Factors Affecting Liquidity Risk Of Commercial Banks In Vietnam.pdf

2018 in the research work “Does financing structure affect bank liquidity risk?” used data of 27 conventional banks and 17 Islamic banks in Malaysia from individual bank financial statem

INTRODUCTION

Introduction

The banking industry plays a key role in national economies, strongly influencing the global financial system (Weisbrod and Rojas-Suárez, 1995) Therefore, risks arising during banks business operations are the primary concern of countries, with liquidity risks are considered to be prioritized to observe, prevent, and overcome promptly Because liquidity represents a bank's ability to immediately meet cash demands, a bank with good liquidity will be able to use available capital at a reasonable cost at the right time At the same time, they can avoid the risks of sudden increases in capital mobilization costs and even default when we cannot meet the cash requirement, threatening the stability of the entire banking system

In world financial history, we experienced the big financial crisis in the United Statesin the period 2007 - 2008, also known as the subprime mortgage crisis,which is regarded one of the historic crises causing economic recession and serious impact on the entire world economy The main cause of the crisis is alleged to be investment banks in the US providing mortgage loans to people individuals who couldn't afford them When loans mature, bad debts accumulate, causing financial and real estate bubbles bursting At that time, real estate prices plummeted, a series of banks and financial institutions collapsed continuously, and the unemployment rate skyrocketed to 10% The peak was on September 15 th , 2008, when Lehman Brothers Holdings filed for bankruptcy after 158 years of operation Starting in the United States, the crisis rapidly extended to other nations, resulting in a worldwide crisis Global trade nearly collapsed, falling 15% from 2008 to 2009 (Rodini, 2023) By 2010 the total number of jobs lost was 30 million Vietnam is also a country affected by the financial crisis Although the financial system has not yet been affected, import-export production and business, investment capital attraction, remittances., etc have been significantly affected (Nguyen Van Tao, 2012) These severe losses are the consequences of banks' lack of liquidity Therefore, liquidity plays an extremely important role, demonstrating the prestige and position of the Bank in particular as well as the safety of the whole banking system in general (Dang Van Dan, 2015); and liquidity risk is considered as a top management priority

In Vietnam, since The Great Crisis, the State Bank has paid more attention to liquidity issues, issued many innovative policies and achieved certain achievements However, some cases of liquidity risks causing serious impacts on the banking system still occur (Dang Van Dan, 2015) In December 2009, the liquidity of commercial banks showed signs of stress when the ratio of liquid capital/deposits mobilized from the economy decreased in all groups of commercial banks compared to the end of

2008 while the capital mobilization balance of commercial banks from the interbank market increased by 65.8% compared to the end of 2008 In the period from October

2010 to January 2011, the credit/capital mobilization ratio of the entire credit institution system increased significantly from 98.6% in October 2010 to 100.07% in October November 2010 Because credit growth was faster than capital mobilization growth continuously within 6 months since October 2010, it caused this liquidity tension (Do Hoai Linh and Lai Thi Thanh Loan, 2018) Banking is a chain system, so maintaining stability in the banking system is a vital task to ensure the safety of the entire economy

Therefore, domestic and international research projects have addressed this topic Mugenyah (2015) in the research article "Determinants of liquidity risk of commercial banks in Kenya" relied on statistical results from data from Central Banks in Kenya to conclude that capital adequacy ratio, liquid asset ratio, ownership type, size and leverage were significant determinants of liquidity risk From there, the study recommended that bank managers can effectively manage liquidity risk by focusing on those factors to make reasonable proposals to minimize liquidity risk In Vietnam, Truong Quang Thong (2014) in the study "Factors Affecting Liquidity Risk in the

System of Vietnamese Commercial Banks" based on the annual reports of 27 commercial banks concluded that the internal factors, such as total asset size, liquidity reserve, inter-bank loan, and ratio of equity to capital, and the external ones, such as growth rate, inflation, and effects of policy lags had impact on liquidity risk Through that, the study also proposed some recommendations to enhance the efficacy of liquidity risk management at commercial banks in Vietnam Risk management is a core function of banks (Waemustafa and Sukri, 2016) while the economy is still changing in complexity every day

Based on the inheritance of previous research and the current situation of business activities of commercial banks in Vietnam, the author chose the topic

"Factors affecting liquidity risks of commercial banks in Vietnam" as a research topic for thesis The study evaluates the factors and their influence on the liquidity risk of commercial banks in the period 2012 - 2022 Based on updated and current results, the article provides recommendations on improving the efficiency of liquidity risk management at banks in Vietnam.

Research objectives

The purpose of the study is to determine the factors and measure, evaluate their level and direction of impact on liquidity risk of commercial banks in Vietnam, thereby proposing recommendations to minimize the commercial banks' liquidity risk and effectively enhance the liquidity management

− Determine factors affecting liquidity risk of commercial banks in Vietnam

− Measure and evaluate the level and direction of impact of factors on liquidity risk of commercial banks in Vietnam

− Propose recommendations to minimize liquidity risk and enhance liquidity management of commercial banks in Vietnam.

Research questions

− What are the factors affecting the liquidity risk of commercial banks in Vietnam?

− What is the level of impact of factors on liquidity risk of commercial banks in Vietnam?

− What are recommendations that can be proposed from the research results to minimize liquidity risks and improve operational efficiency of commercial banks in Vietnam?

Research subjects and scope

− Research subjects: The factors affecting liquidity risk of commercial banks in Vietnam

About research space: The study is based on data from the financial statements of 29 commercial banks in Vietnam with the criteria that banks operate throughout the research process, along with data published transparently on each bank's financial statements

About research time: Data was collected from 2012 to 2022 This is a period of high research value because the domestic economy has gone through many processes of crisis and self-recovery as the result of (1) the complex fluctuations of the real estate crisis in the period 2012 - 2013 and

2018 – 2020; (2) the stock market plummeted to the bottom in 2018 after just reaching a historic peak in 2017; (3) and the global economic crisis due to the outbreak of the Covid-19 pandemic from 2022 to present These economic events have caused a significant impact on the whole Vietnamese economy, particularly the banking industry.

Research Methodology

The study uses both quantitative and qualitative research methods to identify the influencing factors and the extent of their impact on liquidity risk using secondary data collected from 29 commercial banks in Vietnam during the period 2012 – 2022

Qualitative research method: This approach is used to explore and analyze the theoretical foundations related to liquidity and liquidity risk, reviewing previous studies both domestically and internationally This helps establish a basis for building the research model, discussing the results, and proposing solutions

Quantitative research method: including descriptive statistical methods, correlation analysis, and panel data regression techniques, using OLS, FEM, REM models with Stata 17.0 software to determine trends and the level of influence of factors on the liquidity risk of Vietnamese commercial banks Subsequently, model selection tests and model deficiencies testing are performed To overcome model deficiencies, the study utilizes the feasible generalized least squares (FGLS) regression method and checks for endogeneity within the model, addressing them using the Generalized Method of Moments (GMM) approach.

Contribution of the study

Theoretical contribution: The study inherits and adjusts the research model based on previous empirical studies; Thereby contributing to building more solidity in the research foundation and presenting limitations as well as research directions for future research

Practical contribution: The research results provide empirical evidence to help commercial banks in Vietnam identify the current situation of liquidity risk; thereby being able to implement measures to limit risks and ensure safety and efficiency during business operations.

Research Content

To identify, analyze and evaluate the factors and their level of impact on the liquidity risk of 29 commercial banks in Vietnam in the period of 2012 - 2022

Thereby, propose recommendations to improve the operational efficiency of banks The report is divided into five parts:

This chapter introduces and highlights the urgency of the topic; thereby determining research objectives, research questions, research methods, research scope, previous studies, and the significance of the topic's contribution

Chapter 2: Overview of theoretical framework and experimental research

Theories related to the liquidity risk of commercial banks are mentioned in this chapter; simultaneously, the author mentioned the dependent variables of the research model based on previous studies

Chapter 3: Research methods and models

This chapter presents the research method of the topic as well as the data collection process, data processing and the research model chosen by the author

Chapter 4:Research results and discussion

In this chapter, the author presents the results obtained by analyzing the data and running the models using STATA 17.0 software; simultaneously inspects the defects of model Draw conclusions and address the topic from there

Chapter 5: Conclusion and Policy Implications

This chapter synthesizes all research issues and results From there, the author will present limitations as well as directions for future research; at the same time, propose some recommendations to improve the operational efficiency of commercial banks in Vietnam

Chapter 1 provides an overview of the liquidity situation and liquidity risk at commercial banks globally, as well as in Vietnam specifically, laying the groundwork for the fundamental issues addressed in the thesis The key points covered in this chapter include the research objectives, research subjects, research methods, and the structure of the thesis Through this, Chapter 2 will delve deeper into providing an overview of the theoretical framework and empirical research of the topic.

OVERVIEW OF THEORETICAL FRAMEWORK AND

Overview of liquidity risk

Commercial banks management needs to always be focused and continuously improved to prevent risks during operations including interst rate risk, credit risk, liquidity risk, and capital risk (Peter & Sylvia, 2008) Puspitasari et al (2021) also announced in their research that there are four main risks that can threaten the sustainable and stable operation of the banking system, namely market risk, credit risk, liquidity risk, and operational risk According to Business dictionary (2012), risk is defined as "a probability or threat of a damage, injury, liability, loss, or other" In general, risks must be understood as potential unforeseen consequences that bring positive or negative impacts to the recipient (Cao, 2013) This report refers to bank liquidity risk, through which commercial banks fall into a state of insolvency, bankruptcy or are declared bankrupt by competent authorities (Altman, 1968; Nguyen

2.1.2 The concept of liquidity and liquidity suppliers – demands

The concept of liquidity in economic literature pertains to the capacity of an economic participant to exchange their current wealth for goods, services, or alternative asset (Nikolaou, 2009)

The definition of funding liquidity by the Basel Committee on Banking Supervision emphasizes a bank's capacity to fulfill its obligations, unwind, or settle positions as they mature (BIS, 2008) Likewise, the International Monetary Fund

(IMF) offers a parallel definition for funding liquidity, focusing on the ability of financially sound institutions to make timely payments as agreed upon

According to Peter (2001), "Bank liquidity is the ability of a bank to obtain available capital at low cost at the time the bank needs it." Duttweiler (2011) defines liquidity as the capability to fulfill all impending payment obligations, the simplicity of converting an asset into cash, and the market's willingness to accept it In the context of commercial banks, liquidity referred to the capacity to effectively deploy available funds for various business activities, including deposit payments, lending, transactions, and capital transactions (BIS, 2009)

Peter and Sylvia (2008) determined that liquidity is the availability of cash at a time of need at a reasonable cost The size and volatility of cash demand affects a bank's liquidity position

In general, based on previous research, liquidity is the ability to convert assets into cash to promptly meet cash demands at a reasonable cost, serving the bank's different needs

According to Peter and Sylvia (2008), Liquidity supplies are the bank's sources to meet liquidity demand including:

• Revenues from the Sale of Nondeposit Services

• Borrowings from the Money Market

Liquidity demands are the need to pay for the bank's committed financial obligations, and created by the following main factors:

• Credit Requests from Quality Loan Customers

According to Peter (2001), the difference between liquidity supply and demand at a given point in time is expressed through the Net Liquidity Position, calculated by the formula:

From there, there are 3 states that can occur when determining the Net Liquidity Position:

• L = 0 indicates a balanced liquidity state, which is difficult to achieve in reality

• L > 0 indicates that total supply exceeds total liquidity demand (liquidity surplus), so banks need to consider investment strategies to generate income from this surplus, such as: purchasing secondary government securities, interbank lending, etc

• L < 0 indicates that total supply is less than total liquidity demand (liquidity deficit) In this case, banks need to consider increasing supplementary liquidity supply, such as: selling secondary reserves, overnight interbank borrowing, discounting refinancing from the SBV, etc

2.1.3.1 The concept of liquidity risk

According to the "Principles for the Management and Supervision of Liquidity Risk" by Basel (2008), liquidity risk is the risk that a financial institution may not be able to obtain sufficient funding to meet its maturing obligations without adversely affecting its day-to-day operations and without causing a negative impact on its financial condition

Duttweiler (2009) contends that liquidity risk is the risk that arises when a financial institution is not capable to make payments at a certain point in time, or has to mobilize funds at a high cost to meet payment obligations, or due to other reasons that compromise the institution's payment capability This may lead to adverse consequences for the financial institution

Liquidity risk can be categorized into two forms, namely market liquidity risk and financial liquidity risk (Decker, 2000; Gomes & Khan, 2011; Pham, 2019) Market liquidity risk pertains to the potential failure of a bank to swiftly and cost- effectively sell assets in the market On the other hand, financing liquidity risk involves the risk that a bank may be unable to fulfill its debt obligations when they mature due to the inability to liquidate assets or a lack of funding These two risk types frequently interact with each other, exhibiting a contagion effect within financial markets and institutions (Diamond & Rajan, 2005)

According to Vodová (2013), liquidity risk encompasses two types of risks: capital liquidity risk and market liquidity risk Capital liquidity risk is the risk that a bank may not efficiently meet the present and future cash flow needs and disbursement requirements without affecting the financial conditions of the company Market liquidity risk is the risk that a bank may find it challenging to offset or eliminate at market prices

Liquidity risk is a situation where a bank is unable to meet all the demands of depositors, either entirely or partially, within a specified period (Jenkinson, 2008) Liquidity risk can also be interpreted as the bank's incapacity to fulfill short-term financial needs It not only impacts the operational efficiency of the bank but also affects the bank's reputation

Many studies have relatively consistently pointed out that liquidity risk can arise from both asset and liability sides or from off-balance sheet activities of the asset balance sheet of commercial banks (Valla et al., 2006) Furthermore, Nguyen Van Tien (2010) identified three underlying reasons that banks must confront liquidity risk:

The first reason: Banks mobilize and borrow with short-term maturity while continuing to lend with longer maturities As a result, many banks face the risk of mismatched maturities between their assets and liabilities In practice, banks often have a substantial amount of loans that need to be repaid immediately if depositors demand, such as on-demand deposits and prematurely withdrawable fixed-term deposits Therefore, banks must always be prepared for liquidity

The second reason: Sensitivity of financial assets to interest rate changes Depositors tend to deposit where the interest rate is higher when interest rates rise, and borrowers may repay or fully withdraw credit lines with lower contracted interest rates Thus, changes in interest rates affect both deposit and lending cash flows and ultimately impact the liquidity of the bank Additionally, interest rate changes will affect the borrowing cost in the bank's money market

The third reason: Banks must always meet liquidity demands perfectly Liquidity disruptions can erode public trust in the bank Imagine what would happen to a bank if its cash desks or ATMs closed temporarily due to a lack of cash, inability to settle incoming checks, or failure to meet maturing deposits One crucial task for bank managers is to maintain close contact with customers holding large cash balances and those with large unused credit lines to understand their plans for when and how much they intend to withdraw, ensuring a reasonable liquidity plan

2.1.3.3 Impact of liquidity risk on socio-economic activities and on the operations of commercial banks

For the national financial system and the system of commercial banks

The commercial banking system plays an extremely vital role in the financial market Throughout its operations, commercial banks are constantly exposed to various inherent risks, with the most significant ones being credit risk and liquidity risk The occurrence of any type of risk results in certain losses for the banks, causing an increase in operational costs and a reduction in bank profitability In severe cases, banks may face losses leading to bankruptcy This would result in shareholders losing their investments, and depositors losing their savings The trust of depositors, stability, and payment capability of the entire banking system are diminished The bank's reputation in the market is eroded, leading to increased withdrawal of deposits by the public Simultaneously, the bank cannot attract new deposits as people lose confidence in it The escalating liquidity shortage will eventually lead the bank to lose liquidity, potentially resulting in bankruptcy, adversely affecting the financial situation of other banks, triggering a chain reaction, and disrupting the overall stability of the national financial market

For the economic and social system

Overview of previous studies

Bunda and Desquilbet (2008) in the research article "The bank liquidity smile across exchange rate regimes" also used data from 36 countries in the period from

1995 to 2004 to evaluate the liquidity risk of banks The results show that bank size (SIZE), the ratio of equity to asset (ETA), lending rates (LR), the government spending to GDP and the inflation rate (INF) are positively correlated with liquidity risk The results also show that the financial crisis had a positive impact on liquidity risk in case of fixed exchange rates mechanisms and negative impact in case of floating exchange rate mechanism

Vodová (2011, 2013) used data from Czech, Hungary and Finland commercial banks in the period from 2011 to 2009 to determine the determinants of liquidity of Czech, Hungary and Finland banks Results from regression analysis showed that there was a positive correlation between bank liquidity and equity ratio (ETA), the interest rate on loans (IRL) in three nations; share of non-performing loans (NPL) and interest rates on interbank transaction (IRI) in Czech and Finland, whereas inflation rate (INF), business cycle, economic growth rate (GDP) and financial crisis were negatively correlated with liquidity in Czech Republic but positively in Finland

Ganic (2014) in the report "An Empirical Study on Liquidity Risk and its Determinants in Bosnia and Herzegovina" examined the liquidity risk of 17 commercial banks in Bosnia and Herzegovina (B&H) based on data from reports financial in the period 2002 - 2012 The study analyzed data by using multiple regression model to test for statistical significance and explanatory power, as well as data analysis techniques including correlation, R-squared, ANOVA, and the F-test The results concluded that Return on Equity ratio (ROE), Reserve Ratio (RR) had significant negative correlation with liquidity risk, whereas Loan Loss Reserves ratio, Growth rate of gross domestic product growth (GDP) were positively correlated with liquidity risk

Mugenyah (2015) in the research article "Determinants of liquidity risk of commercial banks in Kenya" used secondary data obtained from the website of the Central Bank of Kenya, the websites of the respective banks and the multiple regression model to evaluate the determinants of liquidity risk The regression results indicated that capital adequacy (CAR) had a positive impact on liquidity risk while the ratio of liquid asset, ownership type, size and leverage had negative impact The study concluded that capital adequacy ratio, size, ownership type, liquid assets ratio and leverage were significant determinants of liquidity risk From there, the study recommends that bank managers can effectively manage liquidity risk by focusing generally capital adequacy ratio, size, ownership type, liquid assets ratio and leverage to make reasonable strategies to minimize liquidity risk

Chowdhury et al (2016) in the research article "Relationship between Liquidity Risk and Net Interest Margin of Conventional Banks in Bangladesh" used data from annual reports of conventional banks in Bangladesh over the period 2011 - 2015 to determine the impact of liquidity risk on the Net Interest Margin (NIM) variable of conventional banks The study used descriptive statistics, correlation, and regression model to present results Thereby, the research concluded that liquidity risk had a considerable impact on the NIM of the selected banks NIM correlated positively with liquidity risk ratios

Alzoubi (2017) in the study "Determinants of liquidity risk in Islamic banks" identified factors affecting liquidity risk in Islamic banks based on data of 42 Islamic banks from 15 countries in the period 2007 - 2014 The results show that cash ratio, securities held by bank, bank size (SIZE), total equity to total assets ratio (ETA) have negative correlation with liquidity risk Because equity is a more reliable source of funding for banks, and a larger equity ratio reduces liquidity risk On the other hand, high profit assets and bad financial decisions are positively correlated with liquidity risk

Abdul-Rahman et al (2018) in the research work “Does financing structure affect bank liquidity risk?” used data of 27 conventional banks and 17 Islamic banks in Malaysia from individual bank financial statement data from the Bureau Van Dijk Bankscope database, publicly available audited reports and the websites of Global Market Data Index (GMDI) for the period from 1994 to 2014 to evaluate how determinants of the financing structure affected liquidity risk The study used the unbalanced panel regression method with two models and tested the models using Hausman test, Likihood ratio Test, and F-Statistics to select the model that better reflects the results Research results showed that bank profitability (ROA) had a positive correlation with the entire banking system, Capital Adequacy Ratio (CAR) had a negative correlation with liquidity risk for conventional banks, and Inflation Rate (INF) had a negative correlation for Islamic banks

Truong Quang Thong (2013) in the research article "Factors Affecting Liquidity Risk in the System of Vietnamese Commercial Banks" identified factors affecting the liquidity risk of commercial banks in Vietnam in the period 2002 - 2011 Based on data from audited and published financial reports of 27 commercial banks; and data on macro variables from the IMF The study used Descriptive Statistics, Regression Analysis including fixed effects model (FEM) and random effects model (REM), and Durbin-Watson statistics, Hausman test to conclude that the ratio of equity to total assets (ETA), ratio of bank loans and other loans to total asset (LTA) is positively correlated with liquidity risk, whereas ratio of liquidity reserve to total asset (LRA) is negatively correlated with liquidity risk Higher GDP growth in the present year reduces liquidity risk, but increases it in the following year Inflation rate fluctuations do not impact liquidity risk in the present year, but do lessen it in subsequent years

Dang Van Dan (2015) in the research article " Các nhân tố ảnh hưởng đến rủi ro thanh khoản của các Ngân hàng thương mại tại Việt Nam" was based on data collected from the annual financial reports of 15 large commercial banks in Vietnam over the period 2007 - 2014 combined with macroeconomic data collected from the General Statistics Office of Vietnam (GSOV) to evaluate factors affecting the liquidity risk of commercial banks The study used panel data regression analysis with three models such as Pooled OLS, Fixed Effects Model (FEM), Random Effects Model (REM) and Haussman test to conclude that banks size (SIZE) had negative correlation and Total loans to total assets ratio (TLA) had a positive correlation with liquidity risk

The research article "Các yếu tố ảnh hưởng đến thanh khoản của các ngân hàng thương mại Việt Nam" by Vu Thi Hong (2015) used data from 37 commercial banks in Vietnam during the period 2006 - 2011 Through Statistical analysis, correlation and regression of panel data were not proportional to the Fixed Effect effect, the study found the impact of a number of factors on the liquidity of commercial banks in Vietnam The results showed that “equity to total capital” (CAP), “Non-performing loan ratio” (NPL) and “Profit ratio – Return on Equity” (ROE) had a positive correlation; On the contrary, "Loan-to-deposit ratio" (LDR) had a negative correlation with the liquidity of Vietnamese commercial banks

Nguyen Thi Bich Thuan and Pham Anh Tuyet (2021) in the research article "Nhân tố ảnh hưởng đến rủi ro thanh khoản tại các ngân hàng thương mại Việt Nam" used panel data collected from legitimate financial reports Audited results of 25 Vietnamese commercial banks in the period 2013 - 2019 and Hausman and Breusch and Pagan Lagrangian multiplier tests to select the model that best explains the factors affecting liquidity risk The study used regression with panel data with Pooled Ordinary Least Square (Pooled OLS), Fixed Effects Model (FEM), Random Effects Model (REM) Results showed that Dependence on external funding (EFD) was positively correlated with liquidity risk The remaining factors include: (1) Banks size (SIZE); (2) Ratio of equity capital to total asset (ETA); and (3) Loan to total deposit ratio (LTD) and liquidity reserve ratio (LRA) had negative correlation with liquidity risk

Phan Thi My Hanh and Tong Lam Vy (2021) in the article "Các yếu tố ảnh hưởng đến rủi ro thanh khoản của hệ thống ngân hàng thương mại Việt Nam" analyzed and evaluated the impact of factors on liquidity risk of 21 commercial banks in Vietnam in the period 2008 - 2017 by using financing gap (FGAP) to measure liquidity risk The study used Pooled, FEM and REM models to analyze the data Research results showed that the larger the bank's size (SIZE), the lower its liquidity risk, whereas the higher the ratio of equity to total capital (CAP), loan to total assets ratio (LTA), and return on equity ratio (ROE); the more the liquidity risk will increase The study also showed that dependence on external funding sources increases banks' liquidity risk Besides, macroeconomic factors such as economic growth rate (GDPG) and financial crisis have a positive impact on liquidity risk

Nguyen Hoang Chung (2022) in the research article "Factors Affecting Liquidity Risks of Joint Stock Commercial Banks in Vietnam" used data of 26 commercial banks listed on the Ho Chi Minh City and Hanoi Stock Exchanges during the period from 2008 to 2018 to evaluate factors affecting banks' liquidity risks The research applied the Pool OLS, FEM, REM, FGLS, D&K, GMM – SGMM and LM models by R programming language and the Bootstrap technique to estimate the impact of micro and macro factors on liquidity risk The results showed that customer deposit to total assets ratio (DTA) was negatively correlated with liquidity risk, whereas the loan to asset ratio (LTA), commercial bank liquidity, credit development ratio, the ratio of external funding ratio and the ratio of loan loss provision (LLP) all had positive impact on liquidity risk

Studies Authors Objective Data Methods Conclusions Foreign research

The bank liquidity smile across

Evaluate the liquidity risk of banks

From 36 countries in the period

SIZE, ETA, GDP, INF variables have positive exchange rate regimes from 1995 to

Determine the determinants of liquidity of Czech, Hungary and Finland banks

From Czech, Hungary and Finland commercial banks in the period from

Panel data regression analysis, Fixed effects regression

ETA, IRL NPL have positive correlation with liquidity risk, whereas INF, GDP and financial crisis were negatively correlated with liquidity risk

Examined the liquidity risk of commercial banks in Bosnia and Herzegovina

ROE, RR have negative correlation with liquidity risk, whereas LLR, GDP have positive correlation with liquidity risk

Determinants of liquidity risk of commercial banks in

Evaluate the determinants of liquidity risk

From the website of the Central Bank of Kenya

Multiple linear regression, Cook- Weisberg test, Jarque- Bera test, F-test

CAR has a positive correlation with liquidity risk, whereas SIZE, ownership type, liquid assets ratio and leverage have negative correlation with liquidity risk

Determine the impact of liquidity risk on the Net Interest Margin (NIM) variable of conventional banks

From annual reports of conventional banks in Bangladesh over the period 2011 -

Descriptive statistics, correlation, and regression model

NIM correlated positively with liquidity risk ratios

Determinants of liquidity risk in

Identified factors affecting liquidity risk in Islamic banks

From 42 Islamic banks of 15 countries in the period

SIZE, ETA have negative correlation with liquidity risk, whereas high profit assets, bad financial decisions have positive correlation with liquidity risk

Identified factors affecting the liquidity risk of commercial banks in Vietnam data from audited and published financial reports of 27 commercial banks in the period 2002 -

Descriptive Statistics, Regression Analysis including fixed effects model (FEM) and random effects model (REM), and Durbin- Watson statistics,

ETA, LTA have positive correlation with liquidity risk, whereas LRA has a negative correlation with liquidity risk Higher GDP growth in the present year reduces liquidity risk, but increases it in the following

Hausman test year Inflation rate fluctuations do not impact liquidity risk in the present year, but do lessen it in subsequent years

Các nhân tố ảnh hưởng đến rủi ro thanh khoản của các Ngân hàng thương mại tại Việt

Evaluate factors affecting the liquidity risk of commercial banks

From the annual financial reports of 15 large commercial banks in Vietnam over the period 2007 -

SIZE had negative correlation with liquidity risk, whereas TLA had a positive correlation with liquidity risk

Các yếu tố ảnh hưởng đến thanh khoản của các ngân hàng thương mại Việt

Evaluate the impact of a number of factors on the liquidity of commercial banks in Vietnam

From 37 commercial banks in Vietnam during the period 2006 -

Statistical analysis, correlation and regression of panel data

ROE had positive correlation with liquidity risk, whereas LDR had a negative correlation with the liquidity risk

Nhân tố ảnh hưởng đến rủi ro thanh khoản tại các ngân hàng thương mại

Nguyen Thi Bich Thuan and Pham Anh Tuyet (2021)

Identified factors affecting the liquidity risk of commercial banks in Vietnam

From audited results of 25 Vietnamese commercial banks in the period 2013 -

Regression with panel data and Hausman and Breusch and Pagan Lagrangian

EFD was positively correlated with liquidity risk, whereas SIZE, ETA, LTD, LRA ) had negative correlation multiplier tests with liquidity risk

Các yếu tố ảnh hưởng đến rủi ro thanh khoản của hệ thống ngân hàng thương mại

My Hanh and Tong Lam Vy (2021)

Analyzed and evaluated the impact of factors on liquidity risk of 21 commercial banks in Vietnam data from audited and published financial reports of 21 commercial banks in the period 2008 -

Panel data regression analysis with three models such as Pooled OLS, FEM, REM

SIZE has a negative correlation with liquidity risk, whereas ETA, LTA, ROE, GDP have positive correlation with liquidity risk

Evaluate factors affecting banks' liquidity risks used data of

26 commercial banks listed on the Ho Chi Minh City and Hanoi Stock Exchanges during the period from

2008 to 2018 the Pool OLS, FEM, REM, FGLS, D&K,

SGMM and LM models by

R programmi ng language and the Bootstrap technique

Customer deposit to total assets ratio was negatively correlated with liquidity risk, whereas LTA, credit development ratio, the ratio of external funding ratio, and LLP all had positive impact on liquidity risk

(Source: Compiled by the author)

Overview of research variables

Based on the underlying theoretical framework and previous studies, the variables in the model are defined and measured as follows:

The Liquidity Risk of bank (i) at time (t) can be measured by two methods: the financing gap (or liquidity gap) or liquidity ratios According to the study by Vodova (2013), the liquidity gap is the difference between assets and capital at the current and future points in time, while liquidity ratios are various coefficients calculated from the balance sheet Saunders and Cornett (2006) proposed using the term “financing gap” to measure liquidity risk Dang Van Dan (2015) stated that the financing gap method is the most suitable approach in quantitative analysis The financing gap index fundamentally reflects the liquidity capability of the bank, where the funding gap is the difference between the average balance of loans and the average balance of funding sources In the research by Truong Quang Thong (2013), liquidity risk is measured by the difference between credit and deposit divided by total assets This forms the basis for the author to use the financing gap as the dependent variable in the proposed research model

LDR (The loans to deposit ratio)

The liquid assets ratio (LDR), the L4 index assessing the liquidity of commercial banks, was introduced in Chapter 2 LDR is the ratio used to measure the proportion between illiquid assets such as loans and highly liquid capital sources such as deposits and short-term capital sources This ratio reflects how many times the loan amount is greater than the mobilized amount Therefore, the higher this ratio, the higher the liquidity ratio will be

The bank's scale is calculated by taking the natural logarithm of the total assets of the bank to measure its scale This helps identify the relationship between the scale of total assets and liquidity risk According to economic theory, larger banks, or those with significant total assets, are less likely to face liquidity risk because they can rely on interbank markets or receive liquidity support from ultimate lenders (Aspachs et al., 2005) However, in reality, with the support of the state, large-scale commercial banks may focus on expanding credit activities or investing in portfolios with higher risks to increase profitability, leading to an increase in liquidity risk for these banks

Net Interest Margin is the percentage difference between interest income and interest expenses of a bank It is one of the indicators used to measure the efficiency and profitability of a bank in generating net interest income from loans and investments The higher the NIM ratio, the better the bank’s profitability

ROE (The return on equity ratio)

The Return On Equity ratio (ROE) is measured by dividing the after-tax profit of the bank by the average equity The return on equity ratio indicates the efficiency of each bank in utilizing its equity capital ROE indicates the level of profit generated by a company's equity A positive ROE signifies the bank's profitability, while a negative value indicates that the bank is operating at a loss

LTA (The loans to total assets ratio)

Lending is a common activity among banks in Vietnam, with a focus on utilizing capital for lending operations Since loans typically have low liquidity, increasing the volume of loans will raise the proportion of less liquid assets The higher this ratio signifies that a bank is heavily reliant on loans, resulting in lower liquidity Conversely, a low ratio suggests a high default rate for the bank

ETA (The equity to total assets)

The equity to total assets (ETA) is measured by dividing equity by total assets, indicating the capital structure of banks and their ability to self-finance with their capital A greater ETA indicates a higher proportion of the bank's assets being self- owned, or conversely, it establishes the bank's degree of leverage A lower ratio suggests that the bank heavily relies on financial leverage in its business activities, posing potential liquidity risks that could negatively impact profitability and liquidity However, if the cost of borrowing is reasonable, efficient business operations can increase profitability and liquidity, offsetting these potential negative effects on business activities

LLP (The loan loss provisions ratio)

The loan loss provisions ratio (LLP) is calculated as the percentage ratio between the value of loan loss provisions and the total loans outstanding A bank with a high loan loss provisions ratio is holding assets with high liquidity, meaning it is prepared to address potential losses that may occur when customers fail to meet their borrowing obligations This reduces liquidity risk for the bank The higher the ratio, the more extensive the loan provisions, resulting in a reduction in net income and earnings per share

GDP (The gross domestic product)

The rapid economic growth is a crucial macroeconomic factor that significantly influences almost all industries in society, particularly in the financial and banking sector A robust economic growth will lead to an increase in household income, making it easier for banks to mobilize capital Therefore, the economic growth factor will have a positive impact on the liquidity of banks, reducing potential liquidity risks that banks may encounter

Inflation is the increase in the general price level of goods and services over time and the erosion of the value of currency, and it is measured by the Consumer Price Index (CPI) A rise in inflation can impact activities across the economy, including those within commercial banks Inflation will lead to a reduction in the reserve levels of liquidity for banks, affecting customers' business activities, resulting in difficulties in meeting timely debt payments to the bank, ultimately leading to a loss of liquidity for the bank

In Chapter 2, liquidity risk in banks can be measured by two methods: (i) the group of 4 liquidity ratios and the liquidity gap Additionally, the study lists factors influencing liquidity risk, including factors from commercial banks and macroeconomic factors based on previous empirical studies reviewed to establish the research framework for the subsequent chapters Based on Chapter 2, the research methodology and research model of the thesis will be developed in Chapter 3.

RESEARCH METHODS AND MODELS

Research Methods

To identify the direction and level of the impact of factors on liquidity risk in

29 Vietnamese commercial banks in the period between 2012 and 2022, the research process is depicted in the following diagram:

(Source: Complied by the author)

Step 1: Identify the research problem

Step 2: Review the theoretical framework and relevant previous studies in Vietnam and other nations, then discuss these studies to identify research gaps and determine the direction for designing the research model for the topic

Step 3: Design the anticipated research model based on theoretical foundations and empirical evidence, plan regression equations, explain variables, and formulate research hypothesis

Step 4: Identify a suitable research sample aligned with the research objectives to collect and process data according to the research model

Step 5: Determine the research method using specific analytical and estimation techniques: descriptive statistics, correlation analysis, and regression analysis using OLS, FEM, and REM Test research hypothesis using F-test or t-test with significance levels of 1%, 5%, or 10% to identify statistically significant independent variables explaining the dependent variable Additionally, compare the Pooled OLS and FEM models using an F-test with the hypothesis H0: Choose the Pooled OLS model; use the Hausman test to compare the FEM and REM models with the hypothesis H0: Choose the REM model, or use the Breusch-Pagan Lagrangian test to choose between the Pooled OLS and REM models with the hypothesis H0: Choose the Pooled OLS model, thus selecting the most suitable model Also, test research hypothesis

Step 6: Examine model flaws, including multicollinearity, autocorrelation, and changing error variance If these flaws are absent, proceed to Step 8 and 9

Step 7: If these flaws are present, address them by estimating the model using the FGLS method in cases of autocorrelation and/or changing error variance in the model

Step 8: Present and discuss research results

Step 9: Based on regression results, conclude the research, provide recommendations, and suggest policy implications to answer research questions and achieve the research objectives

The research is based on the basis of secondary data acquired from audited and publicized financial statements which are on the website of the State Bank of Vietnam (SBV), World Bank (WB), General Statistics Office of Vietnam (GSOV), and journals related to the research topic during the period 2012 - 2022 of 29 Vietnamese commercial banks

Qualitative Research: Involves approaching and analyzing theoretical foundations related to liquidity and liquidity risk, reviewing previous studies both domestically and internationally, designing the research model, explaining the variables in the model, formulating research hypotheses, discussing the results, and proposing policy implications

Quantitative research: The report uses descriptive statistics, correlation analysis, and panel data regression methods, along with OLS, FEM, REM models by Stata 17.0 to determine the determine the trends and the levels of influence of factors affecting the liquidity of Vietnamese commercial banks, thereby selecting the most optimal model based on model results through model tests such as F – test, Hausman test, Breusch-Pagan Lagrangian test At the same time, the study also tested the model's defects such as using Variance Inflation Factors (VIF) index for Multicollinearity, Wooldrige Test for Autocorrelation, Wald Test (for FEM Model) or

LM – Breusch and Pagan (for REM Model) for Heteroskedasticity The report also checks the model calibration using the feasible generalized least squares (FGLS) method, and checks for endogeneity issues within the model, addressing them using the Generalized Method of Moments (GMM) approach

Tests to choose the appropriate model

Used to examine the suitability between two models, Pooled OLS and FEM, with the following hypotheses:

H0: The Pooled-OLS model is more suitable for the research variables H1: The FEM model is more suitable for the research variables

If the P-value is less than 0.05, at a significance level of 5%, we reject the hypothesis H0, concluding that there is a significant difference between the entities, meaning that using the FEM model is more appropriate than the OLS model

In the case where the Pooled OLS model is more appropriate than FEM, the Lagrange Multiplier test (Breusch & Pagan, 1980) is used to select the appropriate regression model between Pooled OLS and REM, with the following hypotheses:

H0: The Pooled-OLS model is more suitable for the research variables H1: The REM model is more suitable for the research variables

If the P-value is less than 0.05, at a significance level of 5%, we reject the null hypothesis H0, concluding that the REM model is more suitable than the Pooled OLS model

In the case where the FEM model is more appropriate than Pooled OLS, according to Baltagi (2008) or Gujarati (2004), the Hausman test is used to choose the appropriate regression model between FEM and REM, with the following hypotheses:

H0: The REM model is more suitable for the research variables

H1: The FEM model is more suitable for the research variables

If the P-value is less than 0.05, at a significance level of 5%, we reject the hypothesis H0, concluding that the FEM model is the appropriate method Conversely, if the P-value is greater than 0.05 at a significance level of 5%, we accept H0 and use the REM model

Tests for checking models' defects

The thesis performs multicollinearity tests between an independent variable and the remaining independent variables using the Variance Inflation Factor (VIF) When a variable has a large VIF (VIF > 10), indicating the potential for multicollinearity, the author addresses this by eliminating the variable (Gujarati, 2004)

When the model exhibits autocorrelation, it causes the R-squared estimate to be too high compared to reality, leading to biased estimates that affect the regression model The Wooldridge test is used to check for autocorrelation with the following hypotheses:

If the Prob value is < 5%, we reject the null hypothesis H0, accepting H1, concluding that the model exhibits autocorrelation (Wooldridge, 2002)

Heteroskedasticity occurs when the variance of the error term is not constant, affecting the estimation results The Wald test is used for FEM or the LM-Breusch and Pagan test for REM with the following hypotheses:

If Prob < 5%, we reject the null hypothesis H0, accepting H1, concluding that the model experiences heteroskedasticity (Greene, 2000)

Tests to fix the model:

When the test results yield a P-value more than 0.05, we reject H0, concluding that the model does not exhibit heteroskedasticity, and vice versa However, after checking for violations in the selected model, if the chosen model shows heteroskedasticity or autocorrelation, or both violations, the Generalized Least Squares (FGLS) estimation method can be used to address these issues (Greene, 2012) FGLS estimation has the following characteristics: (i) it addresses the problem of changing variance of the error term for unbiased and efficient estimation results; (ii) FGLS estimates differ from OLS in the original model, but the explanation of coefficients relies on the original variables; (iii) In time-series data analysis, FGLS estimation may still encounter autocorrelation issues If there is no heteroskedasticity, the results from FEM and REM models can be used for analysis and discussion

However, the model needs to be tested for Endogeneity The study utilizes Durbin- Wu-Hausman tests (Durbin, 1954; Wu, 1973; Hausman, 1978) to identify endogenous variables in the model with the hypotheses as follows:

Research Models and Hypothesis

Based on the overview of the results of the presented studies and the purpose of expanding the scope of analysis to achieve more objective results, the author proposes the following two experimental research models on factors affecting liquidity risk on commercial banks in Vietnam from 2012 to 2022:

Model 1: LDR = 𝜷 𝟎 + 𝜷 𝟏 SIZE + 𝜷 𝟐 NIM + 𝜷 𝟑 ROE + 𝜷 𝟒 LTA + 𝜷 𝟓 ETA + 𝜷 𝟔 LLP + 𝜷 𝟕 GDP + 𝜷 𝟖 INF + 𝜺 (𝒊,𝒕)

Model 2: FINGAP = 𝜷 𝟎 + 𝜷 𝟏 SIZE + 𝜷 𝟐 NIM + 𝜷 𝟑 ROE + 𝜷 𝟒 LTA + 𝜷 𝟓 ETA +

LDR: The loan to deposit ratio of commercial bank (i) at time (t)

FINGAP: The financing gap of commercial bank (i) at time (t)

SIZE: The size of commercial banks (i) at time (t)

NIM: Net Interest Margin of commercial bank (i) at time (t)

ROE: The return on equity ratio of commercial bank (i) at time (t)

LTA: The loans to total assets ratio of commercial bank (i) at time (t)

ETA: The equity to total assets of commercial bank (i) at time (t)

LLP: The loan loss provisions ratio of commercial bank (i) at time (t)

GDP: The gross domestic product of Vietnam at time (t)

INF: The inflation rate of of Vietnam at time (t)

𝜺 (𝒊,𝒕) : The random error term; 𝜷 𝟎 : the intercept coefficient; 𝜷 𝟏 - 𝜷 𝟖 : the slope coefficient of the independent variables

SIZE: Previous studies have different results; in the research conducted by

Aspachs et al (2005), Bonfim & Kim (2011), and Truong Quang Thong (2013), the bank's size has a positive correlation with liquidity risk Whereas, Vodava (2013), Delachat et al (2014), Dang Van Dan (2015), Mugenyah (2015), Alzoubi (2017), Nguyen Thi Bich Thuan & Pham Anh Tuyet (2021), Phan Thi My Hanh & Tong Lam

Vy (2021) indicates an inverse relationship between the bank's size and liquidity risk The author expects the bank's size to have a negative correlation with the bank's liquidity risk

Hypothesis H1: Bank size has a negative impact on liquidity risk

NIM: Marozva (2015), Abdulhakim (2019) reached the conclusion that there is an negative correlation between NIM and liquidity risk, while Chowdhury et al

(2016) suggested the notion of a positive relationship between liquidity risk and NIM Therefore, we hypothesize that there is a positive correlation between NIM and liquidity risk

Hypothesis H2:NIM has a positive impact on liquidity risk

ROE: Several studies hightlights the consistent impact of the return on equity

(ROE) on liquidity risk, such as Aspachs et al (2005), Lucchetta (2007), Arif & Anees (2012), Vodava (2013), Ganic (2014), Vu Thi Hong (2015), Phan Thi My Hanh & Tong Lam Vy (2019) Therefore, the author anticipates a positive correlation between ROE and liquidity risk

Hypothesis H3: ROE has a positive impact on liquidity risk

ETA: According to Bunda & Desquilbet (2008), Tobias (2011), Vodová

(2011), Ganic (2014), Vo Xuan Vinh & Mai Xuan Duc (2017), Huong et al (2021), there was a positive influence of ETA on liquidity risk Therefore, we formulate the hypothesis that ETA positively affects liquidity risk

Hypothesis H4: ETA has a positive impact on liquidity risk

LTA: Truong Quang Thong (2013), Dang Van Dan (2015), Phan Thi My Hanh

& Tong Lam Vy (2019), Nguyen Hoang Chung (2022) show a positive relationship between The loan to total assets ratio (LTA) and liquidity risk Therefore, the author expects that LTA has a positive correlation with liquidity risk

Hypothesis H5: LTA has a positive impact on liquidity risk

LLP: Trương Quang Thông (2013), Vodava (2013), Ganic (2014), Moussa

(2015), Rashid et al (2017), Phan Thi My Hanh & Tong Lam Vy (2021), Nguyen (2022), Tran et al (2022) show results regarding the relationship between the loan loss provision ratio and liquidity risk Therefore, the author expects that LLP has a positive correlation with liquidity risk

Hypothesis H6: LLP has a positive impact on liquidity risk

GDP: Research by Truong Quang Thong (2013), Vodava (2013), Ganic (2014), Moussa (2015), Phan Thi My Hanh & Tong Lam Vy (2021) indicate that economic growth has a positive impact on liquidity risk On the other hand, in some studies such as Shen et al (2009), Vodava (2013), Moussa (2015), economic growth is associated with increased consumption, expanded production scale, and reduced liquidity, leading to larger liquidity risks Therefore, the author expects GDP to be positively correlated with liquidity risk

Hypothesis H7: GDP has a positive impact on liquidity risk

INF: Vodová (2011), Abdul-Rahman et al (2018), El-Chaarani (2019), Ahamed (2021) suggest that inflation (INF) might lead to an negative impact on liquidity risk In contrast, Singh & Sharma (2016) state that INF has a positive influence on liquidity risk Therefore, the author expects that INF has a negative correlation with liquidity risk

Hypothesis H8: INF has a negative impact on liquidity risk

Table 3 1 Statistics of expected affect and previous studies of variables in the models Variables Variable measurement Exp

Aspachs et al.(2005), Rychtárik (2009), Vodova (2011)

Truong Quang Thong (2013), Dang Van Dan (2015), Phan Thi My Hanh & Tong Lam Vy (2021)

Mugenyah (2015), Dang Van Dan (2015), Alzoubi (2017), Nguyen Thi Bich Thuan & Pham Anh Tuyet (2021), Phan Thi

Marozva (2015), Chowdhury et al (2016), Abdulhakim (2019)

& Anees (2012), Ganic (2014), Vu Thi Hong (2015), Phan Thi My Hanh & Tong Lam Vy (2021)

Truong Quang Thong (2013), Dang Van Dan (2015), Phan Thi My Hanh & Tong Lam Vy (2021), Nguyen (2022)

Bunda & Desquilbet (2008), Tobias (2011), Vodová (2011), Vo Xuan Vinh & Mai Xuan Duc (2017), Huong et al (2021)

Ganic (2014), Rashid et al (2017), Nguyen (2022), Tran et al (2022)

GDP Data are taken from Vietnam's economic growth index +

Trương Quang Thông (2013), Vodava (2013), Ganic (2014), Moussa

(2015), Phan Thi My Hanh & Tong Lam Vy (2021)

INF Data are taken from Vietnam's economic growth index -

& Sharma (2016), Abdul-Rahman et al (2018), El-Chaarani (2019), Ahamed (2021)

(Source: Complied by the author)

Chapter 3 provides a detailed overview of the research methods, research models, and hypotheses regarding the factors influencing liquidity risk in 29 commercial banks in Vietnam during the period 2012–2022, based on theoretical foundations and previous empirical studies in Vietnam and worldwide as discussed in Chapter 2 The research then conducts data analysis by using STATA 17.0 software through testing and regression methods, so the results will be presented in Chapter 4.

RESEARCH RESULTS AND DISCUSSION

Descriptive statistical analysis

To obtain a general overview of the characteristics of variables in the research model, including the total number of observations, mean values, standard deviation, minimum values, and maximum values, the study uses descriptive statistical methods implemented by the SUM command in the STATA 17.0 software The analysis results are presented in the table below:

Table 4 1 Descriptive Statistics of Variables in Model 1

Variable Obs Mean Std Dev Min Max

(Source: Analysis results from STATA software)

The results in Table 4.1 indicate that the variables in the model have strongly balanced data, with 319 observations from 29 commercial banks over the period from

2012 to 2022 The descriptive statistics of variables in Model 1 are analyzed as follows:

The Liquidity Risk (expressed through the LDR coefficient) is measured by Total Loans / Total Deposits with a mean value of 71.2%, and a standard deviation of 16% The minimum value is 13.57%, attributed to Lien Viet Post Joint Stock Commercial Bank (LPB) in 2018 The maximum value is 127.56%, belonging to

Joint Stock Commercial Bank for Investment and Development of Vietnam (BIDV) in 2017 When comparing observations with the average value, the variation in the LDR ratio among banks is relatively high, mostly dependent on the individual business capacity of each bank The average value of 71.2% indicates that lending and deposit mobilization are core activities of the entire banking system in the market

The bank size variable (SIZE) is measured by calculating the logarithm of Total Assets with an average value of 8.1691, a standard deviation of 0.52, a minimum value of 7.12 (BaoVietBank in 2012), and a maximum value of 9.33 (BIDV in 2022) The standard deviation of 0.52 shows a significant difference in the scale of banks BIDV is the bank with the largest total assets volume in the commercial bank group, reaching 2,120,609,384 VND in 2022 In contrast, BaoVietBank is the bank with the smallest total assets, reaching 13,280,324,9 VND in 2012 In 2022, BIDV, Vietnam Bank for Agriculture and Rural Development (Agribank), Joint Stock Commercial Bank For Foreign Trade Of Vietnam (Vietcombank) are the three banks with the largest total assets and growth rate in the group of 29 commercial banks in Vietnam

Net Interest Margin (NIM) is measured by Net Interest Income/Average Total Earning Assets with an average value of 3.54%, a standard deviation of 4.90% The minimum value is 0.18% (Viet Capital Commercial Joint Stock Bank (BanVietBank) in 2015), and the maximum value is 73.92% (Saigon Bank For Industry And Trade (Saigon Bank) in 2022) Despite the pressure from the State Bank's policies to reduce lending interest rates in 2022, NIM is still considered very positive for several reasons: (i) declining asset quality leading to unstable interest income; (ii) higher mobilization costs due to compliance with the liquidity targets of the banking system, and (iii) Increasing inflation pressure and less optimistic income prospects affecting indirect deposit rates (casa)

Return on Equity (ROE) is measured by Return/Total Equity with an average value of 11.74%, a standard deviation of 20.24% The minimum value is 0.028% (National Citizen Commercial Joint Stock Bank (NCB) in 2020), and the maximum value is 247.93% (Vietnam - Asia Commercial Joint Stock Bank (Viet A Bank) in 2022) The significant standard deviation indicates a considerable difference in the business efficiency among banks, mainly due to individual business capabilities and operational contexts The average profit rate of the banking sector in 2022 increased compared to the previous year, making it one of the industries with high ROE ratios in both the real estate market and other sectors

Loans to Total Assets (LTA) is measured by Loans to Customers/Total Assets with an average value of 57.91%, a standard deviation of 11.36% The minimum value is 21.62% (Southeast Asia Commercial Joint Stock Bank (SeABank) in 2012), and the maximum value is 78.81% (BIDV in 2020) The results show a significant difference in the business capacity of banks BIDV, despite being the bank with the largest total assets in the commercial bank group, maintains a high LTA ratio, indicating the bank's good ability to mobilize capital from customers The average LTA ratio of 57.91% indicates that lending and deposit mobilization are still traditional business activities for the bank group

Equity to Total Assets (ETA) is measured by Equity/Total Assets with an average value of 8.99%, a standard deviation of 5.88% The minimum value is 2.62% (Saigon Commercial Joint Stock Bank (SCB) in 2020), and the maximum value is 90.77% (Vietcombank in 2021) The results show that SCB, due to a serious disruption in the board of directors leading to a significant decline in its own capital, recorded a low ETA ratio in 2020 Vietcombank still holds the leading position with both high own capital and total assets, maintaining the efficiency of the bank's leverage and business operations Banks still need to make efforts to maintain stable risk levels and profitability

Loan Loss Provision (LLP) is measured by Loan Loss Provision/Total Loans with an average value of 0.79%, a standard deviation of 0.36% The minimum value is 0.00038% (Vietnam Technological and Commercial Joint Stock Bank (TCB) in 2022), and the maximum value is 2.56% (Agribank in 2012) The average value of the credit risk provision for the entire industry is relatively low, with fluctuations around a low average value However, the differences between bank groups over the years are still relatively significant

Gross Domestic Product (GDP) has an average value of 6.0018%, a standard deviation of 1.68% The minimum value is 2.6% in 2021, and the maximum value is 8.02% in 2022 The average value shows that Vietnam has a relatively stable growth rate from 2012 to 2022 Especially, despite the record-breaking GDP decrease due to the severe impact of the Covid-19 pandemic, Vietnam is one of the rare countries to maintain positive economic growth, with GDP increasing significantly to 8.02% in

2022, demonstrating the country's remarkable recovery efforts

Inflation rate (INF) has an average value of 3.74%, a standard deviation of 2.197% The minimum value is 0.63% in 2015, and the maximum value is 9.09% in

2012 The results show that despite continuous fluctuations, the inflation rate is always considered a concern that needs to be quickly and prominently addressed by the government

Table 4 2 Descriptive Statistics of Variables in Model 2

Variable Obs Mean Std Dev Min Max

The Liquidity Risk (expressed through the FINGAP coefficient) is measured by (Credit Balance-Total Deposit)/Total Assets with an average value of -13.85%, a standard deviation of 9.85% The minimum value is -42.07% belonging to MSB in

2014 The maximum value is 9.16% for Vietnam Prosperity Joint Stock Commercial Bank (VPB) in 2022 When comparing observations with the average value, the differences in FINGAP values among banks are relatively significant due to fluctuations in their capabilities and individual business policies in the market over the years.

Correlation analysis

The study examines the relationship between the dependent variable (LDR) and the following research variables: SIZE, NIM, ROE, LTA, ETA, LLP, GDP, INF The correlation analysis findings are shown in the table below:

Var LDR SIZE NIM ROE LTA ETA LLP GDP INF

Table 4 3 Correlation matrix among the explanatory variables in Model

Table 4.3 illustrates the relationships between the dependent variable (LDR) and the independent variables The SIZE, NIM, ROE, LTA, ETA, LLP variables have positive effects with LDR Conversely, the GDP, INF variables have negative impacts on LDR Additionally, the correlation coefficient between NIM and ROE is as high as 0.9 > 0.8, but all other correlation coefficients are much lower than 0.8, indicating negligible multicollinearity in Model 1 Among the independent variables, SIZE, NIM, ROE, LTA, LLP; LTA exhibit the strongest positive correlations with the dependent variable LDR, with coefficients of 0.877, followed by LLP, SIZE, ROE, NIM with correlation coefficients of 0.503, 0.419, 0.176, 0.151, respectively On the other hand, INF is the variable with the strongest negative correlation with LDR, with a coefficient of -0.143

Correlation matrix among the explanatory variables in Model 2

Var FINGAP SIZE NIM ROE LTA ETA LLP GDP INF FINGAP 1.000

Table 4.4 depicts the relationships between the dependent variable (FINGAP) and the independent variables The SIZE, NIM, ROE, LTA, ETA, LLP, GDP variables have positive effects with FINGAP Conversely, the INF variable have a negative impact on FINGAP Additionally, the correlation coefficient between NIM and ROE is as high as 0.9 > 0.8, but all other correlation coefficients are much lower than 0.8, indicating negligible multicollinearity in Model 2 Among the independent variables, LTA exhibits the strongest positive correlation with the dependent variable FINGAP, with a coefficient of 0.561, followed by LLP, ROE, NIM, ETA with correlation coefficients of 0.180, 0.167, 0.150, 0.142, respectively.

Estimating the regression models and testing the regression

The study conducted regression analyses on the collected data using three estimation methods: Pooled OLS, Random Effects Model (REM), and Fixed Effects Model (FEM) to determine the influence of independent variables on the dependent variable through the estimated coefficients The regression results are summarized by the author in Tables 4.4, 4.5 as follows:

Table 4 5 Summarize research model included OLS-FEM-REM for model 1

Table 4 6 Summarize research model included OLS-FEM-REM for model 2

Comparison of the fit between the Fixed Effects Model (FEM)

To assess the fit between the Fixed Effects Model (FEM) and the Random Effects Model (REM), the author conducted a Hausman test with the following hypotheses:

Hypothesis (H0): There is no correlation between the independent variables and the residuals, meaning the REM model is appropriate

Hypothesis (H1): There is a correlation between the independent variables and the residuals, meaning the FEM model is appropriate

Table 4 7 Hausman test for model 1

Test: Ho: difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 20.66

The results indicate that the Prob>chi2 value is 0.0081, which is less than 0.05 Therefore, we accept the hypothesis H1 and reject the hypothesis H0 The FEM model is more suitable for the study

Table 4 8 Hausman test for model 2

Test: Ho: difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 17.93

The results indicate that the Prob>chi2 value is 0.0218, which is less than 0.05 Therefore, we accept the hypothesis H1 and reject hypothesis H0 The FEM model is more suitable for the study

The study proceeds to select the fixed effects model (FEM) for comparison once again with the Pooled OLS model to choose the main research models for the study and discuss the results.

Comparison of the fit between the Fixed Effects Model (FEM)

After selecting the appropriate model between the REM and FEM models, the author will now choose the suitable model between the Pooled OLS and FEM models The author applies the F-test to assess the model selection with the hypothesis as follows

H0: POOLED-OLS is the suitable method

H1: FEM is the suitable method

The results from the F-test indicate that Prob > F = 0.0000 < 0.05, so we reject H0 and accept H1 The fixed-effects FEM model is more suitable for the study

The results from the F-test indicate that Prob > F = 0.0000 < 0.05, so we reject H0 and accept H1 The fixed-effects FEM model is the suitable model.

Checking the models’ defects

The study uses the VIF index to test for multicollinearity in the model The Variance Inflation Factor (VIF) indicates severe multicollinearity if a variable is highly correlated with other variables If the VIF of a variable is greater than 10, the variable is considered to have strong correlation Therefore, a higher VIF coefficient indicates a higher level of multicollinearity

Table 4.11 Research of multicollinearity test Variable VIF 1/ VIF

The results indicate that all VIF coefficients are less than 10, suggesting that the models do not exhibit multicollinearity

The study uses the Wooldridge test to examine the presence of autocorrelation in the models, with the following hypotheses:

Table 4 12 Wooldridge test result for model 1

Wooldridge test for autocorrelation in panel data

The result indicates Prob>F = 0.1469 > 0.05, thus rejecting H1 and accepting H0 Model 1 does not have autocorrelation

Table 4 13 Wooldridge test result for model 2

The result indicates Prob>F = 0.0000 < 0.05, thus rejecting H0 and accepting H1 Model 2 has autocorrelation

The study tests the Heteroskedasticity of the models with the following hypotheses:

Table 4 14 Modified Wald test result for model 1

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma (i)^2 = sigma^2 for all i chi2 (29) 3085.74

The results show that the Prob>chi2 value is 0.0000 < 0.05, so we reject H0 and accept H1 The model exhibits heteroscedasticity in the variance of the errors

Table 4 15 Modified Wald test result for model 2

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma (i)^2 = sigma^2 for all i chi2 (29) 341.40

The results show that the Prob>chi2 value is 0.0000 < 0.05, so we reject H0 and accept H1 The model exhibits heteroscedasticity in the variance of the errors.

Remedying the research models

4.5.1 Generalized Least Squares (FGLS) method

MODEL 1: LDR = 𝜷 𝟎 + 𝜷 𝟏 SIZE + 𝜷 𝟐 NIM + 𝜷 𝟑 ROE + 𝜷 𝟒 LTA + 𝜷 𝟓 ETA +

Through analyses and tests, REM is the most suitable model; however, the model has heteroscedasticity Therefore, to address this drawback, the study uses the Generalized Least Squares (FGLS) model

Table 4.16 Summarize research model included OLS-FEM-REM-FGLS for model 1

The results from the FGLS method indicate statistically significant factors in the research model, including SIZE, LTA, ETA, and INF On the other hand, NIM, ROE, LLP, GDP do not have statistical significance in the model Among these, SIZE, LTA, ETA, INF all have positive impacts on LDR FGLS overcomes the heteroskedasticity phenomenon, with a significance level of 5% (Prob>Chi2 0.0000) The model exploring the factors influencing the liquidity risk of Vietnamese commercial banks from 2012 to 2022 is presented as follows:

LDR = -0.346 + 0.0387 SIZE + 1.156 LTA + 0.475 ETA + 0.00500 INF + ε_(i,t)

MODEL 2: FINGAP = 𝜷 𝟎 + 𝜷 𝟏 SIZE + 𝜷 𝟐 NIM + 𝜷 𝟑 ROE + 𝜷 𝟒 LTA + 𝜷 𝟓 ETA +

Table 4.17 Summarize research model included OLS-FEM-REM-FGLS for model 2

The results from the FGLS method indicate statistically significant factors in the research model, including LTA, GDP and INF On the other hand, the remaining factors do not have statistical significance in the model Among these, LTA, GDP and INF have positive impacts on FINGAP FGLS overcomes autocorrelation and heteroskedasticity phenomena, with a significance level of 5% (Prob>Chi2 = 0.0000) The model exploring the factors influencing the liquidity risk of Vietnamese commercial banks from 2012 to 2022 is presented as follows:

Although the FGLS model can address the issues of changing error variances and autocorrelation, the models may still suffer from biased estimates if endogeneity exists (Nguyen, 2022) If the model includes lagged variables, then endogeneity is likely to be present Therefore, the study conducts endogeneity tests in the model

MODEL 1: LDR = 𝜷 𝟎 + 𝜷 𝟏 SIZE + 𝜷 𝟐 NIM + 𝜷 𝟑 ROE + 𝜷 𝟒 LTA + 𝜷 𝟓 ETA +

The study tests endogeneity on the independent variables in Model 1 using the Durbin-Wu-Hausman test, and the results are presented in the following table:

Table 4 18 Results of determining endogenous variables in model 1

The results from Table 4.16 indicate the presence of endogeneity in the model

To address this issue, the study uses the Generalized Method of Moments (GMM) regression method with endogenous variables including NIM, ROE, and ETA

Table 4.19 Summarize model included OLS-FEM-REM-FGLS-GMM for model 1

OLS FEM REM FGLS GMM

Arellano-Bond test for AR(2) in first differeances: z = -1.48 Pr > z = 0.138 Sargan test of overid restrictions: chi 2(8) = 3.80 Prob > chi2 = 0.874

(Not robust, but not weakend by many instruments.)

Hansen test of overid restrictions: chi 2(8) = 2.88 Prob > chi2 = 0.941 (Not robust, but not weakend by many instruments.)

The GMM regression results from Table 4.18 show that the number of instrumental variables does not exceed the number of research groups (18 < 29); the second-serial correlation test, Arellano-Bond AR(2), has a P-value of 0.138 > 0.1, indicating no statistically significant serial correlation, so the model does not exhibit serial correlation; the Hansen and Sargan tests have P-values of 0.941 and 0.874, respectively, both greater than 0.1, demonstrating the suitability of instrumental variables Additionally, the Hansen’s Test P-value exceeding 0.25 indicates the robustness and effectiveness of the GMM regression results (Roodman, 2009)

Furthermore, the results from the Generalized Method of Moments (GMM) regression method show that the significant factors in the research model include SIZE, NIM, ROE, and LTA Conversely, ETA, LLP, GDP, and INF do not exhibit statistical significance in the model Specifically, SIZE, ROE, LTA have positive impacts on LDR, while NIM has a negative effect on liquidity risk (LDR) The model describing the factors influencing liquidity risk in Vietnamese commercial banks from 2012 to 2022 is presented as follows:

LDR = -0.962 + 0.138 SIZE – 1.489 NIM + 0.363 ROE + 0.999 LTA + 𝜀 𝑖,𝑡

MODEL 2: FINGAP = 𝜷 𝟎 + 𝜷 𝟏 SIZE + 𝜷 𝟐 NIM + 𝜷 𝟑 ROE + 𝜷 𝟒 LTA + 𝜷 𝟓 ETA +

The study tests endogeneity on the independent variables in Model 2 using the Durbin-Wu-Hausman test, and the results are presented in the following table:

Table 4 21 Results of determining endogenous variables in model 2

The results from Table 4.19 indicate the presence of endogeneity in the model

To address this issue, the study uses the Generalized Method of Moments (GMM) regression method with endogenous variables including SIZE, NIM, ROE, and ETA

Table 4 22 Summarize model included OLS-FEM-REM-FGLS-GMM for model 2

OLS FEM REM FGLS GMM

Table 4 23 The GMM regression results

Arellano-Bond test for AR(2) in first differeances: z = -0.48 Pr > z = 0.633 Sargan test of overid restrictions: chi 2(16) = 3.31 Prob > chi2 = 1.000 (Not robust, but not weakend by many instruments.)

Hansen test of overid restrictions: chi 2(16) = 9.07 Prob > chi2 = 0.911 (Not robust, but not weakend by many instruments.)

The GMM regression results from Table 4.21 show that the number of instrumental variables does not exceed the number of research groups (26 < 29); the second-serial correlation test, Arellano-Bond AR(2), has a P-value of 0.633 > 0.1, indicating no statistically significant serial correlation, so the model does not exhibit serial correlation; the Hansen and Sargan tests have P-values of 0.911 and 1.000, respectively, both greater than 0.1, demonstrating the suitability of instrumental variables These criterias were satisfied by the GMM regression model, demonstrating its applicability, effectiveness, and high accuracy

Furthermore, the results from the Generalized Method of Moments (GMM) regression method show that the significant factors in the research model 2 are NIM, ROE, ETA, LLP, GDP, INF Conversely, SIZE, LTA do not exhibit statistical significance in the model Specifically, NIM, ETA, LLP, GDP, INF have positive impact on FINGAP, while ROE has a negative effect on liquidity risk (FINGAP) The model describing the factors influencing liquidity risk in Vietnamese commercial banks from 2012 to 2022 is presented as follows:

FINGAP = 0.342 L.FINGAP + 0.652 NIM -0.170 ROE + 1.256 ETA + 3.844 LLP +

Research results discussion

MODEL 1: LDR = -0.962 + 0.138 SIZE – 1.489 NIM + 0.363 ROE + 0.999 LTA

+ 𝜺 𝒊,𝒕 Table 4 24 Summary of influence levels of independent variables in model 1

Affect Affect Significance level SIZE Negative Positive statistically significant at 5% level

NIM Positive Negative statistically significant at 5% level

ROE Positive Positive statistically significant at 5% level

LTA Positive Positive statistically significant at 1% level

ETA Positive Negative statistically insignificant

LLP Positive Positive statistically insignificant

GDP Positive Positive statistically insignificant

INF Negative Positive statistically insignificant

The bank size (SIZE) has a significantly positive correlation with liquidity risk (LDR) at a statistical significance level of 5% The results indicate that, under constant conditions, an increase in bank size by 1 unit leads to a 0.138 unit increase in liquidity risk This implies that as the bank size grows, liquidity risk also increases This finding contradicts the initial expectation of hypothesis H1 In line with prior research by authors such as Aspachs et al (2003), Sauders & Corrnett (2007), Arif & Anees (2012), and Truong Quang Thong (2013), this result is reasonable It can be explained by the need to allocate resources to invest in the expansion of the bank size, leading to a reduction in the reserve amount of the bank's capital Recognizing that larger banks receive special guarantees and support from the government in adverse situations, these banks may use a portion of their reserve of liquid assets to invest in highly liquid and riskier assets with corresponding high profitability This could increase liquidity risk for the bank

The Net Interest Margin (NIM) has a significant negative correlation with liquidity risk (LDR) at a 5% statistical significance level This means that, under constant conditions, an increase in Net Interest Margin by 1 unit results in a decrease of 1.489 units in liquidity risk Therefore, as the Net Interest Margin of the bank decreases, liquidity risk increases This finding contradicts the expectation of hypothesis H2 It aligns with previous research by Abdulhakim (2019) and Marozva (2015) NIM is a coefficient reflecting the rate of revenue growth from interest relative to the rate of cost increase for banks It measures efficiency and profitability, indicating the ability of the banks in generating interest income from loans and maintaining the growth of income sources compared to the increase in costs to cover short - term debts Thus, an increasing NIM demonstrates profitability and business capability, reducing liquidity risk

Return on Equity (ROE) has a positive correlation with liquidity risk, meaning that, under constant conditions, an increase in ROE by 1 unit leads to an increase of 0.363 units in liquidity risk This result is in line with the initial hypothesis H3 and previous studies by Vu Thi Hong (2015), Phan Thi My Hanh & Tong Lam Vy (2021), Ganic (2014), Lucchetta (2007), Arif & Anees (2012) In pursuit of maximizing returns, banks may engage in riskier activities or allocate resources inefficiently, which can reduce their ability to meet short-term financial obligations In addition, larger profits for banks indicate diversified business categories and substantial investment value, so sudden exposure to risks from market fluctuations and uncontrollable incidents could threaten liquidity and increase the likelihood of financial distress, as banks may face threats from liquidity risk Furthermore, investors and stakeholders may view a positive correlation between ROE and liquidity risk as a signal of heightened financial risk This could impact investor confidence, stock valuation, and access to capital markets, as investors may demand higher returns to compensate for increased risk Overall, when ROE exhibits a positive correlation with liquidity risk, it underscores the importance of balanced financial management practices that prioritize both profitability and liquidity Banks need to carefully assess and manage liquidity risk to maintain financial stability and resilience in volatile market conditions

The Loans to Total Assets (LTA) ratio has a positive relationship with liquidity risk In other words, under constant conditions, when the LTA ratio increases by 1 unit, liquidity risk will increase by 0.999 units This result is consistent with studies by Arif & Anees (2012), Vo Xuan Vinh & Mai Xuan Duc (2017), Tobias (2011), Bunda & Desquilbet (2008) It indicates that commercial banks typically focus on using capital for traditional lending activities Loans usually have low liquidity; therefore, large and unforeseen withdrawals can lead to a loss of bank liquidity The LTA index has shown a continuous upward trend from 2012 to 2020, despite slight decreases, and has continued to rise since 2021, indicating concerns as banks tend to focus more on lending without paying attention to asset-liability balance Banks should pay more attention to balancing short-term deposits and loans

Poorman & Blake (2005) argue that relying solely on liquidity ratios to measure bank liquidity may not be sensitive enough and may not be a comprehensive solution Therefore, the ETA, LLP, GDP, INF variables were found to be statistically insignificant To achieve more general research results, the study evaluates the impact of factors on liquidity risk expressed by FINGAP variable in model 2

MODEL 2: FINGAP = 0.342 L.FINGAP + 0.652 NIM -0.170 ROE + 1.256 ETA +

3.844 LLP + 0.00684 GDP + 0.0133 INF + 𝜺 𝒊,𝒕 Table 4 25 Summary of influence levels of independent variables in model 2 Variable

Affect Affect Significance level SIZE Negative Negative statistically insignificant

NIM Positive Positive statistically significant at 1% level

ROE Positive Negative statistically significant at 1% level

LTA Positive Positive statistically insignificant

ETA Positive Positive statistically significant at 1% level

LLP Positive Positive statistically significant at 5% level

GDP Positive Positive statistically significant at 1% level

INF Negative Positive statistically significant at 1% level

The Net Interest Margin (NIM) has a significant positive correlation with liquidity risk (LDR) at a 1% statistical significance level This implies that when NIM increases by 1 unit, the liquidity risk of banks will increase by 0.652 units Therefore, when NIM increases, liquidity risk also increases This result is consistent with Hypothesis 2 of the model and previous studies by Naceur and Kandil (2009),

Chowdhury et al (2016) Net Interest Margin (NIM) is the difference between interest income earned from lending and interest expenses paid on deposits, serving as a metric to assess the profitability and expansion of a bank A high NIM indicates that the bank is earning more profit from lending than from deposit interest payments When banks enhance lending, their long-term assets (such as loans) increase, while short-term cash supply (such as deposits) remains unchanged or decreases This increases liquidity risk because if banks need to pay cash immediately without corresponding funds, they may incur higher costs to attract cash or have to sell long- term assets at lower prices to obtain cash Therefore, although a high NIM may signal increased profitability, it may also come with increased liquidity risk

Return on Equity (ROE) has a negative correlation with liquidity risk, meaning that, under constant conditions, an increase in ROE by 1 unit leads to an decrease of 0.170 units in liquidity risk This is consistent with Hypothesis H3 of the model and previous studies by Kaufman (2004), Louzis et al (2012) Alzoubi (2017) further supported this by demonstrating a negative correlation between liquidity risk and bank equity, with higher equity ratios reducing liquidity risk An increase in ROE is often accompanied by an increase in profitability, thereby enhancing the bank's ability to meet short-term financial obligations When banks can easily meet short-term debts, liquidity risk decreases Additionally, an increase in ROE is typically associated with improved financial performance, meaning that the bank achieves high profits from a relatively low level of equity investment This indicates that the bank is using its capital more efficiently and does not need to rely heavily on short-term funding Effective risk management also helps minimize liquidity-related risks and improves the overall operational performance of the banks

Equity to Total Assets (ETA) has a positive correlation with liquidity risk, meaning that, under constant conditions, an increase in ETA by 1 unit leads to an increase of 1.256 units in liquidity risk This result is consistent with Hypothesis H4 of the model and previous studies by Vodová (2011), Huong et al (2021), Vo Xuan Vinh & Mai Xuan Duc (2017), Tobias (2011), Bunda & Desquilbet (2008) A high

ETA indicates that the bank is using high financial leverage, leading to a sudden increase in capital costs, affecting the bank's business results and reducing liquidity

Loan Loss Provision (LLP) has a positive correlation with liquidity risk (FINGAP), meaning that, under constant conditions, when the LLP ratio increases by

1 unit, liquidity risk increases by 3.844 units This result is in line with studies by Nguyen (2022), Tran et al (2022), Rashid et al (2017), Ganic (2014) Chung-Hua Shen et al (2009) also indicated that As the cost of LLP increased, so did the liquidity risk Loan loss provision is the amount of money that a bank sets aside from its profits to reserve for handling loans that the bank expects to become non-performing When banks allocate more funds for provisions, it can reduce their cash reserves, thereby decreasing their ability for immediate payments and enhancing liquidity risk Moreover, allocating more funds to loan loss provisions can also affect a bank's ability to issue new loans When a portion of a bank's profits is allocated to provisions, it can decrease their capacity to provide new financing, leading to a decrease in liquidity

Gross Domestic Product (GDP) has a significantly positive correlation with liquidity risk (FINGAP) at a 1% statistical significance level This means that, under constant conditions, when GDP increases by 1 unit, the liquidity risk of banks will increase by 0.00684 unit Thus, as GDP increases, the risk for banks also grows This result aligns with the hypothesis H7 of the model and previous research by Ganic (2014), Phan Thi My Hanh & Tong Lam Vy (2021), Vodava (2013), Moussa (2015), Truong Quang Thong (2013) During economic recessions, banks tend to reserve more liquidity, and conversely, in times of economic growth with suitable lending opportunities, banks reduce liquidity reserves to lend more Therefore, the higher the economic growth rate, the greater the liquidity risk

Inflation rate (INF) has a positive correlation with liquidity risk, meaning that, under constant conditions, an increase in INF by 1 unit leads to an decrease of 0.0133 units in liquidity risk This result is consistent with Hypothesis H8 of the model and previous studies by Truong Quang Thong (2013), Vodova (2011) The relationship between inflation and liquidity risk is also influenced by the level of economic development, with higher inflation exacerbating liquidity risk in less developed economies (Ghossoub, 2010) Inflation erodes the purchasing power of money over time This can lead to decreased cash flow and liquidity for banks, as they may need more funds to cover expenses or maintain operations Additionally, when inflation rates are high, the central bank typically takes action to adjust interest rates Higher interest rates can increase the cost of borrowing for banks, making the cost of accessing liquidity through loans or credit lines rise This can constrain liquidity and increase liquidity risk for banks

Regarding the SIZE variable, Vu Thi Hong (2015) in another study said that using the logarithm for total assets led to almost equivalent data between banks Dang Thi Quynh Anh and Tran Le Mai Anh (2022) also believed that due to the decline in GDP and increased inflation rate, the advantage of scale no longer existed

Chapter 4 proceeds to implement descriptive statistical methods for characteristic features and correlations between variables in the two research models based on secondary data collected from financial reports of 29 commercial banks using Stata 17.0 software Subsequently, the study conducts regression analysis using Pooled OLS, FEM, and REM models; tests model defects; and applies FGLS and GMM regression methods to choose the most effective model The results are analyzed and compared with previous studies to draw conclusions for hypotheses and provide directions for policy implications in the following chapter.

CONCLUSION AND POLICY IMPLICATIONS

Conclusion

The research results indicate that, based on data from 29 commercial banks in Vietnam during the period 2012-2022, the liquidity risk of banks may be influenced by factors such as Bank Size, Net Interest Margin, Return on Equity ratio, Loans to

Total Assets ratio, Equity to Total Assets ratio, Loan Loss Provision ratio, GDP growth, and Inflation rate

In Model 1, Bank Size has a positive correlation with liquidity risk (represented by the Loan-to-deposit ratio) This implies that increasing the size of a bank does not necessarily reduce liquidity risk Banks with a large asset base need to focus on investing in high liquidity assets, avoiding cases where they solely concentrate on business investments to increase profits On the other hand, during the process of increasing bank assets, banks may need to use borrowed funds Therefore, banks may face the risk of overdue payments, leading to inefficient business operations In cases of liquidity shortages, holding highly liquid assets or maintaining good liquidity conditions will help banks avoid financial instability Hence, banks need to adhere to policies ensuring safety indices in their business operations, autonomously hold highly liquid assets, and develop a reasonable asset allocation strategy

Net Interest Margin (NIM) has an inverse correlation with liquidity risk (LDR), meaning that as the Net Interest Margin of the bank decreases, liquidity risk increases Since NIM is a measure of efficiency and profit-making capability, it reflects the ability of the board of directors and bank staff to maintain the growth of income compared to the increase in expenses An increasing NIM indicates the profit- making ability and business capacity of the bank, thereby reducing liquidity risk Therefore, banks need to formulate policies to continuously diversify their asset portfolios, improve the credit quality of customers to reduce borrowing costs, increase income from interest rates, and consider adjusting lending and savings interest rates to maintain or maximize NIM without excessively affecting customers and market competition.Nevertheless, there are cases where NIM has a positive effect on liquidity risk A high NIM indicates that the banks nhance lending, their long-term assets (such as loans) increase, while short-term cash supply (such as deposits) remains unchanged or decreases Therefore, although a high NIM may signal increased profitability, it may also come with increased liquidity risk

Return on Equity (ROE) has a positive correlation with liquidity risk, meaning that when other factors are constant, an increase in ROE leads to an increase in the liquidity risk of the bank As banks with higher profits engage in more business activities and have larger investment values, sudden exposure to risks from market fluctuations and uncontrollable incidents may pose a threat to liquidity risk Therefore, banks should monitor and maintain credit quality, ensure a healthy portfolio, and not be burdened by non-performing loans Nevertheless, there are also cases where ROE has an inverse impact on liquidity risk In these scenarios, it indicates that the banks are using their capital more efficiently and do not need to rely heavily on short-term funding Banks should prioritize enhancing liquidity management practices, diversifying funding sources, conducting stress testing, reviewing asset quality, optimizing capital allocation, and maintaining regulatory compliance to mitigate liquidity risk effectively

The Loans to Total Assets (LTA) ratio has a positive correlation with liquidity risk This indicates that commercial banks often concentrate on using capital for traditional lending activities Conventional loans typically have low liquidity, so significant unexpected withdrawals can lead to liquidity loss for the bank Therefore, the bank needs a clear liquidity risk management strategy, including evaluating total capital demand and maintaining sufficient liquidity reserves to cope with unexpected situations Balancing service provision and income enhancement while keeping liquidity risk at an acceptable level is crucial

The Equity to Total Assets (ETA) has an positive correlation with the liquidity gap (FINGAP) A high ETA indicates that the bank is using high financial leverage, leading to a sudden increase in capital costs, affecting the bank's business results and reducing liquidity Banks should focus on optimizing financial management, attracting more stable sources of funding such as individual customer deposits or long-term debt instruments, to reduce reliance on short-term capital They should conduct assessments of the quality and liquidity of their asset portfolios to minimize liquidity risk effectively

The Loan Loss Provision (LLP) has an negative correlation with the liquidity gap (FINGAP) This indicates that commercial banks often focus on using capital for traditional lending activities Conventional loans typically have low liquidity; therefore, significant unexpected withdrawals can lead to liquidity loss for the bank

If a bank has a high credit risk reserve ratio, it means the bank is holding assets with high liquidity This helps the bank address potential losses that may occur when customers fail to meet their loan obligations, thereby reducing liquidity risk for the bank Therefore, banks need to enhance credit risk management to minimize credit risk and boost liquidity Monitoring indices and reports on implemented measures to maintain liquidity and reduce risks is crucial for effective risk management

Gross Domestic Product (GDP) has a significant positive correlation with the liquidity gap (FINGAP) During economic recessions, banks tend to reserve more liquidity, and conversely, during periods of economic growth, when suitable lending opportunities arise, banks reduce liquidity reserves to lend more Hence, the higher the economic growth rate, the greater the liquidity risk Banks need to devise plans to adjust the scale, capital structure, etc., in line with lending activities based on market demand without adversely affecting the bank's liquidity

Inflation rate (INF) has a significant positive correlation with the liquidity gap (FINGAP) This indicates that higher interest rates can increase the cost of borrowing for banks, making the cost of accessing liquidity through loans or credit lines rise This can constrain liquidity and increase liquidity risk for banks Banks should consider implementing strategies such as interest rate swaps or derivatives to hedge against adverse interest rate movements and mitigate the impact on their funding costs, prioritize lending to sectors or industries less impacted by inflation, or adjusting the maturity profile of loans to better match funding sources, and strengthen their liquidity management practices to ensure they have sufficient liquidity buffers to withstand potential liquidity shocks.

Policy implications

To mitigate and prevent liquidity risks, commercial banks should adopt a multifaceted approach First and foremost, diversifying the asset portfolio is crucial

By investing in a variety of assets with high liquidity and low volatility, banks can reduce concentration risk and enhance overall liquidity Secondly, banks need to monitor asset quality by regularly assessing the quality and liquidity of their asset portfolios They should ensure that their assets are easily convertible into cash when needed to meet liquidity demands This may involve stress testing their asset portfolios under various inflation scenarios to identify potential liquidity gaps and develop appropriate contingency plans Thirdly, banks should optimize the capital allocation by reviewing the bank's capital allocation strategy to ensure it aligns with liquidity risk management objectives Banks may need to allocate more capital towards maintaining liquidity buffers or restructuring their asset-liability mix to improve overall liquidity resilience Then efficient liquidity management policies, including assessing overall capital requirements and maintaining adequate reserves, are essential Controlling the loan-to-deposit ratio ensures a balanced source of funds, minimizing reliance on external sources Fifthly, strengthening credit risk management practices is another key aspect, as it helps in identifying potential credit- related liquidity challenges Banks also should actively manage interest rate risk, as inflation often leads to changes in interest rates They should consider implementing strategies such as interest rate swaps or derivatives to hedge against adverse interest rate movements and mitigate the impact on their funding costs Next, regular stress testing and scenario analysis enable banks to evaluate the impact of market fluctuations and formulate proactive contingency plans Establishing robust reporting systems for real-time monitoring of liquidity positions enhances management's ability to make informed and timely decisions Furthermore, collaborating closely with regulatory bodies ensures compliance with liquidity regulations and facilitates government support during emergencies Ultimately, a dynamic and adaptable approach to liquidity risk management, coupled with continuous assessment and improvement of policies, will empower commercial banks to effectively minimize and prevent liquidity risks

5.2.2 For the State Bank of Vietnam

To address liquidity risks effectively, the State Bank of Vietnam (SBV) should consider implementing several strategic solutions Firstly, establishing a comprehensive liquidity risk management framework is crucial This involves adopting advanced analytical tools, stress testing, and scenario analysis to assess potential liquidity challenges The SBV should encourage commercial banks to diversify their funding sources and invest in highly liquid assets Promoting effective communication channels and collaboration between the SBV and banks would facilitate the timely exchange of information, enhancing the overall monitoring and management of liquidity risks Additionally, implementing stringent regulatory measures to ensure banks maintain sufficient liquidity buffers and adhere to prudent risk management practices is essential Regular training programs and capacity- building initiatives for bank personnel on liquidity risk management can strengthen the industry's resilience Furthermore, fostering international cooperation and aligning the SBV's policies with global best practices will contribute to a more robust and adaptable liquidity risk management framework By taking a proactive and collaborative approach, the SBV can significantly minimize and prevent liquidity risks in the banking sector, ensuring stability and resilience in the face of unforeseen challenges.

Limitations

The study focuses on analyzing and evaluating liquidity risks based on data collected from 29 commercial banks during the period 2012 – 2022, which may not be comprehensive, representative enough, or timely enough to reflect the current situation and trends of liquidity risks in Vietnam

The research concentrates on assessing liquidity risk using two indices, LDR and FINGAP, while liquidity risk can also be represented by the remaining two ratios in the set of four liquidity risk ratios, as well as other indicators Therefore, the study still has limitations in terms of generalizability

Liquidity risk, beyond the eight factors mentioned in the study, is influenced by many other factors such as External Funding Dependency (EFD), Return on Total Asset ratio (ROA), interest rates (IR), money supply (MS), etc.

Proposing directions for future research

From the aforementioned limitations, the study proposes several directions for future research Firstly, increasing the sample size will enhance the representativeness and generalizability of the results Extending the research period is also a crucial direction to gain a deeper understanding of trends and fluctuations in liquidity risks over time Utilizing diverse research methods, tests, as well as measurement indicators will make the study more comprehensive and accurate Furthermore, integrating both internal and macroeconomic variables into the research model is a potential avenue to assess a more comprehensive picture of liquidity risks for commercial banks in Vietnam

Chapter 5 has provides recommendations for both commercial banks and the central bank to prevent and minimize liquidity risks for commercial banks in Vietnam Additionally, this chapter has summarized the limitations of the study and suggested directions for further research on this topic

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A Research sample list: 29 commercial banks in Vietnam

1 ABB An Binh Commercial Joint Stock Bank

2 ACB Asia Commercial Joint Stock Bank

5 BaoVietBank Bao Viet Joint Stock Commercial Bank

6 BID Joint Stock Commercial Bank for Investment and

7 BVB Viet Capital Commercial Joint Stock Bank

8 CTG Vietnam Joint Stock Commercial Bank for Industry and Trade

9 EIB Vietnam Commercial Joint Stock Export Import Bank

10 HDB Ho Chi Minh City Development Joint Stock Commercial Bank

11 KLB Kien Long Commercial Joint Stock Bank

12 LPB Lien Viet Post Joint Stock Commercial Bank

13 MBB Military Commercial Joint Stock Bank

14 MSB Vietnam Maritime Commercial Join Stock Bank

15 NAB Nam A Commercial Joint Stock Bank

16 NVB National Citizen Commercial Joint Stock Bank

17 OCB Orient Commercial Joint Stock Bank

18 PGB Prosperity and Growth Commercial Joint Stock Bank

19 SCB Saigon Commercial Joint Stock Bank

20 SGB Saigon Bank For Industry And Trade

21 SHB Saigon Hanoi Commercial Joint Stock Bank

22 SSB Southeast Asia Commercial Joint Stock Bank

23 STB Sai Gon Thuong Tin Commercial Joint Stock Bank

24 TCB Vietnam Technological and Commercial Joint Stock Bank

25 TPB Tien Phong Commercial Joint Stock Bank

26 VAB Vietnam - Asia Commercial Joint Stock Bank

27 VPB Vietnam Prosperity Joint Stock Commercial Bank

28 VCB Bank for Foreign Trade of Vietnam

29 VIB Vietnam International Commercial Joint Stock Bank

B Research data on liquidity of 29 commercial banks in Vietnam during the period 2012 - 2022

STOCK YEAR SIZE NIM ROE LTA ETA LLP GDP INF LDR FINGAP

ABB 2012 7.66 0.0478 0.08 0.399 0.11 0.009 5.5 9.09 0.525 -0.326 ABB 2013 7.76 0.0286 0.03 0.399 0.10 0.011 5.6 6.59 0.498 -0.306 ABB 2014 7.83 0.0267 0.02 0.378 0.08 0.007 6.4 4.08 0.426 -0.291 ABB 2015 7.81 0.0279 0.02 0.474 0.09 0.006 7 0.63 0.546 -0.264 ABB 2016 7.87 0.0293 0.04 0.529 0.08 0.008 6.7 2.67 0.616 -0.170 ABB 2017 7.93 0.0299 0.08 0.558 0.07 0.009 6.9 3.52 0.641 -0.151 ABB 2018 7.95 0.0253 0.11 0.573 0.08 0.007 7.2 3.54 0.729 -0.141 ABB 2019 8.01 0.0287 0.14 0.547 0.08 0.007 7.2 2.8 0.658 -0.182 ABB 2020 8.07 0.0237 0.13 0.538 0.08 0.006 2.9 3.22 0.639 -0.133 ABB 2021 8.08 0.0277 0.15 0.564 0.10 0.007 2.6 1.83 0.724 -0.092 ABB 2022 8.11 0.0326 0.11 0.622 0.10 0.008 8.02 3.15 0.771 -0.083 ACB 2012 8.25 0.0377 0.06 0.575 0.07 0.009 5.5 9.09 0.740 -0.250 ACB 2013 8.22 0.0294 0.07 0.634 0.08 0.009 5.6 6.59 0.735 -0.216 ACB 2014 8.25 0.0308 0.08 0.639 0.07 0.009 6.4 4.08 0.724 -0.239 ACB 2015 8.30 0.0342 0.08 0.664 0.06 0.008 7 0.63 0.763 -0.219 ACB 2016 8.37 0.0347 0.10 0.692 0.06 0.008 6.7 2.67 0.781 -0.223 ACB 2017 8.45 0.0357 0.14 0.692 0.06 0.006 6.9 3.52 0.773 -0.181 ACB 2018 8.52 0.0369 0.28 0.692 0.06 0.008 7.2 3.54 0.793 -0.153 ACB 2019 8.58 0.0369 0.25 0.694 0.07 0.007 7.2 2.8 0.821 -0.164 ACB 2020 8.65 0.0381 0.24 0.694 0.08 0.007 2.9 3.22 0.826 -0.150 ACB 2021 8.72 0.0427 0.24 0.675 0.09 0.011 2.6 1.83 0.833 -0.103 ACB 2022 8.78 0.0449 0.26 0.673 0.10 0.008 8.02 3.15 0.859 -0.081 AGR 2012 8.79 0.0457 0.07 0.759 0.06 0.026 5.5 9.09 0.965 -0.064 AGR 2013 8.84 0.0353 0.03 0.745 0.06 0.024 5.6 6.59 0.916 -0.109 AGR 2014 8.88 0.0319 0.04 0.712 0.05 0.020 6.4 4.08 0.839 -0.154 AGR 2015 8.94 0.0328 0.07 0.707 0.05 0.014 7 0.63 0.822 -0.180 AGR 2016 9.00 0.0327 0.08 0.732 0.04 0.016 6.7 2.67 0.861 -0.149 AGR 2017 9.06 0.0335 0.09 0.750 0.04 0.014 6.9 3.52 0.868 -0.139 AGR 2018 9.11 0.0356 0.11 0.774 0.05 0.011 7.2 3.54 0.906 -0.106 AGR 2019 9.16 0.0339 0.18 0.760 0.05 0.014 7.2 2.8 0.883 -0.133

AGR 2020 9.20 0.0321 0.15 0.759 0.05 0.016 2.9 3.22 0.863 -0.163 AGR 2021 9.23 0.0324 0.16 0.756 0.04 0.020 2.6 1.83 0.852 -0.171 AGR 2022 9.27 0.0363 0.22 0.751 0.05 0.020 8.02 3.15 0.872 -0.157 BAB 2012 7.53 0.0284 0.01 0.654 0.09 0.008 5.5 9.09 0.745 -0.207 BAB 2013 7.70 0.0341 0.06 0.581 0.07 0.005 5.6 6.59 0.643 -0.262 BAB 2014 7.76 0.0224 0.07 0.631 0.07 0.006 6.4 4.08 0.710 -0.188 BAB 2015 7.80 0.0196 0.08 0.650 0.08 0.008 7 0.63 0.754 -0.191 BAB 2016 7.88 0.0205 0.09 0.628 0.08 0.006 6.7 2.67 0.726 -0.171 BAB 2017 7.96 0.0208 0.10 0.598 0.07 0.007 6.9 3.52 0.675 -0.103 BAB 2018 7.99 0.0189 0.10 0.653 0.07 0.006 7.2 3.54 0.744 -0.108 BAB 2019 8.03 0.0201 0.10 0.670 0.07 0.006 7.2 2.8 0.803 -0.093 BAB 2020 8.07 0.0189 0.07 0.671 0.07 0.007 2.9 3.22 0.797 -0.117 BAB 2021 8.08 0.0187 0.08 0.697 0.08 0.009 2.6 1.83 0.825 -0.127 BAB 2022 8.11 0.0212 0.09 0.723 0.08 0.008 8.02 3.15 0.872 -0.091 BaoVietBank 2012 7.12 0.0317 0.04 0.498 0.24 0.010 5.5 9.09 0.689 0.026 BaoVietBank 2013 7.23 0.0338 0.03 0.468 0.19 0.006 5.6 6.59 0.595 -0.044 BaoVietBank 2014 7.38 0.0194 0.03 0.402 0.14 0.003 6.4 4.08 0.476 -0.112 BaoVietBank 2015 7.49 0.0181 0.03 0.416 0.11 0.004 7 0.63 0.544 -0.143 BaoVietBank 2016 7.54 0.0201 0.03 0.439 0.10 0.004 6.7 2.67 0.543 -0.221 BaoVietBank 2017 7.69 0.0226 0.04 0.438 0.07 0.007 6.9 3.52 0.539 -0.144 BaoVietBank 2018 7.75 0.0129 0.02 0.455 0.06 0.006 7.2 3.54 0.571 -0.105 BaoVietBank 2019 7.78 0.0134 0.02 0.406 0.06 0.008 7.2 2.8 0.522 -0.210 BaoVietBank 2020 7.77 0.0096 0.02 0.383 0.06 0.003 2.9 3.22 0.455 -0.256 BaoVietBank 2021 7.82 0.0161 0.02 0.382 0.06 0.004 2.6 1.83 0.431 -0.219 BaoVietBank 2022 7.89 0.0129 0.02 0.420 0.05 0.004 8.02 3.15 0.476 -0.134 BID 2012 8.69 0.0223 0.10 0.689 0.06 0.012 5.5 9.09 0.992 0.006 BID 2013 8.74 0.0293 0.14 0.702 0.06 0.011 5.6 6.59 1.011 0.023 BID 2014 8.81 0.0306 0.15 0.675 0.05 0.010 6.4 4.08 0.846 -0.033 BID 2015 8.93 0.0278 0.17 0.695 0.05 0.009 7 0.63 0.929 -0.046 BID 2016 9.00 0.0271 0.14 0.709 0.04 0.010 6.7 2.67 0.953 -0.079 BID 2017 9.08 0.0300 0.15 0.712 0.04 0.016 6.9 3.52 1.276 -0.073 BID 2018 9.12 0.0298 0.15 0.744 0.04 0.011 7.2 3.54 1.045 -0.041 BID 2019 9.17 0.0285 0.13 0.740 0.05 0.008 7.2 2.8 0.830 -0.050 BID 2020 9.18 0.0263 0.09 0.788 0.05 0.013 2.9 3.22 0.928 -0.062 BID 2021 9.25 0.0305 0.13 0.752 0.05 0.017 2.6 1.83 0.916 -0.101 BID 2022 9.33 0.0312 0.19 0.700 0.05 0.018 8.02 3.15 0.922 -0.069 BVB 2012 7.32 0.0287 0.06 0.373 0.16 0.004 5.5 9.09 0.509 -0.213 BVB 2013 7.36 0.0251 0.03 0.430 0.14 0.005 5.6 6.59 0.513 -0.094 BVB 2014 7.41 0.0244 0.05 0.498 0.13 0.005 6.4 4.08 0.587 -0.073 BVB 2015 7.46 0.0176 0.02 0.542 0.11 0.004 7 0.63 0.645 -0.099 BVB 2016 7.51 0.0194 0.00 0.643 0.10 0.006 6.7 2.67 0.741 -0.117 BVB 2017 7.60 0.0203 0.01 0.621 0.08 0.006 6.9 3.52 0.697 -0.056

BVB 2018 7.67 0.0205 0.03 0.630 0.07 0.007 7.2 3.54 0.704 -0.089 BVB 2019 7.71 0.0210 0.04 0.647 0.07 0.009 7.2 2.8 0.761 -0.080 BVB 2020 7.79 0.0218 0.04 0.643 0.06 0.009 2.9 3.22 0.785 -0.120 BVB 2021 7.88 0.0231 0.06 0.597 0.06 0.009 2.6 1.83 0.773 -0.132 BVB 2022 7.90 0.0243 0.08 0.634 0.06 0.009 8.02 3.15 0.829 -0.120 CTG 2012 8.70 0.0724 0.20 0.655 0.07 0.007 5.5 9.09 0.864 0.024 CTG 2013 8.76 0.0522 0.13 0.647 0.09 0.006 5.6 6.59 0.846 -0.014 CTG 2014 8.82 0.0410 0.10 0.659 0.08 0.007 6.4 4.08 0.833 0.009 CTG 2015 8.89 0.0353 0.10 0.684 0.07 0.006 7 0.63 0.909 0.025 CTG 2016 8.98 0.0379 0.12 0.691 0.06 0.007 6.7 2.67 0.894 -0.025 CTG 2017 9.04 0.0399 0.12 0.714 0.06 0.008 6.9 3.52 0.911 0.006 CTG 2018 9.07 0.0488 0.08 0.732 0.06 0.011 7.2 3.54 0.923 -0.017 CTG 2019 9.09 0.0439 0.13 0.743 0.06 0.010 7.2 2.8 0.933 -0.022 CTG 2020 9.13 0.0402 0.17 0.748 0.06 0.009 2.9 3.22 0.907 -0.035 CTG 2021 9.19 0.0634 0.16 0.721 0.06 0.017 2.6 1.83 0.869 -0.079 CTG 2022 9.26 0.0307 0.17 0.688 0.06 0.016 8.02 3.15 0.874 -0.053 EIB 2012 8.23 0.0314 0.13 0.437 0.09 0.004 5.5 9.09 0.583 -0.047 EIB 2013 8.23 0.0181 0.04 0.487 0.09 0.004 5.6 6.59 0.574 -0.027 EIB 2014 8.21 0.0178 0.00 0.535 0.09 0.006 6.4 4.08 0.612 -0.113 EIB 2015 8.10 0.0261 0.00 0.672 0.11 0.007 7 0.63 0.797 -0.140 EIB 2016 8.11 0.0269 0.02 0.666 0.10 0.008 6.7 2.67 0.798 -0.152 EIB 2017 8.17 0.0211 0.06 0.671 0.10 0.007 6.9 3.52 0.787 -0.136 EIB 2018 8.18 0.0234 0.05 0.675 0.10 0.007 7.2 3.54 0.772 -0.103 EIB 2019 8.22 0.0222 0.06 0.670 0.09 0.006 7.2 2.8 0.766 -0.162 EIB 2020 8.21 0.0220 0.07 0.620 0.10 0.008 2.9 3.22 0.719 -0.215 EIB 2021 8.22 0.0233 0.06 0.683 0.11 0.008 2.6 1.83 0.791 -0.145 EIB 2022 8.27 0.0343 0.15 0.698 0.11 0.007 8.02 3.15 0.813 -0.105 HDB 2012 7.72 0.0230 0.07 0.397 0.10 0.004 5.5 9.09 0.502 -0.321 HDB 2013 7.94 0.0057 0.03 0.503 0.10 0.008 5.6 6.59 0.598 -0.250 HDB 2014 8.00 0.0213 0.05 0.417 0.09 0.005 6.4 4.08 0.495 -0.276 HDB 2015 8.03 0.0372 0.07 0.525 0.09 0.007 7 0.63 0.724 -0.221 HDB 2016 8.18 0.0415 0.09 0.541 0.07 0.006 6.7 2.67 0.669 -0.220 HDB 2017 8.28 0.0410 0.16 0.546 0.08 0.006 6.9 3.52 0.662 -0.143 HDB 2018 8.33 0.0407 0.20 0.564 0.08 0.006 7.2 3.54 0.730 -0.098 HDB 2019 8.36 0.0475 0.22 0.631 0.09 0.007 7.2 2.8 0.832 -0.028 HDB 2020 8.50 0.0474 0.21 0.553 0.08 0.006 2.9 3.22 0.714 -0.102 HDB 2021 8.57 0.0444 0.23 0.536 0.08 0.007 2.6 1.83 0.691 -0.067 HDB 2022 8.62 0.0519 0.24 0.626 0.09 0.007 8.02 3.15 0.855 0.002 KLB 2012 7.27 0.0352 0.10 0.514 0.19 0.008 5.5 9.09 0.709 -0.113 KLB 2013 7.33 0.0215 0.09 0.562 0.16 0.006 5.6 6.59 0.716 -0.084 KLB 2014 7.36 0.0156 0.05 0.580 0.15 0.006 6.4 4.08 0.699 -0.138 KLB 2015 7.40 0.0227 0.05 0.635 0.13 0.005 7 0.63 0.763 -0.158

KLB 2016 7.48 0.0335 0.04 0.644 0.11 0.006 6.7 2.67 0.750 -0.108 KLB 2017 7.57 0.0369 0.06 0.655 0.10 0.006 6.9 3.52 0.745 -0.044 KLB 2018 7.63 0.0291 0.06 0.691 0.09 0.006 7.2 3.54 0.792 -0.007 KLB 2019 7.71 0.0252 0.02 0.649 0.07 0.006 7.2 2.8 0.741 -0.018 KLB 2020 7.76 0.0203 0.03 0.601 0.07 0.005 2.9 3.22 0.678 -0.148 KLB 2021 7.92 0.0310 0.18 0.454 0.06 0.004 2.6 1.83 0.497 -0.163 KLB 2022 7.93 0.0278 0.11 0.514 0.06 0.007 8.02 3.15 0.589 -0.094 LPB 2012 7.82 0.0477 0.12 0.340 0.11 0.006 5.5 9.09 0.399 -0.282 LPB 2013 7.90 0.0395 0.08 0.364 0.09 0.007 5.6 6.59 0.416 -0.334 LPB 2014 8.00 0.0305 0.06 0.405 0.07 0.005 6.4 4.08 0.455 -0.367 LPB 2015 8.03 0.0321 0.05 0.516 0.07 0.006 7 0.63 0.632 -0.225 LPB 2016 8.15 0.0374 0.13 0.555 0.06 0.007 6.7 2.67 0.633 -0.256 LPB 2017 8.21 0.0394 0.15 0.608 0.06 0.008 6.9 3.52 0.710 -0.214 LPB 2018 8.24 0.0331 0.10 0.672 0.06 0.008 7.2 3.54 0.136 -0.099 LPB 2019 8.31 0.0352 0.14 0.687 0.06 0.009 7.2 2.8 0.910 -0.130 LPB 2020 8.38 0.0329 0.14 0.719 0.06 0.009 2.9 3.22 0.933 -0.134 LPB 2021 8.46 0.0368 0.19 0.712 0.06 0.011 2.6 1.83 0.922 -0.039 LPB 2022 8.52 0.1257 0.89 0.704 0.07 0.015 8.02 3.15 0.922 -0.062 MBB 2012 8.24 0.0470 0.19 0.417 0.08 0.007 5.5 9.09 0.502 -0.273 MBB 2013 8.26 0.0379 0.16 0.477 0.09 0.010 5.6 6.59 0.557 -0.289 MBB 2014 8.30 0.0378 0.15 0.489 0.09 0.012 6.4 4.08 0.584 -0.357 MBB 2015 8.34 0.0383 0.12 0.540 0.10 0.009 7 0.63 0.642 -0.292 MBB 2016 8.41 0.0371 0.12 0.580 0.10 0.008 6.7 2.67 0.687 -0.189 MBB 2017 8.50 0.0431 0.12 0.580 0.09 0.007 6.9 3.52 0.692 -0.141 MBB 2018 8.56 0.0469 0.19 0.584 0.09 0.009 7.2 3.54 0.715 -0.109 MBB 2019 8.61 0.0510 0.22 0.601 0.10 0.008 7.2 2.8 0.775 -0.126 MBB 2020 8.69 0.0495 0.19 0.594 0.10 0.009 2.9 3.22 0.824 -0.137 MBB 2021 8.78 0.0539 0.23 0.584 0.10 0.014 2.6 1.83 0.818 -0.159 MBB 2022 8.86 0.0142 0.06 0.616 0.11 0.016 8.02 3.15 0.905 -0.126 MSB 2012 8.04 0.0210 0.02 0.256 0.08 0.007 5.5 9.09 0.322 -0.306 MSB 2013 8.03 0.0183 0.04 0.249 0.09 0.007 5.6 6.59 0.305 -0.388 MSB 2014 8.02 0.0140 0.02 0.220 0.09 0.005 6.4 4.08 0.265 -0.421 MSB 2015 8.02 0.0188 0.01 0.264 0.13 0.006 7 0.63 0.351 -0.368 MSB 2016 7.97 0.0277 0.01 0.374 0.15 0.005 6.7 2.67 0.516 -0.293 MSB 2017 8.05 0.0195 0.01 0.319 0.12 0.004 6.9 3.52 0.419 -0.253 MSB 2018 8.14 0.0289 0.06 0.347 0.10 0.007 7.2 3.54 0.482 -0.175 MSB 2019 8.20 0.0255 0.07 0.399 0.09 0.006 7.2 2.8 0.497 -0.173 MSB 2020 8.25 0.0344 0.13 0.444 0.10 0.005 2.9 3.22 0.553 -0.117 MSB 2021 8.31 0.0374 0.21 0.490 0.11 0.008 2.6 1.83 0.624 -0.038 MSB 2022 8.33 0.0275 0.07 0.560 0.13 0.007 8.02 3.15 0.720 -0.045 NAB 2012 7.20 0.0337 0.05 0.423 0.20 0.004 5.5 9.09 0.633 -0.202 NAB 2013 7.46 0.0212 0.04 0.399 0.11 0.003 5.6 6.59 0.577 -0.250

NAB 2014 7.57 0.0229 0.06 0.442 0.09 0.004 6.4 4.08 0.497 -0.103 NAB 2015 7.55 0.0292 0.06 0.583 0.10 0.005 7 0.63 0.667 -0.104 NAB 2016 7.63 0.0335 0.01 0.552 0.08 0.009 6.7 2.67 0.624 -0.244 NAB 2017 7.74 0.0273 0.07 0.652 0.07 0.015 6.9 3.52 0.765 -0.115 NAB 2018 7.88 0.0285 0.15 0.667 0.06 0.010 7.2 3.54 0.764 -0.090 NAB 2019 7.98 0.0281 0.16 0.705 0.05 0.008 7.2 2.8 0.819 -0.089 NAB 2020 8.13 0.0252 0.14 0.658 0.05 0.006 2.9 3.22 0.760 -0.126 NAB 2021 8.19 0.0326 0.20 0.662 0.05 0.008 2.6 1.83 0.784 -0.159 NAB 2022 8.25 0.0060 0.17 0.666 0.07 0.007 8.02 3.15 0.817 -0.107 NVB 2012 7.33 0.0423 0.00 0.587 0.15 0.010 5.5 9.09 1.042 -0.204 NVB 2013 7.46 0.0304 0.01 0.456 0.11 0.007 5.6 6.59 0.576 -0.249 NVB 2014 7.57 0.0230 0.00 0.446 0.09 0.005 6.4 4.08 0.502 -0.217 NVB 2015 7.68 0.0228 0.00 0.419 0.07 0.004 7 0.63 0.464 -0.290 NVB 2016 7.84 0.0202 0.00 0.363 0.05 0.004 6.7 2.67 0.406 -0.256 NVB 2017 7.86 0.0196 0.01 0.442 0.04 0.005 6.9 3.52 0.523 -0.270 NVB 2018 7.86 0.0168 0.01 0.487 0.04 0.005 7.2 3.54 0.627 -0.291 NVB 2019 7.91 0.0185 0.01 0.466 0.05 0.005 7.2 2.8 0.530 -0.302 NVB 2020 7.95 0.0216 0.00 0.445 0.05 0.005 2.9 3.22 0.497 -0.383 NVB 2021 7.87 0.0208 0.00 0.555 0.06 0.009 2.6 1.83 0.639 -0.347 NVB 2022 7.95 0.1106 0.70 0.520 0.06 0.011 8.02 3.15 0.598 -0.296 OCB 2012 7.44 0.0522 0.06 0.617 0.14 0.011 5.5 9.09 0.798 0.056 OCB 2013 7.52 0.0464 0.06 0.609 0.12 0.006 5.6 6.59 0.728 0.026 OCB 2014 7.59 0.0327 0.06 0.541 0.10 0.008 6.4 4.08 0.633 -0.070 OCB 2015 7.69 0.0328 0.05 0.555 0.09 0.005 7 0.63 0.648 -0.042 OCB 2016 7.80 0.0317 0.09 0.598 0.07 0.005 6.7 2.67 0.705 -0.092 OCB 2017 7.93 0.0350 0.15 0.567 0.07 0.005 6.9 3.52 0.693 -0.116 OCB 2018 8.00 0.0400 0.24 0.558 0.09 0.006 7.2 3.54 0.730 -0.128 OCB 2019 8.07 0.0400 0.25 0.596 0.10 0.006 7.2 2.8 0.812 -0.089 OCB 2020 8.18 0.0398 0.24 0.579 0.11 0.006 2.9 3.22 0.811 -0.100 OCB 2021 8.27 0.0374 0.22 0.547 0.12 0.006 2.6 1.83 0.780 -0.111 OCB 2022 8.29 0.0070 0.02 0.609 0.13 0.008 8.02 3.15 0.960 -0.083 PGB 2012 7.28 0.0556 0.08 0.699 0.17 0.017 5.5 9.09 0.875 0.059 PGB 2013 7.40 0.0265 0.01 0.550 0.13 0.008 5.6 6.59 0.650 -0.007 PGB 2014 7.41 0.0280 0.04 0.556 0.13 0.007 6.4 4.08 0.658 -0.142 PGB 2015 7.39 0.0281 0.01 0.636 0.14 0.007 7 0.63 0.757 -0.047 PGB 2016 7.39 0.0305 0.04 0.699 0.14 0.007 6.7 2.67 0.834 -0.038 PGB 2017 7.47 0.0320 0.02 0.723 0.12 0.008 6.9 3.52 0.847 -0.057 PGB 2018 7.48 0.0325 0.04 0.730 0.12 0.008 7.2 3.54 0.891 -0.084 PGB 2019 7.50 0.0314 0.02 0.742 0.12 0.008 7.2 2.8 0.868 -0.062 PGB 2020 7.56 0.0291 0.04 0.704 0.11 0.006 2.9 3.22 0.813 -0.091 PGB 2021 7.61 0.0206 0.06 0.673 0.10 0.006 2.6 1.83 0.782 -0.020 PGB 2022 7.69 0.0299 0.09 0.587 0.09 0.006 8.02 3.15 0.688 -0.061

SCB 2012 8.17 0.0341 0.01 0.584 0.08 0.007 5.5 9.09 0.905 -0.027 SCB 2013 8.26 0.0178 0.00 0.488 0.07 0.004 5.6 6.59 0.538 -0.325 SCB 2014 8.38 0.0131 0.01 0.550 0.05 0.003 6.4 4.08 0.597 -0.269 SCB 2015 8.49 0.0204 0.01 0.543 0.05 0.004 7 0.63 0.611 -0.282 SCB 2016 8.56 0.0108 0.01 0.608 0.04 0.006 6.7 2.67 0.684 -0.226 SCB 2017 8.65 0.0058 0.01 0.595 0.03 0.005 6.9 3.52 0.649 -0.201 SCB 2018 8.71 0.0078 0.01 0.588 0.03 0.005 7.2 3.54 0.685 -0.234 SCB 2019 8.75 0.0100 0.01 0.583 0.03 0.005 7.2 2.8 0.690 -0.277 SCB 2020 8.80 0.0084 0.00 0.549 0.03 0.005 2.9 3.22 0.725 -0.363 SCB 2021 8.85 0.0055 0.02 0.502 0.03 0.010 2.6 1.83 0.648 -0.373 SCB 2022 8.88 0.0018 0.01 0.506 0.03 0.007 8.02 3.15 0.655 -0.407 SGB 2012 7.17 0.0739 0.09 0.724 0.24 0.007 5.5 9.09 0.995 0.020 SGB 2013 7.18 0.0526 0.05 0.691 0.23 0.007 5.6 6.59 0.933 -0.055 SGB 2014 7.22 0.0491 0.05 0.678 0.21 0.006 6.4 4.08 0.895 -0.080 SGB 2015 7.26 0.0408 0.01 0.627 0.18 0.005 7 0.63 0.790 -0.122 SGB 2016 7.29 0.0374 0.04 0.632 0.18 0.005 6.7 2.67 0.793 -0.120 SGB 2017 7.34 0.0354 0.02 0.638 0.16 0.005 6.9 3.52 0.774 -0.067 SGB 2018 7.32 0.0347 0.01 0.646 0.16 0.005 7.2 3.54 0.793 -0.082 SGB 2019 7.37 0.0368 0.04 0.617 0.15 0.005 7.2 2.8 0.747 -0.078 SGB 2020 7.39 0.0271 0.03 0.624 0.15 0.005 2.9 3.22 0.754 -0.143 SGB 2021 7.39 0.0262 0.04 0.664 0.13 0.007 2.6 1.83 0.807 -0.072 SGB 2022 7.44 0.7392 2.21 0.669 0.14 0.007 8.02 3.15 0.802 -0.071 SHB 2012 8.07 0.0232 0.22 0.478 0.08 0.011 5.5 9.09 0.573 -0.226 SHB 2013 8.16 0.0188 0.09 0.524 0.07 0.008 5.6 6.59 0.687 -0.225 SHB 2014 8.23 0.0201 0.08 0.610 0.06 0.006 6.4 4.08 0.688 -0.143 SHB 2015 8.31 0.0228 0.07 0.635 0.05 0.007 7 0.63 0.743 -0.132 SHB 2016 8.37 0.0216 0.07 0.686 0.06 0.008 6.7 2.67 0.812 -0.084 SHB 2017 8.46 0.0208 0.11 0.683 0.05 0.010 6.9 3.52 0.800 -0.050 SHB 2018 8.51 0.0205 0.11 0.662 0.05 0.009 7.2 3.54 0.803 -0.086 SHB 2019 8.56 0.0257 0.14 0.717 0.05 0.009 7.2 2.8 0.861 -0.066 SHB 2020 8.62 0.0202 0.12 0.741 0.06 0.008 2.9 3.22 0.892 -0.070 SHB 2021 8.70 0.0211 0.17 0.715 0.07 0.009 2.6 1.83 0.891 0.060 SHB 2022 8.74 0.0109 0.10 0.687 0.08 0.013 8.02 3.15 0.877 -0.035 SSB 2012 7.88 0.0163 0.01 0.216 0.07 0.006 5.5 9.09 0.266 -0.240 SSB 2013 7.90 0.0127 0.03 0.256 0.07 0.006 5.6 6.59 0.299 -0.222 SSB 2014 7.90 0.0103 0.02 0.394 0.07 0.006 6.4 4.08 0.454 -0.204 SSB 2015 7.93 0.0161 0.02 0.501 0.07 0.004 7 0.63 0.570 -0.206 SSB 2016 8.01 0.0222 0.02 0.565 0.06 0.005 6.7 2.67 0.635 -0.160 SSB 2017 8.10 0.0190 0.05 0.559 0.05 0.005 6.9 3.52 0.626 -0.111 SSB 2018 8.15 0.0191 0.07 0.590 0.06 0.006 7.2 3.54 0.737 -0.067 SSB 2019 8.20 0.0210 0.12 0.617 0.07 0.007 7.2 2.8 0.773 -0.088 SSB 2020 8.26 0.0195 0.12 0.596 0.08 0.006 2.9 3.22 0.698 -0.070

SSB 2021 8.33 0.0119 0.16 0.594 0.09 0.008 2.6 1.83 0.759 -0.014 SSB 2022 8.36 0.0841 0.22 0.655 0.11 0.011 8.02 3.15 0.878 0.063 STB 2012 8.18 0.0532 0.07 0.613 0.09 0.010 5.5 9.09 0.862 -0.147 STB 2013 8.20 0.0495 0.14 0.665 0.10 0.008 5.6 6.59 0.812 -0.159 STB 2014 8.28 0.0425 0.13 0.653 0.09 0.007 6.4 4.08 0.766 -0.208 STB 2015 8.46 0.0327 0.03 0.614 0.07 0.008 7 0.63 0.707 -0.279 STB 2016 8.52 0.0155 0.00 0.580 0.07 0.007 6.7 2.67 0.668 -0.300 STB 2017 8.56 0.0178 0.05 0.588 0.06 0.008 6.9 3.52 0.678 -0.298 STB 2018 8.60 0.0234 0.07 0.614 0.06 0.009 7.2 3.54 0.726 -0.267 STB 2019 8.70 0.0249 0.09 0.571 0.05 0.007 7.2 2.8 0.719 -0.242 STB 2020 8.69 0.0274 0.09 0.672 0.06 0.010 2.9 3.22 0.769 -0.218 STB 2021 8.72 0.0271 0.11 0.731 0.07 0.013 2.6 1.83 0.876 -0.129 STB 2022 8.77 0.0614 0.56 0.732 0.07 0.010 8.02 3.15 0.908 -0.080 TCB 2012 8.25 0.0358 0.08 0.377 0.07 0.006 5.5 9.09 0.450 -0.314 TCB 2013 8.20 0.0317 0.04 0.439 0.09 0.008 5.6 6.59 0.518 -0.363 TCB 2014 8.25 0.0396 0.08 0.450 0.08 0.009 6.4 4.08 0.533 -0.337 TCB 2015 8.28 0.0439 0.07 0.574 0.08 0.008 7 0.63 0.682 -0.213 TCB 2016 8.37 0.0418 0.16 0.601 0.08 0.008 6.7 2.67 0.716 -0.185 TCB 2017 8.43 0.0385 0.26 0.592 0.09 0.007 6.9 3.52 0.736 -0.112 TCB 2018 8.50 0.0416 0.22 0.494 0.15 0.000 7.2 3.54 0.659 -0.142 TCB 2019 8.58 0.0446 0.16 0.600 0.15 0.000 7.2 2.8 0.776 -0.014 TCB 2020 8.64 0.0501 0.16 0.626 0.17 0.000 2.9 3.22 0.846 -0.009 TCB 2021 8.75 0.0584 0.22 0.604 0.16 0.007 2.6 1.83 0.813 -0.008 TCB 2022 8.84 0.0203 0.06 0.595 0.16 0.007 8.02 3.15 0.800 0.033 TPB 2012 7.18 0.0162 0.05 0.396 0.22 0.006 5.5 9.09 0.606 -0.267 TPB 2013 7.51 0.0273 0.11 0.368 0.12 0.004 5.6 6.59 0.464 -0.152 TPB 2014 7.71 0.0262 0.14 0.382 0.08 0.004 6.4 4.08 0.425 -0.039 TPB 2015 7.88 0.0245 0.12 0.367 0.06 0.003 7 0.63 0.410 -0.151 TPB 2016 8.02 0.0250 0.11 0.437 0.05 0.004 6.7 2.67 0.484 -0.084 TPB 2017 8.09 0.0301 0.16 0.506 0.05 0.005 6.9 3.52 0.584 -0.089 TPB 2018 8.13 0.0383 0.21 0.560 0.08 0.007 7.2 3.54 0.704 -0.063 TPB 2019 8.22 0.0435 0.26 0.574 0.08 0.007 7.2 2.8 0.721 -0.076 TPB 2020 8.31 0.0475 0.24 0.572 0.08 0.009 2.9 3.22 0.765 -0.122 TPB 2021 8.47 0.0463 0.23 0.476 0.09 0.006 2.6 1.83 0.623 -0.121 TPB 2022 8.52 0.0055 0.03 0.484 0.10 0.006 8.02 3.15 0.600 -0.171 VAB 2012 7.39 0.0198 0.05 0.516 0.14 0.008 5.5 9.09 0.763 -0.157 VAB 2013 7.43 0.0235 0.02 0.525 0.13 0.007 5.6 6.59 0.623 -0.171 VAB 2014 7.55 0.0161 0.01 0.439 0.10 0.005 6.4 4.08 0.498 -0.117 VAB 2015 7.62 0.0346 0.02 0.479 0.09 0.005 7 0.63 0.419 -0.105 VAB 2016 7.79 0.0182 0.03 0.488 0.07 0.007 6.7 2.67 0.543 -0.047 VAB 2017 7.81 0.0199 0.02 0.526 0.06 0.005 6.9 3.52 0.599 -0.041 VAB 2018 7.85 0.0179 0.03 0.526 0.06 0.006 7.2 3.54 0.592 -0.078

VAB 2019 7.88 0.0154 0.05 0.551 0.06 0.006 7.2 2.8 0.619 -0.085 VAB 2020 7.94 0.0183 0.07 0.553 0.07 0.007 2.9 3.22 0.625 -0.148 VAB 2021 8.00 0.0193 0.11 0.533 0.06 0.006 2.6 1.83 0.599 -0.156 VAB 2022 8.02 0.4614 2.48 0.588 0.07 0.006 8.02 3.15 0.681 -0.081 VPB 2012 8.01 0.0371 0.10 0.356 0.06 0.004 5.5 9.09 0.433 -0.271 VPB 2013 8.08 0.0433 0.14 0.428 0.06 0.005 5.6 6.59 0.541 -0.326 VPB 2014 8.21 0.0432 0.15 0.473 0.06 0.007 6.4 4.08 0.582 -0.267 VPB 2015 8.29 0.0413 0.14 0.593 0.07 0.009 7 0.63 0.789 -0.191 VPB 2016 8.36 0.0777 0.26 0.623 0.08 0.009 6.7 2.67 0.948 -0.131 VPB 2017 8.44 0.0913 0.27 0.646 0.11 0.011 6.9 3.52 1.095 -0.072 VPB 2018 8.51 0.0938 0.23 0.676 0.11 0.011 7.2 3.54 0.986 -0.003 VPB 2019 8.58 0.0975 0.21 0.671 0.11 0.011 7.2 2.8 0.971 -0.049 VPB 2020 8.62 0.0890 0.22 0.683 0.13 0.011 2.9 3.22 1.003 -0.024 VPB 2021 8.74 0.0791 0.17 0.631 0.16 0.018 2.6 1.83 0.997 0.041 VPB 2022 8.80 0.1015 0.32 0.673 0.16 0.022 8.02 3.15 0.989 0.092 VCB 2012 8.62 0.0302 0.13 0.569 0.10 0.013 5.5 9.09 0.757 -0.122 VCB 2013 8.67 0.0266 0.10 0.571 0.09 0.014 5.6 6.59 0.729 -0.142 VCB 2014 8.76 0.0243 0.11 0.548 0.08 0.012 6.4 4.08 0.695 -0.187 VCB 2015 8.83 0.0263 0.12 0.561 0.07 0.013 7 0.63 0.676 -0.185 VCB 2016 8.90 0.0269 0.15 0.575 0.06 0.010 6.7 2.67 0.695 -0.188 VCB 2017 9.02 0.0265 0.18 0.517 0.05 0.008 6.9 3.52 0.701 -0.185 VCB 2018 9.03 0.0293 0.25 0.579 0.06 0.010 7.2 3.54 0.719 -0.188 VCB 2019 9.09 0.0318 0.26 0.592 0.07 0.009 7.2 2.8 0.733 -0.184 VCB 2020 9.12 0.0303 0.21 0.619 0.07 0.015 2.9 3.22 0.739 -0.176 VCB 2021 9.15 0.0328 0.03 0.661 0.91 0.018 2.6 1.83 0.772 -0.154 VCB 2022 9.26 0.0101 0.01 0.618 0.07 0.014 8.02 3.15 0.776 -0.082 VIB 2012 7.81 0.0407 0.06 0.512 0.13 0.009 5.5 9.09 0.588 -0.104 VIB 2013 7.89 0.0326 0.01 0.446 0.10 0.012 5.6 6.59 0.662 -0.116 VIB 2014 7.91 0.0335 0.06 0.462 0.11 0.011 6.4 4.08 0.551 -0.146 VIB 2015 7.93 0.0319 0.06 0.558 0.10 0.009 7 0.63 0.542 -0.074 VIB 2016 8.02 0.0306 0.06 0.566 0.08 0.010 6.7 2.67 0.712 -0.020 VIB 2017 8.09 0.0326 0.13 0.641 0.07 0.008 6.9 3.52 0.639 0.012 VIB 2018 8.14 0.0390 0.23 0.685 0.08 0.006 7.2 3.54 0.773 0.002 VIB 2019 8.27 0.0425 0.27 0.693 0.07 0.007 7.2 2.8 0.834 -0.063 VIB 2020 8.39 0.0455 0.30 0.686 0.07 0.007 2.9 3.22 0.855 -0.046 VIB 2021 8.49 0.0487 0.30 0.643 0.08 0.008 2.6 1.83 0.870 -0.054 VIB 2022 8.54 0.0505 0.30 0.668 0.10 0.009 8.02 3.15 0.837 -0.009

C The research results on Stata 17.0 software

Panel variable: MCK (strongly balanced)

Variable | Obs Mean Std dev Min Max -+ - LDR | 319 7120928 1598169 135749 1.275569 SIZE | 319 8.169185 5200717 7.123304 9.326461 NIM | 319 0354057 0490706 0017994 7392378 ROE | 319 1173641 2023546 0002829 2.479348 LTA | 319 579145 1135888 2162086 7880604 -+ - ETA | 319 0898788 0588242 0262139 9077135 LLP | 319 007922 0035846 3.83e-06 0256254 GDP | 319 6.001818 1.681391 2.6 8.02 INF | 319 3.738182 2.196745 63 9.09

Variable | Obs Mean Std dev Min Max -+ - FINGAP | 319 -.1384686 0984675 -.4206944 0916162 SIZE | 319 8.169185 5200717 7.123304 9.326461 NIM | 319 0354057 0490706 0017994 7392378 ROE | 319 1173641 2023546 0002829 2.479348 LTA | 319 579145 1135888 2162086 7880604 -+ - ETA | 319 0898788 0588242 0262139 9077135 LLP | 319 007922 0035846 3.83e-06 0256254 GDP | 319 6.001818 1.681391 2.6 8.02 INF | 319 3.738182 2.196745 63 9.09

| LDR SIZE NIM ROE LTA ETA LLP GDP INF -+ - LDR | 1.0000

| FINGAP SIZE NIM ROE LTA ETA LLP GDP INF -+ - FINGAP | 1.0000

The results of the Pooled OLS regression model - Model 1

Source | SS df MS Number of obs = 319 -+ - F(8, 310) = 152.02 Model | 6.47232812 8 809041015 Prob > F = 0.0000 Residual | 1.64985161 310 005322102 R-squared = 0.7969 -+ - Adj R-squared = 0.7916 Total | 8.12217973 318 025541446 Root MSE = 07295

- LDR | Coefficient Std err t P>|t| [95% conf interval] -+ - SIZE | 0294534 0106515 2.77 0.006 008495 0504117 NIM | 1598898 2087077 0.77 0.444 -.2507729 5705526 ROE | -.0107581 050653 -0.21 0.832 -.1104254 0889091 LTA | 1.185512 0440887 26.89 0.000 1.098761 1.272263 ETA | 2173943 0748005 2.91 0.004 0702133 3645753 LLP | 1.994946 1.502891 1.33 0.185 -.9622108 4.952103 GDP | -.0012999 0024733 -0.53 0.600 -.0061665 0035667 INF | 0069547 0020475 3.40 0.001 0029259 0109835 _cons | -.2730379 0877252 -3.11 0.002 -.44565 -.1004259 -

The results of the FEM regression model - Model 1

Fixed-effects (within) regression Number of obs = 319 Group variable: MCK Number of groups = 29

Within = 0.7130 min = 11 Between = 0.8887 avg = 11.0 Overall = 0.7902 max = 11

- LDR | Coefficient Std err t P>|t| [95% conf interval] -+ - SIZE | 0418632 0257855 1.62 0.106 -.0088934 0926197 NIM | -.1486463 2199483 -0.68 0.500 -.5815952 2843026 ROE | 0386186 0536681 0.72 0.472 -.0670224 1442596 LTA | 1.281614 0627209 20.43 0.000 1.158154 1.405075 ETA | 141421 0756251 1.87 0.063 -.0074404 2902824 LLP | 3.764712 1.707882 2.20 0.028 4028976 7.126527 GDP | -.00052 0022852 -0.23 0.820 -.0050183 0039783 INF | 0095486 0022064 4.33 0.000 0052054 0138918 _cons | -.4465131 2098156 -2.13 0.034 -.8595167 -.0335095 -+ - sigma_u | 04327326 sigma_e | 06571471 rho | 30246739 (fraction of variance due to u_i)

The results of the REM regression model - Model 1

Random-effects GLS regression Number of obs = 319 Group variable: MCK Number of groups = 29

Wald chi2(8) = 924.42 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - SIZE | 0251484 0144183 1.74 0.081 -.003111 0534078 NIM | -.0715871 2079391 -0.34 0.731 -.4791403 3359662 ROE | 0319931 0502023 0.64 0.524 -.0664016 1303878 LTA | 1.234778 0518516 23.81 0.000 1.133151 1.336406 ETA | 177189 0733245 2.42 0.016 0334756 3209024 LLP | 2.9575 1.586388 1.86 0.062 -.1517636 6.066763 GDP | -.0010795 002265 -0.48 0.634 -.0055189 0033599 INF | 0077973 0019688 3.96 0.000 0039384 0116561 _cons | -.2717087 1175091 -2.31 0.021 -.5020223 -.0413951 -+ - sigma_u | 02948108 sigma_e | 06571471 rho | 1675421 (fraction of variance due to u_i)

Summarize model included OLS-FEM-REM for model 1

The results of the Pooled OLS regression model - Model 2

Source | SS df MS Number of obs = 319 -+ - F(8, 310) = 23.55 Model | 1.16558377 8 145697971 Prob > F = 0.0000 Residual | 1.91769432 310 006186111 R-squared = 0.3780 -+ - Adj R-squared = 0.3620 Total | 3.08327809 318 009695843 Root MSE = 07865

- FINGAP | Coefficient Std err t P>|t| [95% conf interval] -+ - SIZE | -.0254139 0114836 -2.21 0.028 -.0480095 -.0028183 NIM | -.2823484 225012 -1.25 0.210 -.7250923 1603955 ROE | 105774 0546101 1.94 0.054 -.0016793 2132273 LTA | 5700263 0475329 11.99 0.000 4764983 6635542 ETA | 2539554 080644 3.15 0.002 0952766 4126342 LLP | -2.069113 1.620297 -1.28 0.203 -5.257284 1.119058 GDP | 0020636 0026665 0.77 0.440 -.0031831 0073104 INF | 0027291 0022075 1.24 0.217 -.0016145 0070726 _cons | -.2924241 0945783 -3.09 0.002 -.4785206 -.1063275 -

The results of the FEM regression model - Model 2

Fixed-effects (within) regression Number of obs = 319 Group variable: MCK Number of groups = 29

FINGAP | Coefficient Std err t P>|t| [95% conf interval] -+ - SIZE | 0720345 0226355 3.18 0.002 0274786 1165904 NIM | 1518622 1930785 0.79 0.432 -.2281958 5319203 ROE | -.0307615 0471118 -0.65 0.514 -.1234969 061974 LTA | 6830823 0550586 12.41 0.000 5747043 7914604 ETA | 143205 0663865 2.16 0.032 0125291 2738809 LLP | -2.441063 1.49924 -1.63 0.105 -5.392185 5100583 GDP | 0038641 0020061 1.93 0.055 -.0000847 0078128 INF | 0093999 0019369 4.85 0.000 0055873 0132125 _cons | -1.176165 1841837 -6.39 0.000 -1.538714 -.8136155 -+ - sigma_u | 07817052 sigma_e | 05768673 rho | 64742298 (fraction of variance due to u_i)

The results of the REM regression model - Model 2

Random-effects GLS regression Number of obs = 319 Group variable: MCK Number of groups = 29

Wald chi2(8) = 237.09 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ - SIZE | 0200651 0174805 1.15 0.251 -.0141961 0543263 NIM | 0060578 1916967 0.03 0.975 -.3696609 3817764 ROE | 0150958 0464147 0.33 0.745 -.0758753 1060669 LTA | 6761073 0521972 12.95 0.000 5738028 7784119 ETA | 1578774 0668255 2.36 0.018 0269018 288853 LLP | -2.497401 1.488807 -1.68 0.093 -5.41541 4206072 GDP | 0027961 0020223 1.38 0.167 -.0011675 0067597 INF | 0066005 0018456 3.58 0.000 0029833 0102177

_cons | -.7317956 1424789 -5.14 0.000 -1.011049 -.4525421 -+ - sigma_u | 05423334 sigma_e | 05768673 rho | 46917347 (fraction of variance due to u_i)

Summarize model included OLS-FEM-REM for model 2

Hausman Test Results for model 1 – Comparing FEM & REM model

| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | FEM REM Difference Std err

- b = Consistent under H0 and Ha; obtained from xtreg

B = Inconsistent under Ha, efficient under H0; obtained from xtreg

Test of H0: Difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Hausman Test Results for model 2 – Comparing FEM & REM model

| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | FEM REM Difference Std err

- b = Consistent under H0 and Ha; obtained from xtreg

B = Inconsistent under Ha, efficient under H0; obtained from xtreg

Test of H0: Difference in coefficients not systematic chi2(8) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Heteroskedasticity check results for model 1

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i chi2 (29) = 3085.74

Heteroskedasticity check results for model 2

Modified Wald test for groupwise heteroskedasticity in fixed effect regression model

H0: sigma(i)^2 = sigma^2 for all i chi2 (29) = 341.40

Autocorrelation check results for model 1

Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation

Autocorrelation check results for model 2

FGLS regression model results for model 1

Cross-sectional time-series FGLS regression

Estimated covariances = 29 Number of obs = 319 Estimated autocorrelations = 0 Number of groups = 29 Estimated coefficients = 9 Time periods = 11 Wald chi2(8) = 4030.45 Prob > chi2 = 0.0000

- LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - SIZE | 0386619 0059895 6.45 0.000 0269227 050401 NIM | -.0403145 1390292 -0.29 0.772 -.3128067 2321777 ROE | 0277899 0358282 0.78 0.438 -.0424321 0980118 LTA | 1.156241 0221485 52.20 0.000 1.112831 1.199651 ETA | 4747903 0618535 7.68 0.000 3535597 5960208 LLP | 5192234 9731645 0.53 0.594 -1.388144 2.426591 GDP | 0001561 0013486 0.12 0.908 -.002487 0027992 INF | 005002 0011732 4.26 0.000 0027025 0073014 _cons | -.3459937 051022 -6.78 0.000 -.445995 -.2459924 -

Summarize model included OLS-FEM-REM-FGLS for model 1

- (1) (2) (3) (4) LDR LDR LDR LDR - SIZE 0.0295*** 0.0419 0.0251* 0.0387***

[2.77] [1.62] [1.74] [6.45] NIM 0.160 -0.149 -0.0716 -0.0403 [0.77] [-0.68] [-0.34] [-0.29] ROE -0.0108 0.0386 0.0320 0.0278 [-0.21] [0.72] [0.64] [0.78] LTA 1.186*** 1.282*** 1.235*** 1.156*** [26.89] [20.43] [23.81] [52.20] ETA 0.217*** 0.141* 0.177** 0.475*** [2.91] [1.87] [2.42] [7.68] LLP 1.995 3.765** 2.957* 0.519 [1.33] [2.20] [1.86] [0.53] GDP -0.00130 -0.000520 -0.00108 0.000156 [-0.53] [-0.23] [-0.48] [0.12] INF 0.00695*** 0.00955*** 0.00780*** 0.00500*** [3.40] [4.33] [3.96] [4.26] _cons -0.273*** -0.447** -0.272** -0.346*** [-3.11] [-2.13] [-2.31] [-6.78] -

FGLS regression model results for model 2

Cross-sectional time-series FGLS regression

Correlation: common AR(1) coefficient for all panels (0.6997)

Estimated covariances = 29 Number of obs = 319 Estimated autocorrelations = 1 Number of groups = 29 Estimated coefficients = 9 Time periods = 11 Wald chi2(8) = 145.93 Prob > chi2 = 0.0000

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ - SIZE | -.0079599 0123694 -0.64 0.520 -.0322035 0162837

NIM | -.1285525 1229014 -1.05 0.296 -.3694349 1123299 ROE | 0361774 0336042 1.08 0.282 -.0296857 1020405 LTA | 5250658 048868 10.74 0.000 4292862 6208453 ETA | -.0170957 0275966 -0.62 0.536 -.0711841 0369926 LLP | -.8911887 1.160351 -0.77 0.442 -3.165435 1.383058 GDP | 0039791 001326 3.00 0.003 0013802 0065781 INF | 0041254 0014098 2.93 0.003 0013622 0068886 _cons | -.4015859 0994475 -4.04 0.000 -.5964993 -.2066724 -

Summarize model included OLS-FEM-REM-FGLS for model 2

- (1) (2) (3) (4) FINGAP FINGAP FINGAP FINGAP - SIZE -0.0254** 0.0720*** 0.0201 -0.00796 [-2.21] [3.18] [1.15] [-0.64] NIM -0.282 0.152 0.00606 -0.129 [-1.25] [0.79] [0.03] [-1.05] ROE 0.106* -0.0308 0.0151 0.0362 [1.94] [-0.65] [0.33] [1.08] LTA 0.570*** 0.683*** 0.676*** 0.525*** [11.99] [12.41] [12.95] [10.74] ETA 0.254*** 0.143** 0.158** -0.0171 [3.15] [2.16] [2.36] [-0.62] LLP -2.069 -2.441 -2.497* -0.891 [-1.28] [-1.63] [-1.68] [-0.77] GDP 0.00206 0.00386* 0.00280 0.00398*** [0.77] [1.93] [1.38] [3.00] INF 0.00273 0.00940*** 0.00660*** 0.00413*** [1.24] [4.85] [3.58] [2.93] _cons -0.292*** -1.176*** -0.732*** -0.402*** [-3.09] [-6.39] [-5.14] [-4.04] -

Endogenous variables test results in model 1

Instrumental variables 2SLS regression Number of obs = 290 Wald chi2(8) = 1079.15 Prob > chi2 = 0.0000 R-squared = 0.7884 Root MSE = 07062

- LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - SIZE | 0334293 0110993 3.01 0.003 0116752 0551835 NIM | 1127308 2079215 0.54 0.588 -.2947878 5202494 ROE | 0046996 050453 0.09 0.926 -.0941865 1035856 LTA | 1.145353 0458214 25.00 0.000 1.055545 1.235161 ETA | 1751446 0746678 2.35 0.019 0287984 3214909 LLP | 2.325309 1.578561 1.47 0.141 -.7686127 5.419231 GDP | -.00098 0024024 -0.41 0.683 -.0056886 0037286 INF | 0024146 0029291 0.82 0.410 -.0033263 0081554 _cons | -.2701663 089507 -3.02 0.003 -.4455968 -.0947359 - Instrumented: SIZE

Instruments: NIM ROE LTA ETA LLP GDP INF L.SIZE

- LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - NIM | 6.42394 2.2815 2.82 0.005 1.952283 10.8956 SIZE | 132696 0412828 3.21 0.001 0517832 2136087 ROE | -1.400707 5096325 -2.75 0.006 -2.399568 -.4018457

LTA | 1.114974 0941217 11.85 0.000 9304988 1.299449 ETA | -.2250149 2091426 -1.08 0.282 -.6349269 184897 LLP | -3.159041 3.737178 -0.85 0.398 -10.48378 4.165694 GDP | -.0055195 0051671 -1.07 0.285 -.0156467 0046078 INF | 0019752 0059834 0.33 0.741 -.009752 0137025 _cons | -1.011827 3159623 -3.20 0.001 -1.631101 -.3925519 - Instrumented: NIM

Instruments: SIZE ROE LTA ETA LLP GDP INF L.NIM

- LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - ROE | 7325859 1891658 3.87 0.000 3618277 1.103344 SIZE | -.0105536 0182644 -0.58 0.563 -.0463511 0252439 NIM | -2.638632 7234734 -3.65 0.000 -4.056613 -1.22065 LTA | 1.125906 0603074 18.67 0.000 1.007706 1.244106 ETA | 351625 1067335 3.29 0.001 1424311 5608188 LLP | 4.474463 2.141994 2.09 0.037 2762329 8.672693 GDP | -.0000359 0031619 -0.01 0.991 -.0062332 0061614 INF | 0027296 0038457 0.71 0.478 -.0048077 010267 _cons | 0717855 1453593 0.49 0.621 -.2131135 3566846 - Instrumented: ROE

Instruments: SIZE NIM LTA ETA LLP GDP INF L.ROE

- LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - LTA | 1.171819 0512638 22.86 0.000 1.071344 1.272295 SIZE | 0343469 0110594 3.11 0.002 0126708 056023 NIM | 1210027 2079222 0.58 0.561 -.2865174 5285228 ROE | 0012466 0504558 0.02 0.980 -.097645 1001382 ETA | 1771719 0746956 2.37 0.018 0307712 3235726 LLP | 1.874134 1.597163 1.17 0.241 -1.256247 5.004515 GDP | -.0009775 0024038 -0.41 0.684 -.0056888 0037339 INF | 0028162 0029441 0.96 0.339 -.0029541 0085865 _cons | -.2910242 089116 -3.27 0.001 -.4656883 -.1163601 - Instrumented: LTA

Instruments: SIZE NIM ROE ETA LLP GDP INF L.LTA

- LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - ETA | 848064 3503426 2.42 0.015 1614052 1.534723 SIZE | 052028 0150366 3.46 0.001 0225567 0814992

NIM | -.2082446 2889665 -0.72 0.471 -.7746085 3581192 ROE | 0750505 067958 1.10 0.269 -.0581446 2082456 LTA | 1.141929 0518056 22.04 0.000 1.040392 1.243466 LLP | 5066849 1.973894 0.26 0.797 -3.362076 4.375446 GDP | 001565 0030025 0.52 0.602 -.0043198 0074497 INF | 0025917 0033118 0.78 0.434 -.0038992 0090826 _cons | -.4781149 1402876 -3.41 0.001 -.7530735 -.2031563 - Instrumented: ETA

Instruments: SIZE NIM ROE LTA LLP GDP INF L.ETA

- LDR | Coefficient Std err z P>|z| [95% conf interval] -+ - LLP | -.2295607 2.244806 -0.10 0.919 -4.6293 4.170179 SIZE | 0438294 0123772 3.54 0.000 0195705 0680882 NIM | 1592295 209936 0.76 0.448 -.2522375 5706965 ROE | -.0056678 0508804 -0.11 0.911 -.1053915 0940559 LTA | 1.166759 0483789 24.12 0.000 1.071938 1.261579 ETA | 1909487 0754806 2.53 0.011 0430094 338888 GDP | -.0010973 0024136 -0.45 0.649 -.0058279 0036333 INF | 0031201 0029712 1.05 0.294 -.0027034 0089435 _cons | -.3512551 0993757 -3.53 0.000 -.5460279 -.1564824 - Instrumented: LLP

Instruments: SIZE NIM ROE LTA ETA GDP INF L.LLP

GMM method results for model 1

Dynamic panel-data estimation, two-step system GMM

- Group variable: MCK Number of obs = 261 Time variable : YEAR Number of groups = 29 Number of instruments = 18 Obs per group: min = 9 F(9, 28) = 24.62 avg = 9.00 Prob > F = 0.000 max = 9 - LDR | Coefficient Std err t P>|t| [95% conf interval] -+ - LDR |

SIZE | 1378147 0596218 2.31 0.028 015685 2599444 NIM | -1.489323 6000586 -2.48 0.019 -2.718487 -.2601586 ROE | 3627722 1361092 2.67 0.013 0839651 6415793 LTA | 9987005 1760648 5.67 0.000 6380482 1.359353 ETA | -.0343608 2732448 -0.13 0.901 -.5940773 5253558 LLP | 1.386223 2.300081 0.60 0.552 -3.325279 6.097725 GDP | 0022679 0015728 1.44 0.160 -.0009538 0054896 INF | 0048169 0042231 1.14 0.264 -.0038337 0134675 _cons | -.9621221 4171689 -2.31 0.029 -1.816654 -.1075903 - Warning: Uncorrected two-step standard errors are unreliable

Instruments for first differences equation

GMM-type (missing=0, separate instruments for each period unless collapsed) L(2/6).(L5.NIM L5.ROE L5.ETA) collapsed

GMM-type (missing=0, separate instruments for each period unless collapsed) DL.(L5.NIM L5.ROE L5.ETA) collapsed

Arellano-Bond test for AR(1) in first differences: z = -1.18 Pr > z = 0.236 Arellano-Bond test for AR(2) in first differences: z = -1.48 Pr > z = 0.138 - Sargan test of overid restrictions: chi2(8) = 3.80 Prob > chi2 = 0.874 (Not robust, but not weakened by many instruments.)

Hansen test of overid restrictions: chi2(8) = 2.88 Prob > chi2 = 0.941 (Robust, but can be weakened by many instruments.)

Difference-in-Hansen tests of exogeneity of instrument subsets:

Hansen test excluding group: chi2(5) = 2.24 Prob > chi2 = 0.815 Difference (null H = exogenous): chi2(3) = 0.65 Prob > chi2 = 0.886 iv(L2.GDP L2.INF)

Hansen test excluding group: chi2(6) = 1.42 Prob > chi2 = 0.965 Difference (null H = exogenous): chi2(2) = 1.47 Prob > chi2 = 0.480

Summarize model included OLS-FEM-REM-FGLS-GMM for model 1

LDR LDR LDR LDR LDR

Endogenous variables test results in model 2

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ - SIZE | -.0312641 0120452 -2.60 0.009 -.0548722 -.0076559 NIM | -.3605079 2256413 -1.60 0.110 -.8027568 081741 ROE | 125114 0547528 2.29 0.022 0178005 2324275 LTA | 5546955 0497265 11.15 0.000 4572333 6521577 ETA | 2456333 0810313 3.03 0.002 0868149 4044517 LLP | -2.053836 1.713092 -1.20 0.231 -5.411434 1.303762 GDP | 0019938 0026071 0.76 0.444 -.003116 0071037 INF | 0016051 0031787 0.50 0.614 -.004625 0078353 _cons | -.2306691 0971351 -2.37 0.018 -.4210503 -.0402878 - Instrumented: SIZE

Instruments: NIM ROE LTA ETA LLP GDP INF L.SIZE

Instrumental variables 2SLS regression Number of obs = 290 Wald chi2(8) = 52.05 Prob > chi2 = 0.0000 R-squared = Root MSE = 14128

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ -

NIM | 5.615232 2.235026 2.51 0.012 1.234662 9.995802 SIZE | 0657329 0404418 1.63 0.104 -.0135316 1449975 ROE | -1.205901 4992512 -2.42 0.016 -2.184416 -.2273869 LTA | 5238298 0922044 5.68 0.000 3431124 7045472 ETA | -.1283673 2048823 -0.63 0.531 -.5299293 2731947 LLP | -7.465639 3.661052 -2.04 0.041 -14.64117 -.2901089 GDP | -.0022571 0050618 -0.45 0.656 -.0121781 0076638 INF | 0012889 0058615 0.22 0.826 -.0101994 0127773 _cons | -.9556081 3095261 -3.09 0.002 -1.562268 -.3489479

Instruments: SIZE ROE LTA ETA LLP GDP INF L.NIM

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ - ROE | 9352614 2079099 4.50 0.000 5277654 1.342757 SIZE | -.0775074 0200741 -3.86 0.000 -.116852 -.0381628 NIM | -3.421309 7951611 -4.30 0.000 -4.979796 -1.862822 LTA | 530989 0662832 8.01 0.000 4010764 6609017 ETA | 4463481 1173096 3.80 0.000 2164256 6762706 LLP | 1396786 2.35424 0.06 0.953 -4.474547 4.753904 GDP | 0030802 0034753 0.89 0.375 -.0037311 0098916 INF | 0020456 0042267 0.48 0.628 -.0062386 0103299 _cons | 1295383 1597627 0.81 0.417 -.1835909 4426675

Instruments: SIZE NIM LTA ETA LLP GDP INF L.ROE

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ - LTA | 5709153 0555977 10.27 0.000 4619458 6798847 SIZE | -.0268539 0119944 -2.24 0.025 -.0503624 -.0033453 NIM | -.3340007 2255 -1.48 0.139 -.7759725 1079712 ROE | 1177146 0547214 2.15 0.031 0104627 2249665 ETA | 2513444 0810104 3.10 0.002 092567 4101218 LLP | -2.641163 1.732187 -1.52 0.127 -6.036187 753861 GDP | 0020347 002607 0.78 0.435 -.0030749 0071443 INF | 0020018 003193 0.63 0.531 -.0042563 0082599 _cons | -.2737935 0966498 -2.83 0.005 -.4632237 -.0843633

Instruments: SIZE NIM ROE ETA LLP GDP INF L.LTA

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ -

ETA | 1.365292 4321169 3.16 0.002 5183589 2.212226 SIZE | 0014907 0185464 0.08 0.936 -.0348595 0378409 NIM | -.8833043 3564148 -2.48 0.013 -1.581864 -.1847441 ROE | 2394865 0838202 2.86 0.004 0752019 403771 LTA | 5477673 0638977 8.57 0.000 4225301 6730044 LLP | -5.207238 2.434625 -2.14 0.032 -9.979016 -.4354603 GDP | 0062398 0037033 1.68 0.092 -.0010185 0134981 INF | 0019552 0040848 0.48 0.632 -.0060508 0099612 _cons | -.5901016 1730324 -3.41 0.001 -.929239 -.2509642 - Instrumented: ETA

Instruments: SIZE NIM ROE LTA LLP GDP INF L.ETA

- FINGAP | Coefficient Std err z P>|z| [95% conf interval] -+ - LLP | -3.488829 2.427306 -1.44 0.151 -8.24626 1.268603 SIZE | -.0223792 0133835 -1.67 0.094 -.0486103 0038519 NIM | -.3164054 2270035 -1.39 0.163 -.761324 1285132 ROE | 1148802 0550169 2.09 0.037 0070491 2227113 LTA | 5613051 052312 10.73 0.000 4587754 6638348 ETA | 257495 0816171 3.15 0.002 0975284 4174615 GDP | 0019866 0026098 0.76 0.447 -.0031287 0071018 INF | 0020453 0032128 0.64 0.524 -.0042516 0083422 _cons | -.2988557 1074548 -2.78 0.005 -.5094632 -.0882482

Instruments: SIZE NIM ROE LTA ETA GDP INF L.LLP

GMM method results for model 2

Dynamic panel-data estimation, two-step system GMM

- Group variable: MCK Number of obs = 174 Time variable : YEAR Number of groups = 29 Number of instruments = 26 Obs per group: min = 6 F(9, 28) = 69.57 avg = 6.00 Prob > F = 0.000 max = 6 - FINGAP | Coefficient Std err t P>|t| [95% conf interval] -+ - FINGAP |

SIZE | -.0196283 0205116 -0.96 0.347 -.0616443 0223877 NIM | 6519179 2120499 3.07 0.005 2175533 1.086282 ROE | -.1697396 0539135 -3.15 0.004 -.2801764 -.0593028 LTA | 0556638 1005742 0.55 0.584 -.1503531 2616807 ETA | 1.256016 3088584 4.07 0.000 6233486 1.888684 LLP | 3.844384 1.739042 2.21 0.035 2821177 7.406649 GDP | 0068359 0008196 8.34 0.000 0051569 0085148 INF | 013337 0025243 5.28 0.000 0081661 0185079 _cons | -.1589575 1401052 -1.13 0.266 -.4459499 128035 - Warning: Uncorrected two-step standard errors are unreliable

Instruments for first differences equation

GMM-type (missing=0, separate instruments for each period unless collapsed) L(2/6).(L5.SIZE L2.NIM L2.ROE L2.ETA) collapsed

GMM-type (missing=0, separate instruments for each period unless collapsed) DL.(L5.SIZE L2.NIM L2.ROE L2.ETA) collapsed

- Arellano-Bond test for AR(1) in first differences: z = -1.20 Pr > z = 0.230 Arellano-Bond test for AR(2) in first differences: z = -0.48 Pr > z = 0.633 - Sargan test of overid restrictions: chi2(16) = 3.31 Prob > chi2 = 1.000 (Not robust, but not weakened by many instruments.)

Hansen test of overid restrictions: chi2(16) = 9.07 Prob > chi2 = 0.911 (Robust, but can be weakened by many instruments.)

Difference-in-Hansen tests of exogeneity of instrument subsets:

Hansen test excluding group: chi2(12) = 7.45 Prob > chi2 = 0.827 Difference (null H = exogenous): chi2(4) = 1.62 Prob > chi2 = 0.805 iv(L5.GDP L5.INF)

Hansen test excluding group: chi2(14) = 6.87 Prob > chi2 = 0.940 Difference (null H = exogenous): chi2(2) = 2.20 Prob > chi2 = 0.332

Summarize model included OLS-FEM-REM-FGLS-GMM for model 2

FINGAP FINGAP FINGAP FINGAP FINGAP

Tiêu đề	Factors Affecting Liquidity Risk of Commercial Banks in Vietnam
Tác giả	Tran Nguyen Loan Chau
Người hướng dẫn	Dr. Nguyen Minh Nhat
Trường học	Ho Chi Minh University of Banking
Chuyên ngành	Finance and Banking
Thể loại	Graduation Thesis
Năm xuất bản	2024
Thành phố	Ho Chi Minh City

Định dạng
Số trang	128
Dung lượng	2,72 MB