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;



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..

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

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



(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

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


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

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

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

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



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)



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 /

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


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

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

(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

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% ( chi2 = 0.0000

BreshPagan test
Sargan test


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


OLS

Expected

SGMM

FGAP

NLTA

(+)
(-)

(+)

(+)
(+)

(+/-)

(-)

(-)

LIA

(-)

(-)


(+)

(+)

LLP

(+)

(-)

(-)

(-)

(-)

(-)

(-)

NIM

(+)

(+)

(+)

(+)

NLTA

NLST

(+)
(-)

(+)
(-)

(+)
(-)

(+)

(+)

(+)

(-)

(-)

(-)
(+)

(-)

(+)
(-)

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***


L.nlst

size

size2

lia

llr

lads

eta

llp

nim

gdp

0.556***

0.281***

0.556***

0.109

[12.31]


[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***


[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**


0.000710***

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]


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]


[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***


[-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


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]


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]

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


[-0.42]

[-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***


[1.68]

157

157

157

130

157

157

157

130

157

157

157

130

0.913

0.804


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

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

(+)

(+)

(+)

(+)

(-)


LLR

(-)

LADS

(-)

ETA

(+)

(+)

(+)

(+)

(+)

LLP

(+)

(-)

(-)

(-)


(-)

D_CRIS

(+/-)

(+)

(+)

NLTA

NLST

(-)

(+)

(-)
(-)

(-)

(-)

(-)
(+)

(+)


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)


0.101***

0.433***

0.114***

[20.45]

[4.26]

[20.40]

[15.13]

nlta

lia

llr

lads

size

size2

eta

llp


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***


[-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**


-0.0027

-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]


0.510***

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]


[-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***


[0.77]

[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


0.0391**

-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]


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]