, I would like to thank all of my classmates for their encouragement and their
hard work, which become a good example for me to do the thesis. I wish all of us will
graduate at the same date.

Lastly, I would like to express my gratitude to my families, my beloved group for
their unlimited supports and encouragement. They are the motivation for me to finish
this course research project.

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ABBREVIATION
FE: Fixed-effect estimator
GDP: Gross domestic product
NPLs: Non-performing loans
OLS: Ordinary Least Square
RE: Random-effect estimator
SBV: State Bank of Vietnam
S.GMM: the system generalized method of the moments estimator

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ABSTRACT
Credit risk is one of the elements impact on the health of banking systems and
performance of economic activities. Non-performing loans is the general factor
presents for this bank’s credit risk. There are previous researches indicate the close
relations between bad debts and factors from macroeconomic environment and bank
specifications. This is the motivations for this paper to examine both macro and micro
variables of 30 Vietnamese banks from 2006 to 2016. This dynamic panel data is
estimated by the System Generalized Method of Moments. The regression results
support the strong evidence for the impact of macro indicators on problem loans. The
testing results are in accordance with several papers which indicated the negative
relation with economic growth and positive correlation with lending interest rate and
government debts of problem loans. However, due to the type of labor force, the
increase of unemployment rate will lead to the increase in bad loans in Vietnam. In
addition, with bank-specific factors, tests of skimping hypothesis, diversification (with
proxy is banks’ size) hypothesis and procyclical credit policy hypothesis have the
statistical significance in Vietnam.

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CONTENTS
DECLARATION ........................................................................................................... i
ACKNOWLEDGEMENT ........................................................................................... ii
ABBREVIATION ........................................................................................................iii
ABSTRACT .................................................................................................................iii
CONTENTS .................................................................................................................. v
APPENDIX.................................................................................................................... 1
LIST OF TABLES ........................................................................................................ 2
CHAPTER 1: INTRODUCTION ............................................................................... 3
1.1

Problem statements: ........................................................................................... 3

1.3

Research objectives: ........................................................................................... 4

1.4

Research questions: ............................................................................................ 4

1.5

Structure of Research: ....................................................................................... 4

CHAPTER 2: LITERATURE REVIEWS ................................................................. 6
2.1

Macro-economic factors: ................................................................................... 6

2.1.1 Theories: .............................................................................................................. 6
2.1.2 Empirical review:................................................................................................. 9
2.2

Bank-specific factors: ....................................... Error! Bookmark not defined.

2.2.1 Hypotheses: .......................................................... Error! Bookmark not defined.
2.2.2 Empirical review:............................................................................................... 14
CHAPTER 3: MODEL SPECIFICATION AND DATABASE ............................. 16
3.1

Model specification: ......................................................................................... 16

3.1.1 Econometric models: ......................................................................................... 16

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3.1.2 Variable explanation: ........................................................................................ 21
3.2

Data:................................................................................................................... 25

CHAPTER 4: RESULTS AND DISCUSSIONS...................................................... 26
4.1

Summary statistics: .......................................................................................... 26

4.2

Empirical results: ............................................................................................. 28

CHAPTER 5: CONCLUSIONS AND RECOMMENDATION ............................ 39
5.1

Conclusion:.…………………………………………………………………..39

5.2

Recommendations:………………………………………………………......40

5.3

Limitations: ………………………………………………………………….41

REFERENCES ........................................................................................................... 42
APPENDIX.................................................................................................................. 48

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APPENDIX
Appendix 1: Correlation of variables
Appendix 2: Addition estimation test with 2 lag of variables
Appendix 3: The estimated results for the regression models with separate hypotheses
using system generalized method of the moments
AP

Page | 1


LIST OF TABLES
Table 1: Summary statistics
Table 2: Results with Pooled OLS, FE, RE and SGMM estimations
Table 3: Estimation results of one lag variables
Table A1: Estimation without lagged variables
Table A2: Estimation with lagged variables

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CHAPTER 1: INTRODUCTION
1.1.

Problem statement

Both developed and emerging countries recognize the important of financial
institutions. Nkusu (2011) imply that the health of financial system and economy is the
two-way impact. It means that the performance of financiers could be improved by the
economic growth. On the contract, if bank crisis happens, the economy could be
downturned. Non-performing loans are considered as the general measure for riskiness of
the banks, as well as applied to predict the bank crises. Rajaraman and Visishtha (2002)
indicated the investigation the causality of bad debts is important to control this risk.
Adebola, Wan Yusoff and Dahalan (2011) identify that one of the causalities of economic
crisis in 2008, which affects not only on the U.S economy but also many countries
around the world, is the problem loans. Several loans in this period were issued for the
segments in under standard conditions. Therefore, when the economy goes down, most of
them turn out bad debts. The health of banking system become worse and worse after
that, leads to the negative impact on economy (Nkusu, 2011).
Credit risk is one of the factors to evaluate the health of banking system. This factors
is defined as the problem loans of banks. The non-performing loan ratio of Vietnamese
banking system has a significant increase from 2009 and got a peak at 2012, at 3.44%.
Due to the tighten monetary and lending policies of State Bank Vietnam as well as the
development policies of Government, this ratio has a little decrease after that.
The bad debts in banking system get the obstacles for economic growth, as well as
financial system development recent years. First of all, it is difficult for economic
segments to approach the credit capital. In the controlled period (from the end of 2010),
the increase of credit was limited (approximate 15% per year) and the lending interest
was high (in the range from 17% - 22% per year). In addition, in this stage, there are
several M&A as well as restructured banks lead to the more tightened policies to control
the stability of financial system. The stuck capital flows impacted negatively on
economic activities. The firms were in short of capital to manufacture and investment in
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order to expand production. The consumption was decrease, effect on firm’s revenue and
profit. As a circle, the total output reduce, the economy went down. Until the problem
loans can resolve, the economy has to allocate the scared resources in order to support
banking activities and maintain the stable of banking system. This is the big obstacle
which will pull down the overall economy (Nguyen Xuan Thanh, 2017).
1.2.

Research question

Understanding the root cause of the issue is the best way to solve the problem.
Therefore, this research will try to find the answer for question:
- Which determinant can have the significant impacts on the non-performing loans of
Vietnamese banks?
1.3.

Research objectives

The objectives of this paper are expected to have the answer for above question, as
below:
- To estimate the impacts of macroeconomic determinants to the NPLs ratio of the
Vietnamese banks.
- To examine the impacts of bank-specific determinants to the NPLs ratio of the
Vietnamese banks.
1.4.

Data and econometric model

A cross-sectional dataset is collected to support the objectives of this paper. This data
includes macro-economic factors, such as: economic growth, unemployment rate and
lending interest rate. The data for bank specification will be collected from annually
audited financial statements of 30 banks from 2006 to 2016 and calculated based on the
financial indexes. Generalized Method of Moments (GMM) is suitable for estimating
influence on banks’ problem loans of these variables with the different lagged orders.
1.5.

Structure of thesis

This study is organized in five chapters. The first chapter is the problem statement,
research question and objectives. The second chapter will be review the literature,
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includes theories and previous researches in order to identify the factors impact on NPLs.
Data collection and model specification for the study will be described in the third
section. Next chapter will present and interpret the results of the econometric analysis
with respect to the research’s theoretical and empirical analyses, which are linked to the
hypotheses of the research paper. The results will show the relationship of the economic
factors and the NPLs ratio of banks. Finally, the conclusions could be presented in last
chapter.

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CHAPTER 2: LITERATURE REVIEW
Credit risk is the risk in credit activity of bank when the borrowers can not
complete obligations of their liabilities. When this risk occurs, the banks are affected
negatively. The total assets, profit and capital will decrease due to the increase of loan
loss provision amount. The consequence is the negative effect on the economic activities
due to not only the increase of banks’ exposure to economic crisis but also the restriction
off the credit activities. Therefore, an analysis for credit risk is necessary to maintain the
stability of financial system and have the early warning of possible crisis. All of them are
worked for the final target: the growth of economy. The factors, which impact on credit
risk, are divided into two groups: systematic and unsystematic credit risk (Castro, 2013).
Macro-economic factors are considers as the factors influencing the systematic credit
risk. On the contrast, the bank specifications are grouped as unsystematic credit risk,
include financial indexes and the quality of credit management.
2.1.

Macro-economic factors

2.1.1. Theories:
The theoretical models of business cycle, which indicates the important role of
financiers, offer the good baseline for NPL models. Williamson (1987) highlights the
counter-cyclicality of business failures and credit risk. After that, the researches of
Bernanke, Gertler and Gilchrist in 1980s and 1990s mention about the financial
accelerator framework. The theory of financial accelerator states that the worsening
financial market conditions can amplify the negative shock to economy. More broadly,
the downturn period of finance and macro-economy is propagated by the disadvantage
conditions in the real economy and financial markets. Bernanke, Gertler and Gilchrist
(1996) and Kiyotaki and Moore (1997) use the framework about “principle-agent” view
of credit market in order to rationalize the financial accelerator theoretically. Their
method becomes the important theoretical framework for the macro-financial linkages
when modeling the interaction between NPL and macro-economy.

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GDP growth: When economic growth is stable or increases (i.e. expansion stage
of economy), the payment of borrowers is easy to complete, and the bank credit usually
meets the demand and increases over time. The doubtful loans are not the most serious
problem of bank’s managers. On the other hand, when the economy has to face with the
obstacles for growth, even downturns, the reduction of cash inflows is the trend of all
segments. At this time, the debt payment of firms as well as individuals becomes
difficult. It leads to the increase in non-performing loans in the banking system. Because
banks’ capitals are stuck in the recession, the capital for the economy, which is the most
important for all activities, is in the shortage. The consequence is the stagnation of all
business, and the economy is still deeper in the crisis. This is the causality of banks’ bad
debts and economic growth. There is a negative relation between NPL and GDP growth.
The interest rate: the higher interest rate is argued to be relevant with the debt
burden due to the higher of financial obligations. The asymmetric information theory can
explain for this argument. According to this theory, when the interest rate increases, the
debtors have to face an adverse selection and the loans can be their bad choice in this
scenario (Bohachova, 2008).
To have enough income to cover the debts, the borrowers have a tendency to
invest in riskier projects instead of safe projects with lower return. Furthermore, banks
will grow their income from credit activity due to new issued loans. In addition, with
outstanding loans, the banks can have more returns with the floating lending interest rate,
which adjusts the increase of debt’s liabilities. But banks have the role as financial
intermediations, which lend to a large number of borrowers as well as borrow from a
large number of depositors. In some countries, despite of the high cost for fund and high
risk behaviors as their culture, interest rate will be liberalized. It means that high-risk
creditors will be charged at higher rate in order to mitigate risks. The consequence is the
increase of overall risk exposure (Fofack, 2005).
At the recession stage of business cycle, the banks have to pay more interest for
depositors than the returns received from borrowers. This leads to the profit reduction,
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even the losses. Because total assets of banks include long-term fixed interest rate loans,
the return is not quick enough for banks to handle their liabilities. The temporary solution
is the rise of short-term lending interest rate to pay their liabilities (Mishkin, 1996).
Furthermore, the increase of debt payment for borrowers will lead to the risk for banks’
loan portfolios as their ability is not guaranteed. Therefore, this risk will be compensated
by the higher net interest margin (Ahmad and Ariff, 2007).
The unemployment rate: economic cycle stages have the closed correlation with
the unemployment rate. So this factor is defined a determinant impacts on the credit risk.
According this view, the unemployment rate directly affects the income of households. In
addition, this rate increase will lead the decrease of social consumption, which will
impact on the business production of corporates reflected in sale decline. As the results,
the repayment for obligations has the difficulty to complete, thus the credit risk is
exacerbated (Castro, 2013). The model of Lawrence (1995), implies that low-incomesegment could be charged higher rates than others due to the potential risk of
unemployment and payment inability, based on the life-cycle consumption. According to
Rinaldi and Sanchis-Arellano (2006) results, current income and the unemployment rate,
which are key elements of customer’s bankruptcy ability, are relevant with uncertainty
regarding future income and the lending rates.
Non-performing loans and banks’ losses can increase due to the diminished
employment and corporate returns in the recession stage of economic cycle (Berge and
Boye, 2007). Based on the expectation about the future flow of income and expenditure
of the debtors, the banks will decide the provision amount for their loans. If the
borrowers are unemployed, they have to suffer the higher costs for loan and other
services from banks. The capacity of these customers will be deteriorated due to the
unexpected movements. The result is the increase of credit risk.
The Government debts (the sovereign debt hypothesis): Public debts create the
pressure on economic development to ensure the payment ability for principal and
interest. Therefore, when the ratio of public debt exceeds the acceptable threshold, it will
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negatively affect the growth. This is the causality of vulnerabilities, which are the
baseline of financial crisis if having no punctual adjustment policies... When the
economy is downturn, the banks are careful to finance the loans. The capital from banks
is tightened due to lending reduction. It leads the reduction of production business as well
as the social consumption. The firms’ revenue and households’ income are decrease. So,
the repayment for bank loans is therefore also affected accordingly, leading to bad debt
ratio tends to increase.
2.1.2. Empirical review
Several previous studies do estimation the impact of macro-economic on nonperforming loans. Shu (2002) indicates the change in macroeconomic factor can
influence on the repayment ability of borrowers and banks’ loan portfolio when
examining the banks in Hong Kong. The finding of this study shows that in the expansion
stage of the economic cycle, the banks have more chances to push lending activity, thus
the risk can reduce.
Salas and Saurina (2002) examine the problem loans in commercial and saving
banks in Spain from 1985 to 1997 by using GMM dynamic panel estimations in order to
estimate which determinants of NPL in Spanish banks. The results are showed that
problem loans in neither commercial banks nor saving banks have a negative relation
with the growth of economy overtime.
After that, the research of Jimenez and Saurina (2006) also investigate the loan
loss of Spanish commercial banks from December 1984 to December 2002. By applying
the Generalized Method of Moments (GMM) estimator for dynamic panel models, they
support a significant evidence for the positive relationship between the interest and
problem loans. This conclusion is also supported by the research of Cural, et al. (2013)
for the Southeastern European banks. The explanation for this relation is the top-up
loans’ obligations for borrowers when the interest rate increases.
Burger and Boye (2007) support an evidence for the positive and significant effect
on non-performing loans of unemployment rate in household and corporate segments
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when investigating the Nordic banks from 1993 to 2005. In addition, the finding
emphasizes that the strong effect of income on the capacity of debt-servicing and the
volume of problem loans from household segment. Therefore, when unemployment rate
increases, the income, which is used to cover the borrower’s obligations, can reduce. This
leads the potential increase of bad debt form this segment. At the same time, the income
reduction will effect on individuals’ consumption, includes financial services. The
consequence is the lower domestic demand. The next result is the go down of firm’s
earnings and loan repayment ability. Therefore, banks’ bad debts will increase due to the
higher unemployment rate.
Then, Jakubik (2007) analyses macroeconomic factors effect on the credit risk of
Czech banks by applying the Merton’s methodology. The author concludes that the
decrease of real economic growth will lead the higher credit risk of banks as the negative
impact on the loan portfolio of the reduction from the return of companies, wage growth
and the increase of unemployment rate.
After that, the research of Espinoza and Prasad (2010) estimates the effects of
macroeconomic shocks on non-performing loans, by applying a VAR model for the data
of 80 banks in the Gulf Cooperative Council (GCC) in the period 1995-2008. The set of
macro-economic variables includes non-oil growth, interest rates. Their conclusion is the
increase of NPL is affected by the higher interest rate, as well as the lower real non-oil
GDP growth.
Nkusu (2011) uses the single-equatio panel regressions for the sample of 26
developed countries from 1998 to 2009. His data set is the macro and financial indices,
include economic growth, unemployment rate, inflation, interest rate and the price
variation of housing and stock. The author estimated with many methods, such as: OLS
model, panel-corrected standard error (PCSE) models, lagged dependent variables, fixed
effects and one-step GMM. The regression results indicate that the increase of NPL is
affected by the downturn of macro-economy, which is measured clearly by the lower rate
of economic growth as well as employment.
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Zribi and Boujelbène (2011) have the same conclusion about the inverse relation between
GDP growth and bank credit risk when analyzing the bank credit risk in Tunisia from
1995 to 2008.
Beside, Louzis, et al. (2012) approaches NPL of each loan categories in Greek
banks by using a set of macroeconomic factors, include the real rate of GDP growth, the
unemployment rate and the real interest rate. The result indicates that the injured debts
have relationship not only with this set of variables but also with the bank’s management
qualify. By using government debts factor in order to formulate the sovereign debt
hypothesis, which is based on the findings of Reinhart and Rogoff (2010) and Perotti
(1996), the authors support a strong evidence for this hypothesis.
Reinhart and Rogoff (2010) use OLS with robust errors and fractional logit to
estimate the relation of bank crisis and debts. Their findings indicate that bank crises are
affected by the external financial obligations. In addition, banking crisis usually goes
with the sovereign debts crisis.
In addition, Messai and Jouini (2013) apply data of 85 banks in three countries:
Italia, Greece, and Spain in the duration 2004-2008. The effects of macro determinants on
loan losses are estimated by variables: real growth rate, unemployment rate, real interest
rate. The regression results are consistent with previous studies. The conclusions indicate
that the NPLs have related negatively with real GDP growth and employment rate but
positively with real interest rate.
Recently, Chaibi and Ftiti (2015) conclude that both French and German banks
increase the problem loans when the unemployment rate rising. By using the growth of
GDP and unemployment rate, they find that the credit risk in French banks is more
sensitive to the economic environment than in Germany.

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2.2.

Bank-specific factors

2.2.1. Hypotheses:
Three prominent hypotheses are investigated by Berger and DeYoung (1997)
when the authors take into account the relationship between non-performing loans of the
bank and its cost- inefficiency
“Bad management” hypothesis: The cost efficiency of banks is expected as the
obviously significant factor impacts on non-performing loans of banks. This indicator is
considered as the index to appraise the quality of management. They are assumed that the
bad management could be caused by the poor skills in credit section, such as: scoring,
loan approval, loan monitor, etc. The banks have to spend more and more cost on
operating but the risk management could not be controlled efficiently. Therefore, the
NPL ratio of banks could increase due to the cost inefficiency.
“Skimping” hypothesis: according this hypothesis, there is the trade-off between
operation expense allocation and future problem loans. Skimping on operation costs,
which devote to underwriting and controlling loans, could have cost efficiency in shortrun when lower operation costs still support the quantity of loans. However, the bank
could be faced to the reduction of cost efficiency when non-performing loans become
higher due to its less effort to maintain the quality of loan in long run.
“Moral hazard” hypothesis: one of the solutions to increase bank’s profit is
increasing their loan portfolio. The bank with lower capital usually serves risky segments.
Their performance could be better in short-term but NPL will grow in the future.
Louzis et al. (2011) added three hypotheses for the impact of bank- specific factors on
non-performing loans. They supply more respects to investigate whether other bank
characteristics (different from bank’s cost efficiency) can impact on its bad debts.
“Bad management II” hypothesis: bad performance in the past could predict the
increase of future NPLs. According this view, bank’s performance in the past is another
proxy to the measure of management ability.

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“Diversification” hypothesis: this theory states that if the portfolio could be
diversified, the firm could reduce the risk and maintain the revenue. Banks are also a type
of corporate. Therefore, their profit could be table or increase if they could have the
diversification in operation.
This hypothesis could be examined by bank’s size or its multiple income sources.
It is said that with the large size, the banks have many opportunities to diversify their
portfolio. They could not be depended on credit sector as a majority operation. Therefore,
they could control the problem loans but maintain the stable profit.
Another proxy for diversification of banks is income sources. According this view, the
non-interest income of bank is higher, the diversification in operation of bank is better.
Credit section is not only the main income if bank. So, bank could control their loan
efficiently. This could lead the lower NPL.
“Too big to fail” hypothesis: In this view, the too big to fail banks could be
expected the protection from Government in case of its failure. Therefore, these banks
could have tendency to increase their leverage to gain higher profit. The majority capital,
which finances banks’ assets, is their liabilities such as: customer deposits, borrowed
money from Government and other financiers, bonds, etc. This leads the result that the
bank could lend more money to maintain the higher profit. The bank size is bigger, the
higher its pressure of debts payment. So, its standard for borrowers could be lower and
riskier in order to have more income. The consequence is the increase of problem loans in
the future.
“Procyclical credit policy” hypothesis: According this view, the policy could be
decided due to bank’s optimal profit expectation as well as other targets such as it
reputation. So, the banks’ profitability could be desirable in the market in short-term as
the effect of managers to manipulate earnings. However, this index could be considered
as a negative net present value of credit extension period. As below declaration, these
policies are built up for short-term target. After this blooming development, the credit
policies could be tightened. This leads the denial with positive net present value in this
13


stage. And the bank’s problem loans are more serious in downturn time. Overall, the test
of this hypothesis could reflect the liberal magnitude of bank’s credit policy, due to
comparing the performance with the increase of NPL in future growth.
2.2.2. Empirical review:
Berger and Young (1997) use the Granger-causality models to examine which of
four hypotheses, include bad luck, bad management, skimping and moral hazard, is in
accordance with the data of U.S commercial banks in the period 1985 -1994. The
estimations give a strong support for bad management hypothesis, when the result
indicated that higher of cost inefficiency can lead the rising of non-performing loans. In
addition, the possibility of skimping hypothesis is investigated in individual banks. The
moral hypothesis is also supported in their research.
Podpiera and Weeill (2008) examine whether bad luck or bad management
impacts on the bank failures in Czech banks from 1994 to 2005. By extending the
Granger-causality models, which were developed by Berger and DeYoung 91997), the
authors also apply GMM dynamic panel estimations in their research. Their regression
results support a strong evidence for bad management hypothesis. According to this view,
the cost-efficiency and bank’s problem loans are the negative relation. The result
concludes that the banks try to improve on cost-efficiency can lead to the decrease of
problem loans as well as precede bank failures.
After that, Karim and Hassan (2010) using the Tobit models also support the bad
management hypothesis when they research the problem loans in Singapore and
Malaysian banks.
Although support the hypothesis about “bad management” but the results of
Louzis, et al. (2012) cannot support “moral hazard” hypothesis in Greek banking system,
due to the small number of bank.
When investigating the factors impact on non-performing loans in Eurozone
banking system by different GMM estimation, Makri, Tsagkanos and Bellas (2013) show
the negative and significant relation between NPL ratios with banks’ performance, which
14


is measured by the index of return-on-assets and return-on-equity. Their findings
reconfirm that the deterioration of profitability index can increase the bad debts. The
results are consistent with the research of Louiz, et al. (2012), also support the strong
evidence for the bad management II hypothesis.
Salas and Saurina (2002), Hu, et al. (2004) and Rajan and Dhal (2003) have the
same empirical evidence to support the diversification hypothesis when using proxy is
bank size. Their results indicated that the bigger bank the more diversification
opportunities. However, the study of Louzis, et al. (2012) cannot find the empirical
evidence to support this hypothesis, either by proxy of bank size nor by the proxy of noninterest income ratio. They explain that the bank size could not be present the
diversification fully, or that is the counter-tendencies since the bigger banks have a higher
degree of risk-taking leads to higher NPLs. Furthermore, their result consist with Stiroh
(2004) in rejection hypothesis when apply proxy of income. This consequence could be
from the “potential dark sides of diversification”. It means that NPLs could increase if
bank could not have either the experienced managers or comparative advantages.
Mattana, Petroni and Rossi (2014) support “too-big-to-fail” hypothesis by
examining in European banks via ROA index. However, the results either of Louzis, et
al. (2012) or Boyd and Gerler (1994), Ennis and Malek (2005) cannot have an empirical
evidence to support this hypothesis.
Berger and Udell (2002) cite the speech of Alan Greenspan – old chairman of
Federal Reserve “the worst loans are made at the top of the business cycle”. However, the
findings of Louiz, et al. (2012) cannot support this hypothesis.

15


CHAPTER 3: MODEL SPECIFICATION AND DATABASE
3.1.

Model specification

3.1.1. Econometric models
3.1.1.1.

Dynamic panel data estimator

In the literature review, the non-performing loans are impacted by its ratio in the
last year. In the previous papers, the authors research the effect of the non-performing
loans one year ago on its situation at the present (i.e. how could the ratio of NPL in t-1
influence on NPL in t). Therefore, this study also uses the variable about NPL ratio which
has lag of first order to estimate the current problem debts ratio.
Based on the literature review, non-performing loans are affected by themselves in
the past, especially by the nearest values. Therefore, the dependent variable with one time
lag is added into the right-hand side of the model. So, dynamic panel data is built up.
The general formula of dynamic panel data approach is
𝑌𝑖𝑡 = 𝛼1 𝑌𝑖𝑡−1 + 𝛼2 𝑙𝑎𝑔𝑋𝑖𝑡 + 𝛾𝑖 +∪𝑖𝑡 ; |𝛼1 | < 1, i = 1, …, N; t= 1, …, T (1)
where the subscripts i and t denote the cross sectional and time dimension of the panel
sample respectively, 𝑌𝑖𝑡 is the change in the NPLs, 𝛼2 𝑙𝑎𝑔 is the lag of multiple vectors,
𝑋𝑖𝑡 is the matric of vector of independent variables other than 𝑌𝑖𝑡−1 , 𝛾𝑖 are the unobserved
effects of bank specific and ∪𝑖𝑡 is the error term. The use of Generalized Method of
Moments (GMM) created by Arellano and Bond (1991) and amended by Arellano and
Bover (1995) and Blundell and Bond (1998) is applied to estimate Eq (1). The first
difference transformation of Equation (1) is calculated by Equation (1) at year t minuses
Equation (1) at year t-1. This formula not only is consistent with the GMM estimation of
Arellano and Bond, but also eliminates the impact of bank-specific factor:
∆𝑌𝑖𝑡 = 𝛼1 ∆𝑌𝑖𝑡−1 + 𝛼2 𝑙𝑎𝑔𝑋𝑖𝑡 + ∆ ∪𝑖𝑡 (2)
where ∆ is the first difference calculation. ∆𝑌𝑖𝑡−1 presents for the lag of dependent
variable. Due to the correlation between the lagged explained variable and error term, the
estimation result could be discrepant. Nevertheless, 𝑌𝑖𝑡−2 has the correlation with ∆𝑌𝑖𝑡−1
but independence with ∆ ∪𝑖𝑡 for t = 3, …, T, is an instrument variable of Equation (2)
16


regression in order to prove that ∆ ∪𝑖𝑡 are not serially correlated. The dependent variable
could be lagged two or more but has to meet the moment conditions:
𝐸 [𝑌𝑖𝑡−𝑠 ∆ ∪𝑖𝑡 ] = 0, with t ≥ 3 and s ≥ 2 (3)
Nonetheless, the correlation of the independent variables and residual also causes
the bias results. To resolve the endogeneity, there is an assumption about independences
between error term and all values of explanatory variables, as the equation:
𝐸 [𝑋𝑖𝑡−𝑠 ∆ ∪𝑖𝑡 ] = 0, with t ≥ 3 and s is not limited (4)
The two-way causality is the limitation for the strictly exogenous presumption. For
example, if t has smaller value than s, the value of 𝐸 [𝑋𝑖𝑡−𝑠 ∆ ∪𝑖𝑡 ] is not equal 0. With a set
of fixed independent variables which is fragile exogeneity, the valid instruments are the
value of 𝑋𝑖𝑡 at present and lagged time, as below function:
𝐸 [𝑋𝑖𝑡−𝑠 ∆ ∪𝑖𝑡 ] = 0, with t ≥ 3 and s ≥ 2 (5)
Equation (3), (4) and (5) describe the statistically independent limitations. They
are foundation of the one-step GMM regression, following the assumption about the
independence and homoscedasticity of residuals (both cross sectional and over time),
consistence of parameter estimates. Arellano and Bond (1991) estimate the residuals by
the two-step GMM regression. The result will be a consistent variance–covariance matrix
of the moment conditions. This estimator can enforce the bias in standard errors (tstatistics) due to its dependence on the estimated residuals. Bond (2002); Bond and
Windmeijer (2002), Windmeijer (2005) indicated that is the reason of unreliable
asymptotic statistical conclusion. Arellano and Bond (1991); Blundell and Bond (1998)
re-confirm this inference by the relatively small cross section dimension data samples.
The specification test of Sagan, which distribution is asymptotical as chi-square, will
utilize to examine the variables’ overall validation, based on the assumption about valid
moment conditions. After that, testing the null hypothesis that the difference of error
terms does not having the second order autocorrelation will give the outcome for the
serially uncorrelated errors (∪𝑖𝑡 )fundamental assumption. If the result is rejection this
assumption, it means that the error terms exist the serial correlation. However, this
estimation is not consistent with GMM methods.
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