Collateral liquidity and loan default risks the case of vietnam

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

COLLATERAL LIQUIDITY AND LOAN
DEFAULT RISKS: THE CASE OF VIETNAM

BY

NGUYEN LE HIEU

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY, Dec 2016


UNIVERSITY OF ECONOMICS
HO CHI MINH CITY
VIETNAM

INSTITUTE OF SOCIAL STUDIES
THE HAGUE
THE NETHERLANDS

VIETNAM - NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

COLLATERAL LIQUIDITY AND LOAN
DEFAULT RISKS: THE CASE OF VIETNAM

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

By

NGUYEN LE HIEU

Academic Supervisor:
Dr. LE HO AN CHAU

HO CHI MINH CITY, Dec 2016


DECLARATION
By these statements, I declare that the thesis titled “Collateral liquidity and loan default risks: the case
of Vietnam” is result of my own works and efforts. All the contents in this thesis are my study based
on reviewing some previous papers which are clearly indicated in references. In addition, this thesis
has not been submitted to get any other degrees or certifications.
Signature

NGUYEN LE HIEU
December

2016

ACKNOWLEDGEMENTS
I desire to express my sincere gratitude to my supervisor Dr. Le Ho An Chau for her devotion to this
thesis completion. Her recommendations really help to improve the quality of this study very much.
My special thanks for all lecturers who taught me many profound and useful knowledge. Thanks all
VNP office employees who supported me so much during my master course.
Besides, I really appreciated unforgettable memories that I have experienced with all my classmates in
VNP class 21. This friendship will be maintained and developed deeply in future.
Lastly, I would like to show my thankfulness to my family who really supported me so much and
therefore I am enabled to finish this course.


ABSTRACT
This thesis investigates the impact of the liquidity level of collaterals on the probability of default of
individual loans and examines the channels through which collaterals affect default risks. Following
the approach of Jiménez and Saurina (2004), binominal logit model is applied on the data from
individual loan accounts of a medium – size commercial bank in Vietnam. The empirical results
suggest the significant and negative impact of collaterals’ liquidity on loans’ probability of default,
supporting the dominance of borrower selection effect and risk shifting effect over lender selection
effect. Moreover, the finding also implies that bank has not applied carefully and thoroughly screening
process on loans that are fully secured by low liquid collaterals and therefore impaired the credit
quality of loan portfolio.


CONTENTS
CHAPTER 1 ........................................................................................................................................................................ 1
1.1.

Research background and motivation ............................................................................................... 1

1.2.

Research objectives and research questions.................................................................................... 4

1.3.

Research Methodologies and Data ...................................................................................................... 5

1.4.

Research Contribution ............................................................................................................................. 5

1.5.

Structure of thesis ....................................................................................................................................... 6

CHAPTER 2 ........................................................................................................................................................................ 7
2.1

Theoretical review of relationship between collaterals and loan risks ................................ 7

2.2

Empirical review of relationship between collaterals and loan risk ...................................12

2.3

Theoretical framework ...........................................................................................................................16

CHAPTER 3 ......................................................................................................................................................................17
3.1

Research methodology ............................................................................................................................17

3.2

Data .................................................................................................................................................................19

CHAPTER 4 ......................................................................................................................................................................23
4.1

Descriptive Statistics and Pre-estimation tests .............................................................................23

4.2

Empirical results .......................................................................................................................................25

4.3

Robustness test ...........................................................................................................................................30

CHAPTER 5 ......................................................................................................................................................................35
5.1

Main findings & conclusion..................................................................................................................35

5.2

Policy implications....................................................................................................................................35

5.3

Research limitation and further research ......................................................................................37

REFERENCES ..................................................................................................................................................................39
APPENDIX ........................................................................................................................................................................42


LIST OF TABLES AND FIGURES
Table 1: Summary of variables ............................................................................................................................................................. 21
Table 2: Summary of loans characteristics........................................................................................................................................ 23
Table 3: Summary of default loans according to liquidity levels of collaterals .................................................................... 24
Table 4: Summary of default loans according to varied amounts of loans ............................................................................. 24
Table 5: Summary of loans default according to rate of protection .......................................................................................... 25
Table 6: Summary of loans default according to loan time ......................................................................................................... 25
Table 7: Estimation results of Logit model ....................................................................................................................................... 26
Table 8: Estimation results of Logit model (exclude interest and loan time factors) .......................................................... 27
Table 9: Estimation results of second logit model for robustness test ...................................................................................... 31
Table 10: Estimation results of the third logit model for robustness test ................................................................................ 33
Figure 1: NPL rate of Viet Nam for the period from Dec-2012 to Jun-2013 ............................................................................2
Figure 2: NPL of Viet Nam for the period from Jun-2014 to Dec-2015 ....................................................................................3
Figure 3: The transmission channels of collaterals on loan risk ....................................................................................................7
Figure 4: Screening cost prorated............................................................................................................................................................9
Figure 5: House price index of HCM city from 2009 to Q3-2016 ............................................................................................ 36


CHAPTER 1
INTRODUCTION
1.1.

Research background and motivation

Non-performing loans (NPL) are a severe problem for the whole economy of the world since
they lead to the financial crisis in East Asian countries, America and Sub-Saharan Africa
(Farhan, Satta, Chaudrhy & Khalil, 2012). Therefore, finding out the main determinants of
NPL plays an important role in policy making in order to prevent the future bad debts
(Adebola, Wan Yusoff, & Dahala (2011) in Farhan et al (2012)). Previous studies identify that
macro-economic conditions, bank and borrowers specific characteristics, loans characteristics,
relationship banking1, and collaterals are key drivers of default risks and hence NPL.
The relation between collateral characteristics and loan default is investigated in many studies
over the world. However, the findings are inconsistent among different papers, some of which
show positive relationship while the others provide evidence of a negative effect.
Berger, Frame and Ioannidou (2011) find a positive relationship between collaterals pledge
and ex-post NPL in Bolivia for the period from 1998 to 2003. This result is supported by
Jiménez and Saurina (2004) for Spain. Berger and Udell (1990) in Leitner (2006) shows that
borrowers who pledge collaterals tend to be worse and therefore are riskier. Leitner (2006)
explains that this finding is due to collaterals’ requirement of banks for riskier borrowers. In
contrast, John, Lynch and Puri (2003) investigate the yield difference between secured and
unsecured loans in US and conclude that higher yield is decided by secured loans. This result
implies that borrowers who pledge collaterals are more efficient than others. Kugler and
Oppes (2005) investigate the impact of collaterals2 on loans risk in case of group lending in a
developing country and find that collaterals are used by individuals to prevent loans default
under joint borrowing.
1

Banks who supply more services in long time for customers will have more private information of their customers
according to Argawal et al (2009).
2
Collateral in this paper is defined as equity capital of individual dedicated to investment projects.

1


Berger et al. (2011) argue that the diversified findings about this relationship arise from the
variation of data samples which include different types and characteristics of collaterals.
Moreover, previous papers investigate only the impact of collaterals on NPL by comparing
the default risk (probability of default) between secured loans and unsecured loans. To my
knowledge, there is very limited work on the impact of different collateral types and
characteristics on default risk. Berger et al. (2011) find that liquid collaterals decrease the
probability of default when compared to non-liquid collaterals. However, previous papers
mainly focus on loans for companies/enterprises rather than individual and consumer loans.
In Vietnam, bad debt has increased sharply since 2011 and still been serious until now. As we
can see in Figure 1 and 2, NPL ratio has risen from 4.08% in Dec-2012 up to 4.67% in April2013, then decreased lightly to 4.46% in Jun-2013 and kept declining to 2.58% in Jun-2016.
However, this does not represent an improvement in loan quality of banks but due to banks’
switching to other asset titles in the balance sheet to hide bad debts. The Viet Nam Assets
Management Company (VAMC) was established on July-2013 with its main objective is bad
debts purchasing by issuing special bonds for payment. As of Jun-2016, VAMC purchased
about 251.000 billions of bad debts from banks and only 15% of these bad debts were
collected (VAMC, 2016). Hence, these purchased bad debts help to reduce NPL of banks but
they were not collected in reality and still harm the whole economy. Furthermore, many bad
debts have been restructured but still classified as normal debts instead of bad debt in almost
Vietnamese banks (this problem is permitted by the State Bank of Viet Nam) and therefore
these bad debts were hidden.
Figure 1: NPL rate of Viet Nam for the period from Dec-2012 to Jun-2013

2


Source: State Bank of Viet Nam in />Figure 2: NPL of Viet Nam for the period from Jun-2014 to Dec-2015

Source: State Bank of Viet Nam
Ogeisia et al. (2014) argue that lending in low income countries is notoriously risky because
of information asymmetry problem which are high in developing countries. United Nations
Conference on Trade and Development - UNCTAD (2005) explains that high level of
information asymmetry arises from weak credit information infrastructure, ineffective public
3


records, lack of credit management skills and underdeveloped financial intermediation, which
is worse by generally restrictive and complicated regulatory environment and a large informal
cash-based economy. Majority of loans in Viet Nam are collateralized loans due to
information asymmetry. Although Vietnamese banks determine that loan approval is always
based on the payment ability of borrowers, not collaterals, but in practice, collaterals is the
most important condition that make a loan to be approved. Loans in Viet Nam are 100%
secured loans, therefore the difference of NPL between banks depends on the various quality
of banks’ screening procedures. Higher efficient banks focus on the screening quality and ask
for collaterals only for increasing the borrowers’ incentive for loan repayment to avoid asset
loss. In contrast, smaller banks who have higher cost, may loosen the borrowers screening
quality and use collateral as the premier protection from loan loss. Collateral plays the most
important role in NPL control in small banks in Viet Nam. The information conflict between
lender and borrower might be mitigated by collateral according to Berger et al. (2011) and
therefore mitigate loan approval for the optimists. However, Manove and Padilla (1999) argue
that collaterals can not help to distinguish the optimists and realist and therefore make the PD
prediction base on collateral requirement is unclearly. The reason is that the optimists always
tend to accept the collaterals’ requirement conditions like the realists to get lower cost loans
due to their confidence in the efficiency of their projects. Furthermore, different
characteristics of collaterals may have different impact on PD of loans due to results found by
by Berger et al (2011) as mentioned above. However, liquid collaterals in their work are only
Deposit and Bank guarantee and non liquid ones are the other type of assets while in Viet

Nam, the most popular collaterals are real estates and vehicles which are diversified in types
and therefore in liquidities. Each type of them is ranked in one level of liquidity and
desirability and this level is determined by banks. According to these above problems, an
investigation about the impact on PD of different collaterals which diversify in characteristics
for the case of Viet Nam should be implemented.
1.2.

Research objectives and research questions

The objective of this paper is to examine the impact of collaterals’ liquidity characteristics on
loans’ probability of default (PD) at the commercial banks in Vietnam.

4


In order to achieve that research objective, this thesis aims to seek convincing answers for the
following research questions:


Do higher liquidity levels of collaterals decrease the PD of loans?



If so, through which channels this effect is transmitted?

1.3.

Research Methodologies and Data

This paper applies the logit model to examine the responses of different liquidity levels of

collaterals, loans amount and ranks of protection rates of loans on PD of personals loans. All
predictors are categorical variables and the response takes only one of two categories at the
same time: default and non - default.
Data of this research is collected from internal loans account data source of a medium size
Vietnamese bank. Loans accounts are first generated in 3 years as 2010, 2011, 2012 and from
business units placed in Ho Chi Minh city and Hanoi. In order to investigate the direct impact
of collaterals on loans default, other factors that potentially affect loan’s PD are minimized by
collecting loan accounts that are secured by only one collateral at one point of time during the
period from 2010 to 2012. Hence, 2,295 observations are included in the research’s empirical
analysis.
1.4.

Research Contribution

This study provides empirical evidence about the impact of collaterals’ liquidity
characteristics on the PD of personal loans in a Vietnamese bank. Different from previous
researches, this thesis tries to find out how PD of loans response to various liquidity levels of
collaterals in cases of fully protected loans while previous studies only focus on difference in
PD between secured and unsecured loans. Data of this thesis are collected from internal data
of one bank in Viet Nam and differ from other researches of which data almost were provided
by National Credit information centers of other countries or collected from questionnaire.
Furthermore, to my knowledge, there have been limited studies about the relationship
between collateral characteristics and loan default risk has not been widely studied in Viet
Nam. One of the most important reasons is weak credit information infrastructure and
5


ineffective public records as mentioned above and therefore makes the data collection costly.
However, banks may have their own researches about this topic but do not publish due to
information privacy.

Negative responses of high liquidity level on PD are found in this paper, strongly supporting
the dominance of borrower selection and risk shifting effect in this bank. The higher liquidity
levels of collaterals, the lower probability of default on individual loans. From this result,
improving the screening quality in case of lower liquid collaterals is suggested for
investigated bank. Moreover, status of hidden subprime loans existence is warned to policy
makers. Therefore, some recommendations are suggested for the State bank of Viet Nam in
order to prevent severe loan loss if assets prices have devaluated.
1.5.

Structure of thesis

The rest of this paper is organized as follows:
Chapter 1 introduces about the background and motivation of this research. Inconsistent
empirical evidence about the impact of collaterals on PD is shown and the cause of this
inconsistency is discussed shortly. Bad debt situation in Viet Nam from 2011 until now is also
presented to show the need and motivation of a research about determinants of bad debts.
Chapter 2 will review the theoretical and empirical literatures about the relationship between
collaterals and non-performing loans (NPL). Brief explanation of the interactions of 4
channels through which collaterals affect loan default is discussed in this part.
Research methodology and data will be presented clearly in chapter 3. Brief explanation about
the meaning and suitability of logit model for this research and data collection is discussed.
This chapter also shows the limitation of data source.
Chapter 4 discusses empirical results and chapter 5 summarizes the research’s main findings
from which policy implication is suggested.

6


CHAPTER 2
THEORICAL FRAMEWORK AND

LITERATURE REVIEW
2.1

Theoretical review of relationship between collaterals and loan risks

There are two strands of theories that explain different effects of collateral requirement on
loan risk, ex ant and ex post theory. The ex-ant theory interprets the borrower selection effect
and the ex-post one explains the lender selection effect, risk shifting effect and loss mitigation
effect (Berger et al. 2011).
Figure 3: The transmission channels of collaterals on loan risk
Borrower selection effect (-)

Loan risk
(probability of
default)

Collateral
pledge

Lender selection effect (+)

Risk shifting effect (-)

Loss mitigation effect (+)

The ex ant theory explains the negative relationship between collateral requirement and NPL
due to the borrower selection effect. In this channel, higher quality borrowers tend to pledge
more liquid collateral to take lower interest rates on loans thanking to lower screening cost. In
7

this case, decision of banks in approving loans is based on signaling which means that banks
observe behavior of borrowers between secured and unsecured loans in order to classify the
quality of borrowers (Japhet and Memba, 2015). And according to Berger et al (2009) in
Japhet and Memba (2015), this ex ant theory is only applicable in cases of short relationship
between borrowers and lenders which implies a high level of asymmetry information between
two parties.
The choice of pledging collateral of borrowers is based on the expectation of avoiding
screening cost of banks and therefore lower interest rate. The screening procedure of banks is
costly and obviously this cost will be accounted in the interest rate. Manove et al. (1998)
argues that in equilibrium, banks screen all projects, but fund the good ones only and charge
an interest rate which is equal to the cost of fund plus screening cost of that approved project
and prorated share of the screening cost of all unapproved projects. Banks tend to loose the
screening process if being highly protected by collaterals. Therefore, high-type applicants
(applicants with good projects) tend to pledge collateral to remove banks from screening in
order to get lower interest rate. Hence, the borrower selection effect predicts a negative
relation between collateral pledging and loan risk, which means that the more quantity of
collaterals and the higher liquid collateral is associated with the lower probability of default.
However, the strength of this effect may declines due to the optimism of some borrowers.
Wishful thinking makes borrower’s perception biased according to Manove and Padilla
(1999). Also De Bondt and Thaler (1995) in Manove and Padilla (1999, pp. 325) reports that
“perhaps the most robust finding in the psychology of judgment is that people are
overconfident”. This report based on the summary from studies that made by behavioral
economists, psychologists and sociologists. In the context of optimistic borrowers, borrowers
always exaggerate the efficiency of their projects and underestimate the likelihood of loans
default. Consequently, if collateral requirement eases the approval of projects and result in
lower interest rate, optimists tend to pledge more collateral. In this case, collateral
requirement reduces the efficiency of economic and social welfare because of resources
distribution into low quality projects (Manove and Padilla, 1999). Hence, a secured loan may
imply a lower probability of default due to high type borrower and efficient project, except

optimistic borrower.

8


Figure 4: Screening cost prorated

Bank do not screen if fully
protected  do not pay this S

Good borrowers
prevent this cost
by pledging
collaterals

S of Approved
loans

Total
screening cost
(S)

Interest of
approved
loans
S of denied
loans

In another point of view, secured loans may reflect low quality of borrowers and projects.
This perception is based on the lender selection effect. Banks have advantage in project

assessment and ability in distinguishing bad and/or optimistic borrowers and projects. In order
to protect themselves from loan risk, banks require low quality borrowers/projects to pledge
more collaterals. Nevertheless, the act of screening of banks is unobservable. Banks may
decide to screen the efficiency of the projects or not, depending on the profit acquired from
screening. According to Manove et al (1998), in case of the benefit to the bank from screening
(information of borrowers and projects obtained) is less than the screening cost banks will not
screen the projects. The choice of screening is explained in Manove et al (1998) as follows:
Expected loss equation is given as: L = (1- PH)*(R – K)
where: S is screening cost, PH: Probability of success in case of good borrower and project,
R: Original loan amount, K: Loan amount which is secured by collateral.

9


Bank will lose loans amount which is not protected (R-K) if projects fail with a probability of
(1- PH). Therefore, banks will screen if S < L which means bank will pay screening cost S to
avoid loss L which is larger than S in case of project failure. Hence, if screening cost is small
enough, bank will get profit from screening projects.
If K is smaller than R, this implies larger loss in case of projects failure, the more incentive
that banks have to screen the projects to prevent higher expected loss.
If K ≥ R, there is no expected loss, banks will earn profit with certainty and therefore never
screen. In this case, banks screen the project just because of its credit policy that desires to
fund good projects and borrowers (Manove et al. 1998).
One more reason that gives more incentive for banks to approve secured loans but do not
screen is loss mitigation effect. As mentioned above, if loan is fully protected up to 100% by
collaterals, banks can earn profit in certainty and have more incentive to approve loan without
screening. Manove and Padilla (1999) argue that banks are willing to fund inefficient projects
of enterprises and require more collaterals for these loans in order to protect banks from risks.
Moreover, collaterals may decrease the effort of banks in monitoring loans even though they
can help to prevent PD (Cadot, 2011 in Japhet and Memba, 2015) and therefore increase PD.

Hence, in case of fully protected loans, quality of borrowers and projects may be recognized
or not and depends on the choice of screening of banks. If banks screen due to the desire to
fund high quality projects, secured loans may imply low quality borrowers and projects. In
contrast, if banks do not screen, a secured loan is not able to show the quality of borrowers.
The prediction of quality of borrower in this case is based on the borrower selection effect.
PD will be lower if good borrowers pledge collaterals to get low interest rate, but PD will be
higher if borrowers are optimists as mentioned above. In cases that loans are not fully secured
by collaterals including unsecured loans (bank screen in these cases), due to lender selection
effect, collateral requirement of banks implies low quality of borrowers and projects and
therefore implies higher PD.
Based on 3 channels through which collaterals affect loan risk, quality of borrowers and
projects as well as PD are doubtful and unclearly predicted due to unobservable perception of
10


borrowers (optimist) and screening choice of banks. However, there is still one more channel
which supports the positive relationship between collateral and loan risk. That is risk shifting
effect which explains that in case of secured loans, borrowers tend to be careful in choosing
projects to invest and shift their investment into high quality projects to prevent asset loss in
cases of default (Berger et al, 2011) and therefore decreases the PD. Furthermore, in the extent
of high level of moral hazard and asymmetry information, collaterals help banks to reduce the
impact of bad actions of borrowers after loans are granted.
Besides, strength of each channel not only depends on loans which are protected by collaterals
or not, but also on economic characteristics (liquid, desirable) and types of collaterals based
on Berger et al (2011). Berger et al (2011) find that lender selection effect is critical for
outside collaterals, risk shifting and loss mitigation effect are essential for liquid collaterals.
This finding makes sense. The higher the liquidity and desirability of collateral is, the more
effort that borrowers pay to prevent asset loss. Besides, banks are easier in liquidating liquid
assets to collect debt in case of loan default with lower cost than lower liquid assets.
Consequently, prediction of PD that is based on secured status of loans belongs to the channel

which has stronger effect. In case PD of secured loans is higher than that of unsecured loans,
secured loans may not belong to better quality borrowers and projects than unsecured loans
because of more carefully screening process of banks in cases of unsecured loans to prevent
loss when loans default and therefore only good borrowers are approved which implies lower
PD for unsecured loans. Worse borrowers are only approved if they pledge collateral to secure
for loans. Lender selection effect is strong in this case. In contrast, borrower selection effect
as well as risk shifting effect is weak in this case. This is due to optimistic borrowers and
loosing screening process of banks if loans are protected by collaterals and therefore banks
approve low type borrowers and projects.
For the case PD of secured loans is lower than unsecured loans, borrower selection effect and
risk shifting effect is stronger than lender selection effect. Borrowers who accept to pledge
their assets to secure for loans to get lower cost are high type applicants and the effect of
optimist is small here. In case of unsecured loans, banks appraise quality of borrowers’ and
projects’ inefficiency and therefore approve many bad applicants.

11


In the comparison between fully protected loans, if PD of loans which are secured by higher
liquid assets is lower than ones that are protected by lower liquid assets, Borrower selection
effect and risk shifting effect is stronger than lender selection effect in case of high liquid
collaterals. For loans that are secured by high liquid collaterals, borrowers screen their
projects carefully and put a lot of effort to make project to be profitable in order to prevent
asset loss. Hence, even though banks tend to loose their screening process because of high
protection level of loans, the PD is still lower due to Risk shifting effect and Borrower
selection effect. In this case, if banks choose to screen loans applicants carefully so that only
good borrowers and projects are approved, banks still fail in evaluating exactly the quality of
projects due to moral hazard (NPL still exsits when banks screen projects) and therefore high
liquid collaterals help banks to prevent ex post non performance loans more efficiency than
low ones.

2.2

Empirical review of relationship between collaterals and loan risk

According to Elsas and Krahnen (2000), there are different impacts of collateral on default
risk. This impact is found to be positive in some papers but negatives in others and even does
not exist (Berger et al. 2011).
Berger et al (2011) examines the difference in PD between secured and unsecured loans,
using a sample of 25,391 firm loans in Bolivia and find that collaterals increase PD and
reduce loan risk premium. Inderst and Mueller (2007) in Japhet and Memba (2015) supports
this finding by predicting that riskier borrowers tend to pledge collaterals and therefore have
higher ex post loan risk. Also, Jiménez and Saurina (2004) uses binomial logit model to test
this relation for 3,000,000 company loans in Spain and conclude that secured loans increase
the PD when compared with unsecured lending. Another finding from this paper is the
significant relation between the proportion of loans amount that is secured by collateral and
PD, particularly 100% secured loans have lower PD than those that are secured from over
50% but less than 100% loans amount. In contrast, Niinimaki (2010) find that “costly
collateral3 turns out to have positive incentive effect whether its value is stochastic or nonstochastic” and they explain that borrowers exercise all their effort in order to prevent asset
3

Costly collateral is cost to borrower if investment project fail.

12


loss. This paper also concludes that in case of large variation of collateral value, stochastic
collaterals have smaller incentive effects than non-stochastic ones. This is explained as if
expected value of collateral is significantly higher than present value, borrowers tend to pay
more effort to repay loans because they will loss more in this case. In contrast, if expected
value of collateral is stochastic or unpredictable, borrower pay less effort than previous case.

Variation level of collateral value is considered as part of liquidity level of collaterals.
No relationship between collateral and loan risk is found in Elsas and Krahnen (2000). This
paper concludes there is no correlation between borrower quality and incidence of collaterals
in Germany using the data collected from top five German banks and concludes that
collaterals are required primarily by lenders that have previous relation with borrowers in
order to lock borrowers in this relation and therefore increase the power of bargaining of
banks in the future renegotiation.
The inconsistent results found in these papers may be due to the dissimilar economic
conditions of different countries and samples choices. Berger et al (2011) argues that different
samples that have dissimilar types of collaterals may lead to different results about this
relationship. Furthermore, these researches only investigate the difference in probability of
default between secured and unsecured loans while limited studies examine different levels of
collateral liquidity. Berger et al (2011) find that in cases of secured loans, the liquidity level
and desirability of assets which are used as collaterals for loans are found to have impact on
PD. The authors define liquidity of asset based on “the ease, cost, and time with which the
secured assets can be converted into cash at fair market value in the event of default” and
therefore a liquid asset is quickly transferred into cash without a significant discount on its
value. This research finds negatively and statistically significant relationship between liquid
collateral (Deposits, Bank guarantees, Securities) and loan risk. This result implies the
dominance of risk shifting effect, borrower selection effect over lender selection effect. Japhet
and Memba (2015) collect information from banks’ loans processing and compliance
departments and from entrepreneurs of SMEs of 14 commercial banks in Kisii County Kenya through questionnaire to find out which type of collaterals are preferred by banks in
order to reduce loan risk. Using descriptive statistics, the paper find that motor vehicles are
preferred over lands and buildings by most of banks as collaterals for loans to reduce PD. The
13


explanation of the cause of this finding is the complicated legal procedure in pledging and
liquidating lands and buildings. Moreover, the process of liquidating lands and buildings costs
more than that of motor vehicles. One more finding that support the Risk shifting effect and

loss mitigation effect in this research is associated with a very low default rate of loans which
are secured by motor vehicles. Hence, these findings lead to a conclusion that the more liquid
and desirable collaterals are, the lower the probability of default is. However, these findings
may be biased due to the desirable of interviewees who are employees of banks for more
simple works when handling with loans secured by motor cycles compare to loans secured by
lands and buildings.
Furthermore, as aforementioned, collaterals in developing countries play a more critical role
than developed countries in credit market. Typically in Viet Nam, almost loans are secured
loans due to the critical moral hazard and asymmetry information. Ogeisia et al. (2014) argue
that lending in low income countries is notoriously risky because of information asymmetries
problem which are high in developing countries. United Nations Conference on Trade and
Development - UNCTAD (2005, page 119) state that the explanations for high level of
information asymmetry are weak credit information infrastructure, ineffective public records,
lack of credit management skills and underdeveloped financial intermediation, and this
asymmetry problem was made worse by generally restrictive and complicated regulatory
environment and a large informal cash-based economy. Hence, the role of collateral in credit
market in developing countries is very important in controlling default risk. Another
explanation for the higher influence level of collaterals in developing countries may be the
essential meaning of assets to inhabitants. The reason of this is the big gap between collaterals
value (such as houses, cars) and average income per capita in low-income countries.
In conclusion, impact of collaterals characteristics on PD is inconsistent between previous
studies due to different samples and researched countries. Almost researches study difference
in PD which is affected by collaterals requirement between secured and unsecured loans. Few
papers investigate this difference between loans that are fully secured by collaterals which
differ in liquidity levels. Comparison of PD which is affected by collaterals between secured
and unsecured loans is significantly different compared with that of fully protected loans. In
Viet Nam, fully protected bad debts are still increasing sharply in the last period even though
14

collaterals requirement is the most important condition in loans approval. The question about
the role of collaterals in controlling for these bad debts which are fully secured has not yet
been widely investigated. Almost loans in Viet Nam are fully secured by many types of
collaterals which are different each other in liquidity levels. Moreover, as mentioned above,
home, residential lands, cars are main assets to be requested by banks for collaterals due to
their easy transfer into cash with low cost. This argument is supported by Japhet and Memba
(2015) and support for different decision of bank in loans approval in cases of different
liquidity levels of collaterals. Banks will request higher liquid collaterals in cases of low
quality borrowers due to lender selection effect and loss mitigation effect and therefore these
loans have higher PD. However, valuable assets are essential meaning to inhabitants in low
income country as Viet Nam due to big gap between collaterals value and average income per
capita. Hence, in low income countries, people tend to spend many efforts to prevent their
assets loss, especially in case of highly desirable collaterals. Besides, people who afford to
own an asset usually be considered as having income generation ability and therefore have
capacity in loans repayment. Borrower selection effect and risk shifting effect are dominant in
this case and imply lower PD of loans protected by higher liquidity level collaterals. From
these arguments, PD in cases of higher liquidity collaterals is doubtful and base on the
dominance of which effect channel. The dominance of which effect channel in turn bases on
the screening choice as well as screening quality of bank. As mention above, banks do not
have incentive to screen in case of fully protected and therefore PD of loans are depend
mainly on borrower selection and risk shifting effect which imply lower PD for higher
liquidity collaterals. From these above arguments and from the theoretical and empirical
review about the relationship between collaterals characteristics and PD of loans, the
following hypotheses are developed:
H1: Secured loans with higher liquidity level of collaterals have lower PD than those with
lower liquidity level of collaterals.
H2: Borrower selection effect and risk shifting effect are transmission mechanisms through
which collaterals affect loan risk default.

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2.3

Theoretical framework

Loans characteristics:
Interest, loan time, loan
amount
Borrower selection
effect

Liquidity levels of
collaterals

Lender selection
effect

Probability of default

Risk shifting effect
Loss mitigation
effect

Control variables: time,
professions, region

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CHAPTER 3
RESEARCH METHODOLOGY AND
DATA
3.1

Research methodology

This paper follows the research methodology of Jiménez and Saurina (2004), applying
binomial logit model to examine the impact of the collaterals’ liquidity level on the
probability of default of individual loans.
Dependent variable is dummy variable yit which take value of 1 if loan i generated in year t is
default and 0 if not. A loan is default if it is downgraded to the rank 3-5 or is overdue more
than 90 days during loan time according to 493/2005/QĐ-NHNN, and 18/2007/QĐ-NHNN
regulations that were promulgated by the State Bank of Viet Nam. These documents regulated
the debt classification and provision that applied for financial institutions in Viet Nam. This
NPL criteria is used in Jiménez and Saurina (2004) and consistent with the regulations of the
State bank of Viet Nam.
Predictors of the model include variables that represent liquidity ranks of collaterals, interest
rates, loans times, protected levels of loans, loans sizes and ownership status of collaterals.
Jiménez and Saurina (2004) find negative relationship between loans duration, size of loans
and probability of default which means the longer the loan duration is, the lower the PD of
that loan is and the same as in case of size of loans. The model is controlled for professions of
borrowers, timely factors and regions that loans are generated in order to cover the difference
in lending policies between time and other temporal differences. Lending standards differ
from time. As mentioned above, Berger et al (2011) find that lender selection effect is critical
for outside collaterals which are owned by the third parties such as house of owners of the
company. If owners of company lose their own private assets in case of loan default, they tend
to consider investment projects more carefully. This may suggests a difference in PD between

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collaterals which are owned by borrowers and those are owned by the third parties who
almost are members of borrower’s family.
Variable that presents the liquidity ranks of collaterals is ordinal variable. This variable takes
value from 1 to 6 that represent for 6 liquidity levels of collaterals from low to high. Liquidity
levels of collaterals are collected from internal loans accounts database of investigated bank.
Loan sizes are divided into 3 levels from low to high: equal or less than 500 millions dong,
more than 500 millions dong to 1 billion dong, more than 1 billion dong. 2 dummy variables
are added to the model to control for 3 levels of loans sizes. Interest rates are changed every 3
months (this term is defined in loans contracts), therefore average interest rate of loans are
calculated in order to have the relative consistent number in comparing with the fact. Loans
times are measured in months.
Because linear model is not adequate for dummy dependent variable which presents default
status of loan due to constant variance assumption, logit model is applied to predict the
probability of default.
Specific model
An individual borrower will default if the utility that borrower expects to obtain when default
is greater than that he/she would obtain if not default. Call y*it is the difference in the utility
mentioned above and y*it is non-observable. As argued above, a borrower will default if y*it
>0 which means borrower get higher utility in case of default.
y*it takes the form: y*it = α + x’it β + z’t γ + w’i Ω + hcm + ε it
x’it: Liquidity levels of collaterals. This is the main explanation variable.
z’t: set of other explanatory variables such as: interest rates, loans times, protected levels of
loans, loans sizes, ownership.
dummy variable for the year in that each loan is created.
w’i: control variables for time, profession and regions factors.
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