TÓM tắt TIẾNG ANH tác động của rủi ro thanh khoản đến hiệu quả hoạt động

MINISTRY OF EDUCATION

THE STATE BANK OF VIETNAM

BANKING UNIVERSITY OF HO CHI MINH CITY

TRAN THI THANH NGA

THE IMPACT OF LIQUIDITY RISK ON BANK
PERFORMANCE EFFICIENCY: EMPIRICAL
EVIDENCE FROM SOUTH EAST ASIA COUNTRIES

DOCTOR OF PHILOSOPHY IN ECONOMICS
THESIS SUMMARY

Major: Finance – Banking
Code: 62 34 02 01

Academic advisor: Assoc. Prof. Dr. Tram Thi Xuan Huong
Dr. Le Thi Anh Dao

HO CHI MINH CITY - 2018


CHAPTER 1: INTRODUCTION
1.1. Research Problem
The relationship between liquidity risk and performance efficiency has made
interested through the approach to hypotheses such as Market Power Hypothesis,
Efficient Structure Hypothesis. (Diamond and Dybvig, 1983) showed that the impact
of liquidity risk on bank performance efficiency is unclear.
Some studies in Africa (Sayedi, 2014; Aburime, 2009; Athanasoglou et al.,2008;

Ajibike & Aremu, 2015; Alshatti, 2015); in Asia (Wasiuzzaman & Tarmizi, 2010;
Arif & Nauman Anees, 2012; Shen et al.,2009), in Europe (Bourke,1989; Poposka &
Trpkoski, 2013; Goddard, Molyneux & Wilson, 2004; Kosmidou, Tanna & Pasiouras,
2005) found out the positive effect. Others in Asia (Lee & Kim, 2013); in Africa
(Bassey & Moses, 2015) found out the negative effect. A number of studies was also
found that there was a very weak relationship or no relationship between liquidity risk
and bank performance (Sufian & Chong, 2008; Roman & Sargu, 2015; Alper &
Anbar, 2011; Almumani, 2013; Ayaydin & Karakaya, 2014) or depends on economic
characteristics and research models (Naceur & Kandil, 2009; Ferrouhi, 2014).
After reviewing studies, the authors found that the majority of the previous studies
approaching the impact of liquidity risk on bank performance efficiency (Sufian &
Chong, 2008; Sayedi, 2014; Oluwasegun & Samuel, 2015; Lartey, Antwi, & Boadi,
2013; Bourke,1989; Tabari, Ahmadi & Emami, 2013; Arif & Nauman Anees, 2012;
Bassey & Moses, 2015; Ferrouhi, 2014; Alshatti, 2015; Aburime,2009; Athanasoglou
et al., 2008; Naceur & Kandil, 2009). Some studies have approached the impact of
bank performance efficiency on liquidity risk in different countries (Vodova, 2011;
Abdullah & Khan, 2012; Roman & Sargu, 2015). This shows that the trend of the
impact of the liquidity risk on bank performance efficiency has been attented by the
researchers and managers, especially the impact of the financial crisis on bank
performance efficiency (Lee và Kim, 2013).
Most empirial researchs approaching to factors affecting to liquidity risk and
the impact of liquidity risk on bank performance efficiency have taken in the region of
one country only, except study of (Roman & Sargu, 2015) based on European data or
(Bordeleau & Graham, 2010) in America, (Shen et al.,2009) both in Europe and
America. Cross-countries studies on aspect to examine the interlinkage between
liquidity risk and bank performance efficiency. On the other hand, some recent study
claimed that liquidity is endogenously determined and the question on the impact of
liquidity risk on bank performance efficiency cannot be studied without controlling
for endogeneity.
1

The empirial researchs showed that Vietnamese is one of the countries with lower
average income population of Southeast Asia and there are too many banks but lacked
a main banking to competitive with other regional economies (Nguyen Cong Tam &
Nguyen Minh Ha, 2012). Thus, this study used Bankscope data of 171 banks during
the period 2004–2016 and the system Generalized Method of Moments (SGMM)
method to analyze the impact of liquidity risk on bank efficiency performance in
South-East Asia countries to estimate the impact of liquidity risk on banks financial
performance in South-East Asia countries case. Studies in different spaces and periods
will give unequal results.
To fill the gap research, thesis combining research approach to factors influencing
liquidity risk and the impact of liquidity risk on bank efficiency performance in SouthEast Asia countries are extremely important and valuable.
So, the author selected the topic "The Impact of Liquidity Risk on Bank Fficiency
Performance : A Case Study in South-East Asia Countries" as a thesis. In addition, the
study combines a case study of South - East Asia and Vietnamese to propose policy
suggestions for Vietnam. This study will contribute to empirical evidence and provide
some useful information on the factors affecting liquidity risk and the impact of
liquidity risk on bank efficiency performance.
1.2. Research objectives
1.2.1. Research objectives
The main objective of the thesis is to identify factors influencing liquidity risk and
analyze the impact of liquidity risk on bank efficiency performance, study the case of
South-East Asia countries in the period of 2004 - 2016.
1.2.2. Specific objectives:
Based on that, the specific objectives of the project are defined as:
The firstly, to analyze factors influencing liquidity risk, study the case of South-East
Asia countries and Vietnamese.
The secondly, to analyze the impact of liquidity risk on bank efficiency performance,
study the case of South-East Asia countries and Vietnam..

Thirdly, to suggested policies on liquidity risk management and Effective
management of banking in Vietnam.
1.2.3. Research questions
1) What factors influence the liquidity risk and the level impact of factors on
liquidity risk the case of South-East Asia countries?

2


2) Are there any differences in the results study the factors affecting the liquidity
risk in the case of South-East Asia countries and Vietnam?
3) What is the impact of liquidity risk on bank efficiency performance, in the case
of of South-East Asia countries?
4) Are there any differences in the results study impact of liquidity risk on bank
efficiency performance, in the case of South-East Asia countries and Vietnam?
5) What are the policy implications of liquidity risk management and Effective
management of banking in Vietnam.
1.3. Research Object and research scope:
Research Object: The study Object of the thesis are liquidity risk and bank
efficiency performance, in the case of South-East Asia countries.
Research scope: The scope of the study was extended to for 11 countries in
South-East Asia (Brunei, Cambodia, EasiTimor, Indonesia, Laos, Myanmar,
Malaysia, Philippines, Singapore, Thai Land, Viet Nam) from 2004 to 2016.
The database was collected from 2 sources: (i) bank-level data from Bankscope,
(ii) macroeconomic information data from Asian Development Bank (ADB).
1.4. Recearch methodology:
The research has combined the approach of (Ferrouhi & Lahadiri, 2014; Trenca,
Petria & Corovei, 2015) to analyze impact of the factors on liquidity risk and its
approach (Growe và cộng sự, 2014; Ferrouhi, 2014) to analyze the impact of liquidity
risk on bank efficiency performance, in the case of South-East Asia countries.

On the other hand, comparative study of the result in the case of South-East Asia
countries and Viet Nam to proposed policy implication for Vietnam.
1.5. Thesis structure:
The structure of the thesis consists of 5 chapters:
Chapter 1: Introduction
Chapter 2: Theory basis and literature review
Chapter 3: Research Methodology
Chapter 4: Research results
Chapter 5: Conclusions and Policy Implications

3


CHAPTER 2
THEORY AND LITERATURE REVIEW
2.1. Liquidity Risk in Commercial Banks
2.1.1. Theoretical Framework Underlying the Study
2.1.1.1 Commercial Loan Theory and Liquidity
2.1.1.2 The Shiftability Theory of Liquidity
2.1.1.3 Anticipated Income Theory of Liquidity
2.1.2 The concept of liquidity risk
The Basel Committee on Banking Supervision (2003) contends that Liquidity Risk
is a risk that a bank's inability to accommodate decreases in liabilities or to fund
increases in assets
Rudolf Duttweiler1, contends that Liquidity represents the ability to payment all
payment obligations upon maturity. The inability of banks to raise liquidity can be
attributed to a funding liquidity risk that is caused either by the maturity mismatch
between inflows and outflows and/or the sudden and unexpected liquidity needs
arising from contingency conditions. Lack of liquidity will lead to liquidity risk.
According Bonfim and Kim (2014), the complexity of the functions of banks gives

rise to an intrinsic risk that lies deep in their core function; their unique intermediation
role. Banks use a limited amount of their own resources in granting loans to
entrepreneurs and consumers and thus provide them with the liquidity to finance their
investment and consumption demands. Much of these resources used by these banks
are normally associated with liabilities to third parties traditionally in the form of
deposits. For profit purposes, this transformation of liquid liabilities (deposits) into
risky liquid (illiquid) assets in the form of loans capitalizing on their maturity
mismatch expose them to liquidity risk (Diamond and Dybvig, 1983; Jekinson, 2008).
In order to lessen the maturity gap between assets and liabilities or the inherent
illiquidity, banks can adequately manage the liquidity risk underlying their balance
sheet structure by holding a buffer of liquid assets. However, aside the high
opportunity cost of holding a buffer of liquid assets as compared to the higher returns
associated with illiquid assets, it manifests a degree of inefficiency on the part of
management as it limits banks' ability to provide liquidity to entrepreneurs and
1

Rudolf Duttweiler: "Liquidity Management in Banking: Top-down Approaches", Ho Chi Minh City General
Publisher, p.23

4


consumers. Hence, even though banks have some incentives to hold a fraction of
liquid assets (in the form of cash, short term assets or government bonds), these
buffers will hardly ever be sufficient to fully insure against a bank run or liquidity risk
(Bonfim and Kim, 2014).
2.1.3 Liquidity risk measurement methods
2.1.3.1 Approaches to the guaranteed ratio is regulated by the the Basel Monitoring
Committee
2.1.3.2 Approaches to liquidity indicators

2.1.4 Empirical literatures on determinants of liquidity risk
Though liquidity risk has always been considered in literature as a major
determinant of bank performance, only a few of studies have gone further to take into
consideration the various determinants of liquidity risk in the daily operations of a
bank. Work done by some few researchers show varied determinants in different
banking environments basically categorized under bank specific and macro-economic
factors.
The determinants affected liquidity risk are focused on the following factors:
The bank size: previous studies found that a negative relationship between
bank size and liquidity risk (Lucchetta, 2007; Munteanu, 2012; Abdullah & Khan,
2012; Delécha et al., 2012; Bonfim & Kim, 2014). While other studies suggested that
the relationship between bank size and liquidity risk may be nonlinear or ambiguous
(Vodova, 2011; Shen et al., 2009; Aspachs & cộng sự, 2005; Truong Quang Thong,
2013).
Asset quality: A key liquidity ratio is the liquid assets ratio (Liquid
assets/Total assets). Previous studies (Bonfim và Kim, 2014; Bunda và Desquilbet,
2008; Delécha và cộng sự, 2012; Lucchetta, 2007; Munteanu, 2012; Vodova, 2011)
was found that lower liquidity means higher risk. The portfolio theory suggests higher
risk leads to higher profitability. In addition, some studies (Lucchetta, 2007; Bunda &
Desquilbet, 2008; Vodova, 2011; Delécha et al., 2012) is used be the ratio of liquid
assets/total deposits. Liquidity has also been measured by liquid assets to total
deposits (Liquid assets/deposits) and some studies measured by liquid assets to shortterm deposits (Bunda & Desquilbet, 2008;Vodova, 2011; Cucinelli, 2013; Delécha et
al., 2012). Hence, higher values of this ratio denote less liquidity. The higher the
liquidity structure, the lower the liquidity risk.
Capital: indicators measure the strength of the bank’s capital position,
including its ability to withstand and recover from economic shocks. Theoretical
expectations, as well as empirical results (Lucchetta, 2007; Bunda & Desquilbet,
5

2008; Cucinelli, 2013; Munteanu, 2012; Bonfim & Kim, 2014; Trương Quang Thông,
2013), for the equity to assets ratio (Total equity/Total assets) suggest that the ratio
will be positively related to liquidity risk. In addition, studies across industries have
found that the actual relationship between Capital and liquidity risk was negative
(Delécha et al., 2012; Berger & Bouwman, 2013). The implication is that the level of
equity in a bank’s capital structure should be negatively related to liquidity risk.
Credit risk: is the risk that a portion of interest or both interest and principal
of a loan will not be repaid as committed. The competitiveness of the bank depends
largely on its ability to manage credit risk (Bonfim & Kim, 2014). Previous studies
(Delécha et al., 2012; Cucinelli, 2013; Bonfim & Kim, 2014; Trenca, Petria &
Corovei, 2015) using Loan Loss Provision/Total Loans is assessed by credit risk.
While other studies (Bonfim & Kim, 2014; Cucinelli, 2013; Delécha et al., 2012)
suggested that higher lending ratios, lower liquidity. It means, bank have more
vulnerable capital structures, liquidity risk is higher.
Interest income: a key ratio is the efficiency or cost to income ratio (interest
expense/Total income). Several studies have found that high values on it higher
liquidity risk (Abdullah & Khan, 2012; Bonfim & Kim, 2014; Delécha et al., 2012;
Munteanu, 2012).
The macro factors include: The GDP growth variable is assessed by the
year’s real change in gross domestic product (GDP) for the nation the bank is located
in, sometimes on a per capita basis. GDP growth was related to liquidity risk in a
number of studies (Aspachs et al., 2005; Bonfim & Kim, 2014; Bunda & Desquilbet,
2008; Cucinelli, 2013; Delécha et al., 2012; Munteanu, 2012; Growe et al., 2014;
Trương Quang Thông, 2013; Vodova, 2011). The inflation rate is assessed by
entering in the CPI change rate for the nation and the year. GDP as a measure of total
economic activity in an economy, higher economic growth encourages banks to lend
more and permits them to charge higher margins, and improve the quality of their
assets as suggested by previous studies (Aspachs et al., 2005; Bonfim & Kim, 2014;
Bunda & Desquilbet, 2008; Cucinelli, 2013; Delécha et al., 2012; Munteanu, 2012;
Growe et al., 2014; Trương Quang Thông, 2013; Vodova, 2011). Financial crisis

(Bunda & Desquilbet, 2008; Delécha et al., 2012; Lucchetta, 2007; Munteanu, 2012;
Growe et al., 2014; Shen et al., 2009; Skully & Perera, 2012; Vodova, 2011) was
found that is one of the factors affecting the liquidity risk.
As, the findings of previous studies are quite consistent with the realities in the
financial markets. The empirical studies continue to assess the determinants of the
impact on the liquidity risk in banks through the specific factors (bank size, asset
6


quality, capital, credit risk, interest income,…) and macro factors (Real GDP
growth rate, fluctuations of inflation, financial crisis, ..).
2.2 Bank Performance Efficiency
2.2.1 Theories on Bank Performance Efficiency
Bank Performance Efficiency is often measured by profitability. Studies of
Bank Performance Efficiency or profitability is used basing on two theories: market
power theory (MP – market power) and structural efficiency theory (ES - efficient
structure).
2.2.1.1 The theory of market power
2.2.1.2 The theory of efficient structure
2.2.2 The concept of Performance Efficiency in bank
When evaluating the Performance Efficiency in business, it can be based on
two indicators that are absolute efficiency and relative efficiency.
Absolute efficiency: Measured by business results minus cost to achieve results.
This ratio reflects the scale, volume and profits gained in specific conditions, time and
place.
Relative efficiency: based on comparative ratio between inputs and outputs.
Relative efficiency is defined as: Efficiency = output / input or Efficiency = input /
output. This assessment is very convenient on comparing different organizations from
sizes, space and time.
2.2.3 Methods of measuring bank’s Performance Efficiency

In methodology, previous studies used three different approaches to measuring
business performance: measure Performance Efficiency by the ratio method, measure
the Performance Efficiency from market data and measure Performance Efficiency
from profit margin. The results of applying different measurements can lead to
different results. No research has shown which measurement method is the best. For
the selection methods, the authors combine the assessment, analysis of advantages and
disadvantages of each method, then approach methods of measuring bank’s
Performance Efficiency in accordance with the scope and objects of research.
2.3 The impact of liquidity risk on bank’s Performance Efficiency
2.3.1 Theory of the relationship between liquidity risk and bank’s Performance

Efficiency
2.3.1.1 The theory of risk for profit
2.3.1.2 The theory of Banking Specificities Theory

7


2.3.2 Empirical reseachs on the relationship between liquidity risk and bank’s

Performance Efficiency.
Previous studies have shown that research gaps exist in relation to the relationship
between liquidity risk and bank’s Performance Efficiency.
Firstly, Gap on the approach. There are many studies on liquidity risk, but studies
focus on the causes of liquidity risk (Ahmed, Ahmed & Naqvi, 2011; Angora &
Roulet, 2011; Bonfim & Kim, 2014; Bunda & Desquilbet, 2008; Gibilaro, Giannotti &
Mattarocci, 2010; Horváth et al., 2012; Rauch et al., 2010; Skully & Perera, 2012;
Vodova, 2013) and studies on liquidity risk management to stabilize banks such as
(Acharya & Naqvi, 2012; Scannella, 2016; Wagner, 2007). Researchs on liquidity risk
is also considered one of the types of bank risk such as credit risk (BissoondoyalBheenick & Treepongkaruna, 2011) or one of the factors affecting bank’s

Performance Efficiency (Athanasoglou et al., 2008; Shen et al., 2009). However, only
a few studies that combine an analysis of factors affecting liquidity risk and the impact
of liquidity risk on bank’s Performance Efficiency across multiple countries.
Secondly, Gap on the spaces and periods researchs. Most empirial researchs
have taken in the region of one country only, except study of (Roman & Sargu, 2015)
based on European data or (Bordeleau & Graham, 2010) in America, (Shen et al.,
2009) both in Europe and America. Cross-countries studies on aspect to examine the
interlinkage between liquidity risk and bank’s performance efficiency. According to
the author, these are only three well-known empirical studies of liquidity risk and
bank’s performance efficiency across multiple countries and are published in highly
reliable journals. In the case of South East Asia countries, there is no separate study
on the impact of liquidity risk on bank’s performance efficiency across multiple
countries. Different spaces and periods researchs, will be result in dissimilar results on
the relationship between liquidity risk and bank’s performance efficiency.
Thirdly, Gap on the measurement elements. Other empirical studies also showed
that there are many factors affecting the bank’s performance efficiency such as:
lending chanel through the ratio of loan of total assets (Nguyen Viet Hung, 2008; Gul
et al., 2011; Trinh Quoc Trung & Nguyen Minh Sang, 2013…); Banking capital
mobilization and operation using bank capita used be the ratio of total mobilized
capital of total loan (Nguyen Viet Hung, 2008; Nguyen Thi Loan & Tran Thi Ngoc
Hanh, 2013 ...); the size of equity (Nguyen Viet Hung, 2008; Gul et al., 2011; Nguyen
Thi Loan & Tran Thi Ngoc Hanh, 2013; Ongore & Kusa, 2013 …); the size of asset
(Nguyen Viet Hu, 2008; Gul et al., 2011; Ongore & Kusa, 2013; Ayadi, 2014 …), the
economic growth rate (Gul et al., 2011; Ongore & Kusa, 2013;…), the inflation rate
8


(Gul et al., 2011; Ongore & Kusa, 2013;…). Particularly, the factors influencing
liquidity risk have used but rarely. Some studies had just used liquidity ratios to
measure liquidity risk but Poorman and Blake (2005)2 indicated that it was not enough

to measure liquidity just using liquidity ratios and it was not the best solution. In this
study, the combination of financial gap method and liquidity ratios to measure
liquidity risk in banking business.
Fourthly, Gaps in character:
Liteliture review showed that the trend of studying the impact of liquidity risk on
bank’s performance efficiency is mainly (Sufian & Chong, 2008; Sayedi, 2014;
Oluwasegun & Samuel, 2015; Lartey, Antwi & Boadi, 2013; Bourke,1989; Tabari,
Ahmadi & Emami, 2013; Arif & Nauman Anees, 2012; Bassey & Moses, 2015;
Ferrouhi, 2014; Alshatti, 2015; Aburime, 2009; Athanasoglou et al., 2008; Naceur &
Kandil, 2009; Wasiuzzaman & Tarmizi, 2010; Lee & Kim, 2013). Other studies have
approached the impact of bank’s performance efficiency on liquidity risk across
multiple countries (Abdullah & Khan, 2012; Roman & Sargu, 2015). So, the recent
trend, scientists and managers are very interested in the impact of liquidity risk on
bank’s performance efficiency. Especially, the impact of the financial crisis on bank’s
performance efficiency (Lee & Kim, 2013).
However, it have rarely researched combined analysis of factors affecting liquidity
risk with the impact of liquidity risk on bank’s performance efficiency across multiple
countries. In general, previous studies identified that it is necessary to study the
impact of liquidity risk on bank’s performance efficiency.
In this study, we use each bank’s financing gap ratio (FGAP) as the independent
variable and financial crisis variable to compare the case studies of South East Asia
and Vietnamese countries. From which to propose policy suggestions for Vietnam.
Besides, research thesis the case of Vietnam to to compare the different impact of
liquidity risk on bank’s performance efficiency with the case of South East Asia
countries.

2

Diamond and Dybvig (1983) developed a model to explain why banks choose to issue deposits that are more
liquid than their assets

9


CHAPTER 3: RESEARCH METHODOLOGY
3.1 Research Methods
Due to the limitations of the Pool OLS model in panel data estimation (Kiviet, 1995),
so that FEM and REM model can be used to analyse individual effects. However, since FEM
and REM do not handle endogenous phenomena (Ahn & Schmidt, 1995), so the SGMM
estimation technique is used to solve the problems mentioned above (Arellano & Bond, 1991;
Hansen, 1982; Hansen, Heaton, & Yaron, 1996). The the system Generalized Method of
Moments (SGMM) estimators applied to panel data models address the problem of the
potential endogeneity of all explanatory variables, measurement errors and omitted variables.
Stata software version 12 was used to for all the estimations to determine the results of this
study.

3.2 Research models of factors influencing liquidity risk
3.2.1 Research models
The research has combined the approach of (Ferrouhi & Lahadiri, 2014) and
(Trenca, Petria & Corovei, 2015) with the addition of lag liquidity variables and credit
risk variables to examine factors influencing liquidity risk in South-East Asia case.
Econometric models are thus presented as follows:
Models (1): LIQUIDITYRISKt = f(α, LIQUIDITYRISKt-1, SIZEit, SIZEit^2,
LIAit, LLRit, LADSit, ETAit, LLPit, NIMit GDPit, INFit, M2it, D_CRISt, u)
From the equations above, the dependent variables is LIQUIDITYRISKt variables;
include liquidity risk (FGAPit – Bank’s loans – customer deposits/ total assets; NLTAit Loans /total Assets, NLSTit - Loans/deposits + Short term liabilities); The independent
variables include lag liquidity risk variables; Bank size, Natural logarithm of total assets
(SIZEit); Natural logarithm of total assets squared (SIZEit^2); liquid assets/ total
asset(LIAit); liquid assets / total Loans (LLRit); liquid assets / short term liabilities
(LADSit); the ratio of equity to total assets (ETAit); Loan loss reserves/Total loans

(LLPit); Net interest income (NIMit). Macroeconomic variables include change in GDP
(GDPit); change in inflation (INFit); change in money supply (M2it); Dummy variables
the impact of crisis on banking performance efficiency (D_CRIS).
With α (the constant), i (bank), t (year), u (the error).
Table 3.1: Relationship between dependent and independent variable in the model of
factors influencing bank liquidity risk
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SUMMARY VARIABLES TO BE USED IN THE MODEL
(Factors affecting liquidity risk in South East Asia countries and Vietnam)
Variable

Definitions / symbols

Measurement

Literature review

Results

Expecte
d

Data
Source

The dependent variables
Bank’s loans – customer deposits/ total assets

Ferrouhi & Lahadiri (2014), Shen et al., (2009);
Saunders & Cornett (2006), Arif & Nauman
Anees (2012)

BankScope

Loans /total Assets

Ferrouhi & Lahadiri (2014); Lucchetta (2007);
Vodova (2011), Roman & Sargu (2015);
Munteanu (2012)
Ferrouhi & Lahadiri (2014); Vodova (2011),
Saunders & Cornett (2006), Shen et al., (2009);
Munteanu (2012)

BankScope

FGAP
Liquidity
risk
NLTA
Loans/deposits + Short term liabilities
NLST

BankScope

The independent variables
Bank Characteristics
Lag
Liquidity

risk

SIZE

SIZE ^2

LIA

Measurement

Literature review

There is substantial evidence that
bank Liquidity levels persist from
one year to the next

lag (liquidityriskt-1 )

Delécha et al., (2012), Trenca, Petria & Corovei
(2015)

According to the economic theory
of scale, The larger the bank size,
The lower the liquidity risk

Log (total Assets)

Hypothesis “too big too fall” the
larger the bank size, the greater the
risk.

Components of liquid assets may
vary across countries, but generally
include cash, government securities,
interbank deposits, and short-term
marketable securities. Lower
liquidity means higher risk.

Log (total Assets)^2

Results

Expecte
d
(+)

Data
Source
BankScope

(-)

BankScope

(+/-)

BankScope

(-)

BankScope

.
Delécha et al., (2012); Lucchetta (2007); Truong
Quang Thong, (2013); Vodova (2011), Trenca,
Petria & Corovei (2015), Ferrouhi & Lahadiri
(2014), Bonfim & Kim (2014); Horvàth et al.,
(2012)et al., (2009); Truong Quang Thong,
Shen
(2013); Lee & Kim (2013); Ferrouhi &
Lahadiri (2014).
Delécha et al., (2012), Vodova (2011), Ferrouhi
& Lahadiri (2014), Ahmed et al., (2011).

liquid assets/ total asset

11


Liquid assets /short term liabilities

Vodova (2011), Cucinelli (2013), Delécha et al.,
(2012), Ferrouhi & Lahadiri (2014)

(-)

BankScope

liquid assets / total Loans

Lucchetta (2007); Vodova (2011), Delécha et

al., (2012), Ferrouhi & Lahadiri (2014)

(-)

BankScope

Access to fragile financial structure
and dominance deposit structure
effects suggests that capital and
Liquidity risk
are positively
correlated
Banks with a higher proportion
of reserves may be those with more
aggressive lending strategies.
Capital Structure is vulnerable, the
higher the risk

the ratio of equity to total assets

Delécha et al., (2012), Lucchetta (2007);
Cucinelli (2013), Munteanu (2012), Ferrouhi &
Lahadiri (2014)

(+)

BankScope

Loan loss reserves/Total loans

Delécha et al., (2012), Cucinelli (2013), Trenca,
Petria & Corovei (2015), Arif & Nauman Anees
(2012)

(+)

BankScope

The higher the interest from the loan
portfolio, The higher the liquidity
risk

(Interest income - Interest expense) /
Average asset

Delécha et al., (2012), Munteanu (2012);
Trenca, Petria & Corovei (2015), Roman &
Sargu (2015)

(+)

BankScope

LADS

LLR

ETA

LLP

NIM

Dummy variables
D_CRIS

Financial crisis

1: crisis period (2008 -2010)
0: pre-crisis period (2005 -2007)

Ferrouhi & Lahadiri (2014); Delécha et al.,
(2012); Vodova (2011); Lucchetta (2007)

(+)

Ferrouhi & Lahadiri (2014); Truong Quang
Thong (2013); Vodova (2011)
Yurdakul (2014a)

(+)

ADB

(+)

ADB

Ferrouhi & Lahadiri (2014); Bonfim & Kim
(2014); Vodova ( 2011); Cucinelli (2013)

(+)

ADB

Macroeconomic variables
GDP

Liquidity is always sensitive to
economic fluctuations.

M2

Change in Money supply

INF

Inflation has changed a lot,
affecting liquidity risk

Log ((GDPt-GDPt-1)/ GDPt-1)
Log(M2)
Consumer Price Index

Notes: (-) negative correlation, (+) positive correlation, (- / +) nonlinearity
Munteanu (2012)

12

Source: Self-synthesis of the author

3.3 Research models the impact of liquidity risk on Performance Efficiency
3.3.1 Research models
The research has combined the approach of (Growe et al., 2014) with the addition of
crisis variables to examine the impact of liquidity risk on Performance Efficiency. In addition,

this study is based on the model (Ferrouhi, 2014) to supplement other variables to
examine the impact of liquidity risk on Performance Efficiency in case of South-East
Asia and Vietnam. Econometric models are thus presented as follows:
Models (2): Pt = f(α, Pt-1, LIQUIDITY RISK it, CONTROLit, u)
From the equations above, the bank specific variables include liquidity risk (FGAPit – Bank’s
loans – customer deposits/ total assets; NLTAit - Loans /total Assets, NLSTit - Loans/deposits+Short
term liabilities); Control variables include bank size, Natural logarithm of total assets (SIZEit);
Natural logarithm of total assets squared (SIZEit^2 ); liquid assets/ total asset(LIAit); liquid assets /
total Loans (LLRit); liquid assets / short term liabilities (LADSit); the ratio of equity to total assets
(ETAit); the ratio of loan loss provision to loans, (LLPit). Macroeconomic variables include change
in GDP (GDPit); change in inflation (INFit); Dummy variables the impact of crisis on banking
performance efficiency (D_CRIS). The dependent variables include Pit(NIM, ROA, ROE); with α
(the constant), i (bank), t (year), u (the error).

Table 3.2: Relationship between dependent and independent variable in the model of the
the impact of liquidity risk on Performance Efficiency in case of South-East Asia

13


SUMMARY VARIABLES TO BE USED IN THE MODEL 2
(The impact of liquidity risk on Performance Efficiency in case of South-East Asia)
Definitions /

symbols

Variable

Measurement

Literature review

Results

Expec
ted

Data
Source

The dependent variables
Return on Assets

Bassey & Moses (2015), Anbar & Alper (2011). Ferrouhi
(2014), Arif & Nauman Anees (2012), Growe et al., (2014)

BankScope

ROE

Return on Equity

BankScope

NIM

(Interest income - Interest expense) /
Average asset

Bassey & Moses (2015), Ajibike & Aremu (2015), Ferrouhi
(2014); Arif & Nauman Anees (2012); Growe et al., (2014);
Anbar & Alper (2011).
Shen et al., (2009), Naceur & Kandil (2009), Ferrouhi (2014),
Arif & Nauman Anees (2012); Growe et al., (2014)

ROA
Performance Efficiency

BankScope

The independent variables
Definitions /
symbols

Variable

Measurement

Expec
ted

Data
Source

Ferrouhi (2014); Lucchetta (2007); Saunders & Cornett
(2006), Bunda & Desquilbet (2008); Shen et al., (2009)

(+)

BankScope

Literature review

Results

FGAP

Bank’s loans – customer deposits/ total
assets

NLTA

Loans /total Assets

Ayaydin & Karakaya (2014), Ferrouhi (2014), Growe et
al., (2014); Anbar & Alper (2011)

(+)

BankScope

Loans/deposits + Short term liabilities

Munteanu (2012), Ferrouhi (2014), Growe et al., (2014);

Anbar & Alper (2011); Ayaydin & Karakaya (2014)

(+)

BankScope

Ayaydin & Karakaya ( 2014); Lee & Hsieh (2013); Perera et
al., (2013); Growe et al., (2014)

(+)

BankScope

Munteanu (2012), Lee & Hsieh (2013); Anbar & Alper
(2011); Ferrouhi (2014); Growe et al., (2014)

(+)

BankScope

(-/+)

BankScope

LiquidityRisk

NLST

Control variables
Lag Performance

Efficiency
SIZE

Squared size
LIA

There is substantial evidence that bank
Performance Efficiency levels persist
from one year to the next
The larger the scale, the greater the
market power , improving technology
efficiency at low cost.
profit increased by scale at some point
will reduce profits

Components of liquid assets may
vary across countries, but generally
include cash, government securities,
interbank deposits, and short-term

lag (Pt-1 )

Log (total Assets)
Log (total Assets)^2

Shen et al., (2009); Growe et al., (2014); Ayaydin & Karakaya
(2014); Lee & Kim (2013)

liquid assets/ total asset

Kosmidou et al., (2005), Poposka & Trpkoski (2013), Shen et
al., (2009), Ferrouhi (2014); Growe et al., (2014); Anbar &
Alper (2011); Ayaydin & Karakaya (2014)

14

(+)

BankScope


marketable securities. Lower
liquidity means higher risk.
LLR

Liquid assets /short term
liabilities

Shen et al., (2009); Ferrouhi (2014); Growe et al., (2014);
Anbar & Alper (2011); Ayaydin &Karakaya (2014)

(-)

BankScope

LADS

liquid assets / total Loans

Almumani (2013), Ayaydin & Karakaya (2014) , Ferrouhi

(2014), Anbar & Alper (2011)

(-)

BankScope

(+)

BankScope

(-)

BankScope

ETA

the higher the capital, the lower the risk

the ratio of equity to total assets Shen et al., (2009), Ferrouhi (2014); Growe et al., (2014);
Anbar & Alper (2011); Ayaydin & Karakaya (2014)

LLPT

Fluctuations Asset quality often reduces Loan loss reserves/Total loans Ayaydin & Karakaya (2014); Shen et al., (2009); Trujillothe bank's profitability.
Ponce (2013); Growe et al., (2014)

Macroeconomic variables
GDP

Real change in gross domestic product

(GDP) per year for each country.

M2

Change in Money supply
Rate of change of CPI for each country
of each year

INF

Log ((GDPt-GDPt-1)/ GDPt-1)
Log ( M2)

Consumer Price Index

Shen et al., (2009); Anbar & Alper (2011); Ferrouhi
(2014); Growe et al., (2014); Ayaydin & Karakaya
(2014),

(+)

ADB

Dietrich và Wanzenried (2014)

(+)

ADB

Ayaydin & Karakaya ( 2014); Shen et al., (2009); Sufian

& Chong (2008); Ferrouhi (2014); Growe et al., (2014);
Anbar & Alper (2011)

(-)

ADB

Dummy variables
D_CRIS

Financial crisis

Ferrouhi (2014), Sufian & Chong (2008); Growe et al.,
1: crisis period (2008 -2010)
0: pre-crisis period (2005 -2007) (2014); Ayaydin & Karakaya (2014),

Notes: (-) negative correlation, (+) positive correlation, (- / +) nonlinearity

15

Source: summary from research results of the author


CHAPTER 4: EMPIRICAL RESULTS
4.1 Factors affecting Liquidity Risk
4.1.1 Descriptive statistics
Table 1.1: Descriptive statistics for variables, case study of South East Asia countries
Table 4.2: Descriptive statistics for variables, case study in Viet Nam
4.1.2 Analysis of correlation coefficient
Table 4.3: Correlation between independent variables in the model of factors affecting

Liquidity Risk, case study of South East Asia countries
Table 4.4: Correlation between independent variables in the model of factors affecting
Liquidity Risk, case study in Vietnam
4.1.3 Analysis and discussion of results, case studies of South East Asia countries.
To evaluate factors affecting Liquidity Risk in banks, the study used 12 different
regressions (Table 4.5). The study used the F, LM, Hausman test to select the appropriate
model for the analysis. VIF ratio is less than 20, so the model does not exist multi-collinear
phenomenon. The P-values of F, LM test are less than 5% (<0.05), there is evidence to
reject the hypothesis. Hausman's test for the p-value (Prob> F) of the model is less than 0.05
(Table 4.5), this suggests that the FEM model is more appropriate than REM.
Wooldridge test and Wald test with P-value (<0.05) indicates the presence of
variance and self-correlation in FEM, which results in inefficient regression coefficients.
The author continues to use the SGMM method to estimate the model, which eliminates the
problem of variance, autocorrelation or endogenity so the estimation result will be effective
and durable. The results of the final analysis are based on the SGMM method. Sargan Test
to test the over-identifying of tool variables. The results show that the p-value coefficient is
greater than 0.05, indicating that the tool variable used in the GMM model is suitable. The
results are robust and fully analy (Table 4.5).

16


Table 4.5: Factors affecting bank liquidity risk, case study of Southeast Asian countries (Appendix)
Models (1): LIQUIDITYRISKt = f(α, LIQUIDITYRISKt-1, SIZEit, SIZEit^2, LIAit, LLRit, LADSit ETAit, LLPit, NIMit GDPit, INFit, M2it,D_CRISt, u)
Dependent variable: Liquidity risk (FGAP, NLTA, NLST).
Independent variable: (SIZE- natural logarithm of total assets; SIZE^2- natural logarithm of total assets squared; LIA- the ratio liquid assets to total assets; LLR- the ratio liquid assets / total Loans, LADSthe ratio liquid assets / short term liabilities; ETA- the ratio of equity to total assets.; LLP- the ratio of loan loss provision to loans, GDP- Annual percent change of GDP, INF- Annual percent change of
inflation, M2 – Annual percent change of money supply; D_cris - Dummy variable ).
Database from 2004 to 2016. Estimation technique: OSL, FEM, REM và SGMM.
Model

OLS

Variable
L.fgap

FEM

REM

SGMM

OLS

FEM

FGAP

REM

SGMM

NLTA

0.787***

0.518***

0.761***

0.396***

[53.92]

[21.73]

[48.50]

[11.97]

L.nlta

0.800***

0.551***

[69.28]

[29.74]

size2

lia

0.759***
[59.35]

eta

gdp

SGMM

0.374***
[9.16]
0.758***

0.378***

0.758***

0.253***

[62.94]

[17.46]

[62.94]

[106.35]

-0.0491***

0.00483

-0.0276**

0.521**

-2.487**

0.494*

-2.375**

2.677**

-0.815

2.677**

-12.18***

[1.38]

[-3.63]

[1.35]

[-2.10]

[2.05]

[-2.46]

[1.73]

[-2.54]

[2.21]

[-0.17]

[2.21]

[-9.53]

0.0000966

0.00426***

0.0000805

0.00242**

0.0713***

0.169**

0.0682**

0.164**

0.261**

0.0468

0.261**

0.829***

[0.29]

[4.06]

[0.23]

[2.29]

[-2.75]

[2.15]

[-2.47]

[2.28]

[-2.10]

[0.13]

[-2.10]

[8.42]

-0.00260***

-0.00637***

-0.00302***

-0.0156***

-0.0189

-0.0699

-0.0122

-0.497***

-0.549**

-0.528

-0.549**

-0.975***

[3.97]

[5.97]

[4.32]

[15.09]

[-0.38]

[0.88]

[-0.22]

[2.31]

[1.41]

[2.31]

[5.65]

0.00000764***

0.00000787***

0.00000805***

-0.0000116

0.00103***

0.00105***

0.00110***

[5.32]
0.00121***

0.00158**

0.00111

0.00158**

0.00197***

[4.27]

[3.73]

[4.42]

[11.11]

[7.59]

[6.69]

[7.86]

[5.99]

[2.44]

[1.50]

[2.44]

[8.58]

-0.000956***

-0.00117***

-0.000992***

-0.00150

-0.108***

-0.130***

-0.116***

-0.154***

-0.209***

-0.0512

-0.209***

0.0330

[-9.04]

[-8.53]

[-9.04]

[-13.43]

[-13.24]

[-12.81]

[-13.46]

[-14.75]

[-5.84]

[-1.08]

[-5.84]

[-3.43]

0.00165***

0.00228***

0.00184***

0.00955***

-0.0387*

-0.194***

-0.0532**

0.266***

0.433***

-0.315

0.433***

1.942***

[5.15]

[3.76]

[5.29]

[12.10]

[-1.65]

[-4.30]

[-1.99]

[4.33]

[-1.48]

-0.000543***

-0.000533***

-0.00102***

-0.0579***

-0.0616***

-0.0620***

-0.108***

-0.0830***

[3.71]
0.0927***

[20.64]

-0.000505***

[3.71]
0.0927***

-0.156***

[-7.99]

[-6.57]

[-8.16]

[-12.50]

[-12.11]

[-10.04]

[-12.37]

[-9.39]

[-4.19]

[-2.88]

[-4.19]

[-12.72]

0.00543***

0.0122***

0.00643***

0.00236***

0.443***

0.797***

0.554***

0.188***

0.489

1.484**

0.489

0.676***

[5.69]

[6.12]

[6.22]

[1.14]

[6.04]

[5.28]

[6.69]

[0.95]

[1.47]

[2.17]

[1.47]

[3.50]

-0.000026

-0.0000797

-0.0000331

0.0000204

-0.000921

-0.00372

-0.00132

-0.00499

0.013

-0.000881

0.013

0.00983***

[-0.46]

[-1.49]

[-0.60]

[-0.76]

[-0.21]

[-0.93]

[-0.31]

[-1.96]

[0.63]

[-0.05]

[0.63]

[3.77]

llp

nim

REM

0.00455

llr

lads

FEM

NLST

L.nlst

size

OLS

17


infl

0.000612

-0.00159**

0.000313

-0.000292***

0.0349

-0.0822

0.0128

-0.0404**

-0.0143

-0.158

-0.0143

0.144***

[1.05]

[-2.24]

[0.52]

[-0.76]

[0.78]

[-1.55]

[0.27]

[0.98]

[-0.64]

[-0.07]

[2.88]

-0.00000296

-5.06E-06

-0.00000308

-0.00000322***

-0.000167

-0.000249

-0.000231

-0.0000481

[-0.07]
0.0000777

-0.000799

-7.8E-05

-0.0000712

[-1.57]

[-1.48]

[-1.53]

[-2.60]

[-1.15]

[-0.97]

[-1.43]

[0.41]

[-0.11]

[-0.67]

[-0.11]

[-0.35]

0.0130**

0.0315***

0.0140***

0.0317***

0.986**

2.203***

1.106***

1.646***

4.103**

4.797***

4.103**

0.789

[2.55]

[6.00]

[2.76]

[7.96]

[2.53]

[5.62]

[2.88]

[5.57]

[2.21]

[2.65]

[2.21]

[1.48]

-0.125***

-0.146***

-0.144***

-0.267***

11.25***

31.53***

12.91***

38.14***

5.045

46.61***

5.045

55.03***

[-8.81]

[-5.25]

[-9.42]

[-10.21]

[10.81]

[14.76]

[11.28]

[13.12]

[1.20]

[5.04]

[1.20]

[20.09]

1372

1372

1372

1194

1372

1372

1372

1194

1372

1372

1372

1194

0.852

0.519

0.907

0.646

0.8

0.23

m2

d_cris

_cons

N
R-sq
Mean VIF
White's test

3.6

Ho: homoskedasticity
chi2(116) = 240.50
Prob > chi2 = 0.0000

BreshPagan test
Sargan test

ArellanoBond test

Turning
Point
Size (%)

3.47

Ho: homoskedasticity
chi2(116) = 345.34
Prob > chi2 = 0.0000

F-test

Hausman
test

3.59

F test that all u_i=0:
F(151, 1207) = 2.59
Prob > F = 0.0000

Ho: homoskedasticity
chi2(116) = 1044.71
Prob > chi2 = 0.0000
F test that all u_i=0:
F(151, 1207) = 3.09
Prob > F = 0.0000

Ho: difference in coefficients not systematic
chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 210.78
Prob > chi2 = 0.0000

Ho: difference in coefficients not systematic
chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 293.90
Prob > chi2 = 0.0000

Test: Var(u) = 0
chibar2(01) = 3.45
Prob > chibar2 = 0.0316
H0: overidentifying restrictions are valid
chi2(65) = 86.63543
Prob > chi2 = 0.0378
H0: no autocorrelation
Prob > z = 0.7165

Test: Var(u) = 0
chibar2(01) = 8.28
Prob > chibar2 = 0.0020
H0: overidentifying restrictions are valid
chi2(65) = 87.68562
Prob > chi2 = 0.0336
H0: no autocorrelation
Prob > z = 0.9076

F test that all u_i=0:
F(151, 1207) = 3.37
Prob > F = 0.0000

Ho: difference in coefficients not systematic
chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) =
456.56
Prob > chi2 = 0.0000
Test: Var(u) = 0
chibar2(01) = 0.00
Prob > chibar2 = 0.0000
H0: overidentifying restrictions are valid
chi2(65) = 87.1709
Prob > chi2 = 0.0347
H0: no autocorrelation
Prob > z = 0.2926

299.60

1395.28

1550.29

Note: The symbols (***), (**), (*) indicate statistically significant levels of 1%, 5%, 10%
Turning points are calculated according to the formula

18

similar to Ouyang và Rajan (2010).


Table 4.6: Factors affecting bank liquidity risk, case study of South east Asia countries
Result
Variable

OLS

Expected

SGMM

FGAP

NLTA

(+)
(-)

(+)

(+)
(+)

(+/-)

(-)

(-)

LIA

(-)

(-)

(-)

LLR

(-)

(-)

(-)

LADS

(-)

(+)

(+)

ETA

(+)

(+)

(-)

(+)

(+)

(+)

(+)

LLP

(+)

(-)

(-)

(-)

(-)

(-)

(-)

NIM

(+)

(+)

(+)

(+)

(+)

(+)

GDP

(+)

M2

(+)

INFL
D_CRIS

(+)
(+)

liquidityriskt-1
SIZE
SIZE ^2

NLST
(+)

(-)

FGAP

NLTA

NLST

(+)
(-)

(+)
(-)

(+)
(-)

(+)

(+)

(+)

(-)

(-)

(-)
(+)

(-)

(+)
(-)

(+)
(-)

(-)
(+)

(-)
(+)

(+)

Source: summary from research results of the author
In summary, the results of the model above show many interesting contents.
Impact of factors such as bank size, lag liquidity, quality asset, bank capital, marginal
interest, financial crisis on liquidity risk, case of South East Asia countries, expectations of
the author. The effect of bank-size variables on liquidity risk is nonlinear and U-shaped,
suggesting that bank size is a buffer to limit the risk of bank failures. Increasing the scale at
some point will affect the liquidity risk. In addition, research shows banks are prone to
higher liquidity risk in the financial crisis.
4.1.4 Analysis and discussion of results, case study in Vietnam
To answer the question which factors affecting the liquidity risk of the case study
bank in Vietnam, this study has implemented regression models from the data of 26
Vietnamese banks in the period 2004-2016 for the following results:
Table 2: Factors affecting bank liquidity risk, case study in Vietnam (Appendix)

19


Table 4.7: Factors affecting bank liquidity risk, case study in Vietnam (Appendix)
Models (1): LIQUIDITYRISKt = f(α, LIQUIDITYRISKt-1, SIZEit, SIZEit^2, LIAit, LLRit, LADSit ETAit, LLPit, NIMit GDPit, INFit, M2it,D_CRISt, u)

Dependent variable: Liquidity risk (FGAP, NLTA, NLST). Independent variable: (SIZE- natural logarithm of total assets; SIZE^2- natural logarithm of total assets squared; LIA- the ratio liquid
assets to total assets; LLR- the ratio liquid assets / total Loans, LADS- the ratio liquid assets / short term liabilities; ETA- the ratio of equity to total assets.; LLP- the ratio of loan loss provision to loans,
GDP- Annual percent change of GDP, INF- Annual percent change of inflation, M2 – Annual percent change of money supply; D_cris - Dummy variable ).
Database from 2004 to 2016. Estimation technique: OSL, FEM, REM và SGMM.
Model
OLS
FEM
REM
SGMM
OLS
FEM
REM
SGMM
OLS
FEM
REM
SGMM
Variable
L.fgap

FGAP

NLTA

0.396***

0.152***

0.351***

0.122**

[8.82]

[3.00]

[7.79]

[-1.34]

L.nlta

NLST

0.444***

0.208***

0.410***

-0.0107

[9.72]

[3.98]

[8.99]

[-0.12]

L.nlst

size

size2

lia

llr

lads

eta

llp

nim

gdp

0.556***

0.281***

0.556***

0.109

[12.31]

[4.56]

[12.31]

[0.84]

0.369**

0.291

0.388**

-0.306

37.38**

27.95

38.04**

74.91

53.89**

63.42**

53.89**

76.26

[2.23]

[1.64]

[2.33]

[-0.94]

[2.35]

[1.62]

[2.38]

[1.51]

[2.45]

[2.46]

[2.45]

[0.69]

1.175***

1.254***

1.254***

1.669

107.6***

116.2***

113.5***

152.6

95.91***

110.9**

95.91***

50.23

[4.41]

[3.91]

[4.72]

[2.65]

[4.16]

[3.70]

[4.40]

[3.26]

[2.79]

[2.38]

[2.79]

[0.41]

0.00718***

0.00780***

0.00713***

0.0137

0.069

-0.0962

0.029

0.533

0.334**

0.441**

0.334**

-1.497***

[6.27]

[5.48]

[5.96]

[6.68]

[0.67]

[-0.71]

[0.27]

[1.32]

[2.32]

[2.18]

[2.32]

[2.98]

0.000710***

0.000827***

0.000758***

-0.000707***

0.0679***

0.0785***

0.0711***

-0.121***

0.0727***

0.0965***

0.0727***

-0.0922***

[5.66]

[5.87]

[6.07]

[3.85]

[5.64]

[5.68]

[5.92]

[3.56]

[4.48]

[4.75]

[4.48]

[1.18]

0.00227

-0.000465

0.00148

0.00875

0.223

-0.1

0.144

1.199

0.389

0.299

0.389

1.510***

[0.62]

[-0.09]

[0.38]

[3.72]

[0.63]

[-0.21]

[0.39]

[1.27]

[0.80]

[0.42]

[0.80]

[1.94]

0.0105

-0.00719

0.00614

0.00787***

0.651

-0.845

0.344

0.464***

-0.0433

-1.533

-0.0433

-0.958

[1.65]

[-0.94]

[0.89]

[-1.27]

[1.05]

[-1.13]

[0.52]

[0.36]

[-0.05]

[-1.37]

[-0.05]

[-0.39]

-0.00742***

-0.00793***

-0.00787***

-0.00644***

-0.700***

-0.752***

-0.731***

-1.097***

-0.750***

-0.913***

-0.750***

-0.808

[-7.44]

[-6.90]

[-7.86]

[-3.94]

[-7.24]

[-6.65]

[-7.53]

[-3.53]

[-5.82]

[-5.50]

[-5.82]

[-1.14]

0.00574

0.0149***

0.00733

0.0161***

0.419

1.321***

0.547

0.755

0.512

1.260*

0.512

3.129***

[1.32]

[3.14]

[1.63]

[4.43]

[1.00]

[2.84]

[1.26]

[0.73]

[0.89]

[1.83]

[0.89]

[1.62]

0.0058

0.0123*

0.00545

0.0293***

0.64

1.198*

0.619

2.424

0.913

1.15

0.913

2.786

20


infl

[0.73]

[1.72]

[0.72]

[5.04]

[0.83]

[1.72]

[0.84]

[1.64]

[0.87]

[1.12]

[0.87]

[1.61]

-0.000118

-0.000101

-0.000187

0.000494

0.00353

-0.00465

-0.00214

-0.0366

0.105

0.0858

0.105

0.14

[-0.15]

[-0.16]

[-0.26]

[0.05]

[-0.07]

[-0.03]

[0.91]

[1.04]

-0.00000222

-0.0000026

-0.00027

-0.00022

-0.00025

[-0.70]
0.000727**

[1.04]

-2.92E-06

[1.23]
0.00000332***

-0.00058

-0.000503

-0.00058

[1.58]

0.000744***

[-0.93]

[-0.84]

[-0.88]

[-2.80]

[-0.88]

[-0.86]

[-0.86]

[-2.40]

[-1.38]

[-1.31]

[-1.38]

[-2.87]

-0.00924

-0.00714

-0.00928

-0.00716

-0.158

-0.451

-0.243

0.0788

1.423

1.74

1.423

2.769

[-0.72]

[-0.64]

[-0.76]

[-0.77]

[-0.13]

[-0.42]

[-0.20]

[0.05]

[0.83]

[1.07]

[0.83]

[1.09]

-0.217***

-0.320***

-0.220***

-0.552***

35.38***

50.21***

38.68***

43.47***

28.13***

46.12***

28.13***

21.33*

[-3.35]

[-4.52]

[-3.41]

[-9.40]

[5.46]

[7.49]

[6.01]

[3.98]

[3.17]

[4.56]

[3.17]

[1.68]

157

157

157

130

157

157

157

130

157

157

157

130

0.913

0.804

0.908

0.795

0.899

0.682

m2

d_cris

_cons

N
R-sq
Mean VIF
White's
test
F-test

Hausman
test

BreshPagan test
Sargan
test

4.15

4.6

Ho: homoskedasticity
chi2(102) = 134.04
Prob > chi2 = 0.0183

Ho: homoskedasticity
chi2(102) = 134.52
Prob > chi2 = 0.0171
F test that all u_i=0:
F(24, 119) = 4.47
Prob > F = 0.0000

Ho: difference in coefficients not systematic
chi2(12) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 80.75
Prob > chi2 = 0.0000
Test: Var(u) = 0
chibar2(01) = 6.57
Prob > chibar2 = 0.0052
H0: overidentifying restrictions are valid
chi2(57) = 12.071
Prob > chi2 = 1.0000

F test that all u_i=0:
F(24, 119) = 4.27
Prob > F = 0.0000
Ho: difference in coefficients not systematic
chi2(12) = (b-B)'[(V_b-V_B)^(-1)](b-B) =
22.03
Prob > chi2 = 0.0372

Test: Var(u) = 0
chibar2(01) = 7.5
Prob > chibar2 = 0.0031
H0: overidentifying restrictions are valid
chi2(57) = 15.141
Prob > chi2 = 1.0000

5.8
Ho: homoskedasticity
chi2(102) = 121.85
Prob > chi2 = 0.0877
F test that all u_i=0:
F(24, 119) = 2.86
Prob > F = 0.0001
Ho: difference in coefficients not systematic
chi2(11) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 211
Prob > chi2 = 0.0000
Test: Var(u) = 0
chibar2(01) = 0.00
Prob > chibar2 = 1.000
H0: overidentifying restrictions are valid
chi2(57) = 11.682
Prob > chi2 = 1.0000

Note: The symbols (***), (**), (*) indicate statistically significant levels of 1%, 5%, 10%

21


Considering both models of factors affecting the liquidity risk, the case study of

South East Asia countries and Vietnam is shown in Table 4.8:
Table 4.8: Factors influencing the liquidity risk, case study of South East Asia and
Vietnam.
Result
Variable

Expected

South East Asia

Vietnam

FGAP

NLTA

NLST

FGAP

(+)

(+)

(+)

(+)

(-)

(-)

lag (liquidityt-1 )

(+)

SIZE

(-)

SIZE ^2

(+/-)

(+)

(+)

(+)

LIA

(-)

(-)

(-)

(-)

LLR

(-)

LADS

(-)

ETA

(+)

(+)

(+)

(+)

(+)

LLP

(+)

(-)

(-)

(-)

(-)

NIM

(+)

(+)

(+)

(+)

(+)

GDP

(+)

M2

(+)

(-)

INF

(+)

(-)

(-)

D_CRIS

(+/-)

(+)

(+)

NLTA

NLST

(-)

(+)

(-)
(-)

(-)

(-)

(-)
(+)

(+)

(+)

(+)

(-)

(-)

(-)

(-)

(-)

(-)

Source: summary from research results of the author

In summary, considering both models of factors affecting the liquidity risk, the case
study of South East Asian countries and Vietnam, the regression results in the Southeast
Asian case study model show that the majority of explanatory variables in the regression
models is significantly higher than in the case of Vietnam.
In particular, the main explanatory variables related to lag liquidity risk, bank size,
asset quality, equity to total assets, credit risk, net interest income, GDP growth, money
supply, inflation are statistically significant in many models. This is a remarkable result in
empirical studies. By using different methods of measurement and estimation, the study
gives some clear results on the expected correlation. In the case of Vietnam, the study found
22

no statistically significant evidence of the impact of bank size, GDP growth and financial
crisis on liquidity risk.

4.2 Impact of liquidity risk on Performance Efficiency
4.2.1 Descriptive statistics
Table 4.9: Descriptive statistics, case studies of South East Asia countries in the impact
model of liquidity risk on bank Performance Efficiency
Table 4.10: Descriptive statistics, case studies of Vietnam in the impact model of liquidity
risk on bank Performance Efficiency

4.2.2 Analysis of correlation coefficient
Table 4.11: Correlation between the independent variables in the impact model of
liquidity risk on bank Performance Efficiency, case studies of South East Asia
countries.
Table 4.12: Correlation between the independent variables in the impact model of liquidity
risk on bank Performance Efficiency, case studies of Vietnam.
4.2.3 Analyze and discuss, case studies of South East Asia countries.
The study has used 12 different estimation models with three ratios (ROA,
ROE,NIM), which each model was determined by OLS, REM, FEM, SGMM to assess the
impact of liquidity risk to performance of banks. Table 4.13 reports the empirical results of
bank liquidity risk and performance model using FGAP (Bank’s loans – customer deposits/
total assets), NLTAit (Loans/total Assets), NLSTit (Loans/deposits+Short term liabilities) to
measure liquidity risk.
Table 4.13. Results in the impact model of liquidity risk on bank Performance
Efficiency, case studies of South East Asia countries.

23


Bảng 4.13. Results in the impact of liquidity risk on Performance Efficiency, case of South East Asia countries (Appendix)

The model: Pt = f(α, Pt-1, LIQUIDITY RISK it, CONTROLit, u)
Dependent variables: P (NIM, ROA, ROE) measure to bank’s Performance efficiency. Independent variables : Pt-1 – lag of bank’s Performance; LIQUIDITYRISK - (FGAP- financing gap to total assets,
NLTA- Loans /total Assets, NLST- Loans/deposits+Short term liabilities), CONTROL VARIABLÉ (SIZE- natural logarithm of total assets; SIZE^2- natural logarithm of total assets squared; LIA- the ratio
liquid assets to total assets; LLR- the ratio liquid assets / total Loans, LADS- the ratio liquid assets / short term liabilities; ETA- the ratio of equity to total assets.; LLP- the ratio of loan loss provision to
loans, GDP- Annual percent change of GDP, INF- Annual percent change of inflation, d_cris - Dummy variable ).
Database from 2004 to 2016. Estimation technique: OSL, FEM, REM và SGMM.
Model

OLS

Variable
L.roa

FEM

REM

SGMM

OLS

FEM

ROA

REM

SGMM

0.433***

0.101***

0.433***

0.114***

[20.45]

[4.26]

[20.40]

[15.13]

nlta

lia

llr

lads

size

size2

eta

llp

REM

SGMM

NIM

0.115***

0.0169

0.115***

0.0394***

[5.14]

[0.72]

[5.14]

[49.70]

L.nim

nlst

FEM

ROE

L.roe

fgap

OLS

0.836***

0.546***

0.806***

0.668***

[80.34]

[27.93]

[69.44]

[32.38]

3.391***

1.187

3.394***

1.392***

20.75

-6.217

20.75

-4.768

0.108

0.438

0.27

0.185

[4.27]

[1.46]

[4.28]

[5.48]

[1.37]

[-0.35]

[1.37]

[-1.10]

[0.21]

[0.80]

[0.52]

[0.68]

0.00129

-0.00161

0.00129

0.000841***

-0.00797

-0.000242

-0.00797

0.0230***

-0.000863*

-0.00196**

-0.00113**

0.00106***

[1.61]

[-1.19]

[1.61]

[-3.39]

[-0.52]

[-0.01]

[-0.52]

[-4.08]

[-1.65]

[-2.14]

[-1.97]

[-4.21]

-0.0299***

-0.0027

-0.0299***

-0.000409***

-0.0891

0.0836

-0.0891

0.0860*

0.0101*

0.0281***

0.0127**

0.0299***

[-3.45]

[-0.27]

[-3.46]

[-0.11]

[-0.54]

[0.38]

[-0.54]

[-1.72]

[1.79]

[4.15]

[2.17]

[8.54]

0.0484***

0.0931***

0.0485***

0.0977***

0.508*

-0.135

0.508*

0.510***

0.00864

0.0129

0.00796

0.0114***

[3.38]

[4.78]

[3.39]

[8.47]

[1.84]

[-0.32]

[1.84]

[-7.35]

[0.92]

[0.99]

[0.80]

[-1.01]

-0.0000909***

-0.000147***

-0.0000909***

-0.000138***

-0.00140**

-0.001

-0.00140**

-0.000291**

0.00000555

-1.62E-05

6.26E-07

-0.0000102**

[-2.83]

[-4.18]

[-2.83]

[-9.27]

[-2.25]

[-1.29]

[-2.25]

[-2.43]

[0.26]

[-0.69]

[0.03]

[1.29]

0.00351*

0.00557**

0.00352*

-0.00657***

0.0742*

0.0505

0.0742*

0.00984

0.00148

0.00145

0.00151

-0.00112

[1.74]

[2.29]

[1.74]

[11.92]

[1.90]

[0.94]

[1.90]

[0.77]

[1.11]

[0.89]

[1.07]

[-1.34]

0.308***

-0.619***

0.309***

0.0696***

4.620***

2.874

4.620***

5.214***

-0.0487

-0.0281

-0.0292

0.312***

[5.11]

[-2.76]

[5.11]

[-0.73]

[4.00]

[0.58]

[4.00]

[8.27]

[-1.24]

[-0.19]

[-0.67]

[2.92]

-0.0304***

0.0391**

-0.0304***

-0.0177***

-0.409***

-0.285

-0.409***

-0.471***

0.0130***

0.00804

0.0110***

-0.0167**

[-4.96]

[2.24]

[-4.97]

[-0.24]

[-3.50]

[-0.74]

[-3.50]

[-9.55]

[3.28]

[0.69]

[2.61]

[-2.02]

0.0254***

0.0229*

0.0254***

0.00425***

-0.137

0.443*

-0.137

1.041***

0.0111*

0.0253***

0.0148**

0.0589***

[2.78]

[1.96]

[2.78]

[-0.65]

[-0.79]

[1.72]

[-0.79]

[22.19]

[1.87]

[3.23]

[2.39]

[9.68]

0.00305***

0.00526***

0.00306***

0.00648***

0.0452**

0.0356

0.0452**

0.00423***

-0.000352

0.00122

3.49E-06

0.0000953

24