The urgency of the research topic
The State Bank of Vietnam (2022) warns that if the economic recovery remains sluggish due to the ongoing impact of the Covid-19 pandemic, the domestic non-performing loan ratio could exceed 7.5% within a year In the medium to long term, continued complications from the pandemic may severely compromise the credit quality of financial institutions, leading to a significant rise in both on-balance sheet and potential non-performing loans This escalating risk of non-performing loans, driven by the pandemic, poses a serious threat to overall economic activities.
Dr Can Van Luc, Chief Economist of BIDV, highlighted concerns regarding the legal framework for non-performing loan management in the banking sector for the latter half of 2022 He noted that the expiration of Circular 14 on June 30, 2022, without an extension, could lead to a clearer visibility of potential non-performing loans on banks' balance sheets, thereby increasing the associated risks.
42 will also expire from August 15, 2022, and then the entire pilot mechanism to handle non-performing loans under this Resolution will also end
If Resolution 42 is not extended or legalized, Vietnam may face significant challenges in effectively managing non-performing loans (NPLs), which could dominate the financial market in 2022 The gross NPL ratio has reached its highest level in four years, threatening the restructuring progress of credit institutions Delays in addressing this issue could result in NPLs on balance sheets rising to 2.3-2.5%, with the overall gross NPL rate projected to hit approximately 6% in 2022, according to Mr Luc.
Understanding the factors influencing credit risk in Vietnamese joint-stock commercial banks is crucial, particularly given that real estate often serves as loan collateral While credit risk in banking is inevitable, it can be effectively managed and mitigated, leading to enhanced profitability for banks Maintaining low credit risk not only provides a competitive edge but also serves as a valuable tool for developing more efficient business strategies To minimize credit risk within these banks, it is essential to identify and analyze the determinants of credit risk, thereby gaining a clearer understanding of the credit risk landscape in Vietnamese joint-stock commercial banks and formulating actionable recommendations Consequently, the research topic chosen is "Determinants of Credit Risk: Empirical Evidence in Vietnamese Joint-Stock Commercial Banks."
Research objective
This study examines the determinants of credit risk in Vietnamese joint-stock commercial banks, specifically investigating the influence of macroeconomic and microeconomic factors on their credit risk levels.
- Reviewing the theoretical basis of factors affecting credit risk
- Measure the impact of macro and micro factors on credit risk using three econometric models OLS, REM, and FEM
- Proposing solutions based on the model's conclusions to minimize credit risks and develop the economy
Object and scope of research
Determinants of credit risk at commercial banks The question is, what factors are the credit risks of banks determined by?
3.2 Research scope: includes time scale, spatial scope, and content
About the time range: Data mining study for the period 2007-2021
In terms of spatial scope: A level study of the entire Vietnamese banking system including 19 state-owned and private joint-stock commercial banks
This study examines the factors influencing credit risk in Vietnamese joint-stock commercial banks by analyzing statistical data from 19 banks between 2007 and 2021 It models and tests the relationship between macroeconomic and microeconomic variables affecting credit risk, utilizing data sourced from the World Bank, the General Statistics Office of Vietnam, and banks' financial statements.
Research methodology
In this study, the following models are used:
- The Least-squares Model (OLS)
This study employs a static model to evaluate various testing methods, including the fixed-effects model (FEM), random-effects model (REM), and Pooled OLS model Through rigorous testing, such as the Hausman test and the Lagrange multiplier test, the FEM model is determined to be the most appropriate for the dataset Subsequent analysis reveals that while the model is free from multicollinearity, it does exhibit issues with autocorrelation and variable variance.
Research structure
In addition to the Declaration, Acknowledgment, Table of Contents, List of Abbreviations, and List of Tables, the research is structured into 5 chapters as follows:
LITERATURE REVIEW OF DETERMINANTS OF CREDIT RISK
Research on credit risk
Risks in the banking sector refer to unforeseen events that can result in asset losses, reduced profits, or increased costs associated with financial transactions One significant type of risk is credit risk, which pertains to the lending and credit extension activities of Joint Stock Commercial Banks (JSCBs) It's important to note that there is no universally accepted definition of credit risk in the industry.
Credit risk, as defined by Thomas P Fitch (1997), refers to the potential loss that arises when borrowers do not fulfill their contractual obligations to repay debts as agreed This type of risk, alongside interest rate risk, represents a primary concern in the lending operations of commercial banks.
Timothy W Koch (1995) highlighted that commercial banks face credit risk when customers fail to repay loans on time, impacting the bank's productive assets This risk can lead to potential fluctuations in net income and a decrease in the market value of the bank's capital due to customers' inability or delay in fulfilling their repayment obligations.
Credit risk, as defined by Sonja Bratanovic, refers to the situation where borrowers do not meet their obligations to pay interest or repay the principal within the agreed timeframe outlined in credit contracts This risk is a fundamental aspect of commercial banking operations, manifesting as delayed repayments or, in severe instances, total non-payment Such occurrences pose significant challenges to cash flow, ultimately impacting the liquidity of commercial banks.
A Saunders and H.Lange defined Financial institutions management – A modern perspective that: Credit risk is a potential loss when a commercial bank
5 provides a credit loan to a customer, or the ability that intended income flows from loans of commercial banks cannot implement fully both quantity and required duration
In the document "Banking Technology for Developed Countries," credit risk is described as the financial loss incurred by banks when a segment of their customers defaults on loan repayments.
Credit risk, as defined by the Basel Committee (2000), refers to the potential that a borrower or bank counterparty may fail to meet their contractual obligations This risk extends beyond the traditional lending relationship between banks and their clients, encompassing various activities including investments and derivatives transactions conducted by the bank.
In Vietnam, credit risk is defined by Article 1 of Circular No 40/2018/TT-NHNN, issued by the State Bank of Vietnam on December 28, 2018 It refers to the risk that customers may not fulfill their debt repayment obligations, either partially or fully, as stipulated in their contracts or agreements with commercial banks or foreign bank branches, excluding specific exceptions outlined in the regulation.
Customers, including credit institutions and foreign bank branches, engage with commercial banks and foreign bank branches to obtain credit, including credit extensions through trust arrangements, as well as to receive deposits and issue corporate bonds.
Credit risk refers to the potential loss a lender faces when a borrower fails to meet repayment obligations in a credit agreement This violation can manifest as either a total or partial inability to repay, posing significant risks to the lender, such as loss of capital and interest, cash flow disruptions, and increased collection costs Credit risk arises when any of the three key characteristics of credit operations—trust, repayment, and timeliness—are breached.
1.1.2 Criteria for assessing credit risk at commercial banks
First, Macro factors: showing the influence of the environment and macroeconomic fluctuations on the risk in credit activities of the bank
Economic growth: expressed by GDP growth
Most of the studies prove the negative influence of economic growth on credit risk for joint-stock commercial banks and other banks in general
Salas and Saurina (2002) examined the factors influencing the non-performing loan ratio of Spanish joint-stock commercial banks and savings banks from 1985 to 1997 They defined a savings bank as an institution focused on mobilizing individual savings, contrasting with commercial banks that primarily serve business needs Their findings revealed a negative correlation between GDP growth and the non-performing loan ratio, applicable to both types of banks.
Research by Boudriga et al (2009) on 46 banks across 12 Middle Eastern and North African countries from 2002 to 2006 indicates that the lagged GDP growth does not significantly influence the current credit risk of joint-stock commercial banks in developed nations In contrast, the business cycle appears to have a notable effect on credit risk in developing economies, highlighting the differing impacts of economic growth on banking stability across regions.
A survey of 30 Vietnamese joint-stock commercial banks from 2006 to 2015 revealed a significant negative correlation between GDP growth and non-performing loans, indicating that stable and sustainable economic growth positively influences the reduction of non-performing loan ratios in these banks.
(2014) when studying 26 domestic joint-stock commercial banks in the period 2009
- 2012, did not find a significant correlation between GDP growth rate in the current
A study by Nguyen Thuy Duong and Do Thi Thu Huong (2017) on 20 joint-stock commercial banks from 2006 to 2014 revealed a significant positive relationship between GDP growth and credit risk, contradicting previous research This phenomenon in Vietnam can be attributed to the economy's heavy reliance on bank loans, where rapid credit expansion often leads to the misallocation of funds into high-risk ventures, thereby increasing credit risk The General Statistics Office indicates that Vietnam's Capital Efficiency Ratio (ICOR) remains high compared to regional peers, highlighting the inefficiency in utilizing mobilized investment capital Consequently, the non-performing loan ratio, which lags one year behind GDP growth, shows a positive correlation with GDP, suggesting that credit quality tends to decline during economic growth periods.
Research consistently shows that high-interest rates significantly influence bank lending rates, leading to an increase in non-performing loans (NPLs) Initial studies by Sinkey and Greenawalt (1991) highlighted that rising interest rates correlate with higher loan losses among large US commercial banks Similarly, Berge and Boye (2007) found a direct relationship between increased interest rates and the rise in bad loans in Norway Espinoza and Prasad (2010) further demonstrated that in the Gulf Cooperation Council (GCC) banking sector, higher interest rates elevate lending rates, which negatively affects borrowers' ability to repay due to inflated interest payments Numerous studies, including those by Beck et al (2015), Ghosh (2015), and Us (2017), support the notion that increased interest rates hinder borrowers' repayment capacity However, some research, such as that by Messai and Jouini (2013), indicates that the impact of high-interest rates on loan quality is primarily significant in banks with floating-rate loan structures, while fixed-rate loans remain unaffected, preserving borrowers' ability to meet their debt obligations.
Inflation: expressed by the inflation rate
The impact of inflation on the credit risk of joint-stock commercial banks has garnered significant research interest, yielding mixed results A study by Wiem Ben Jabra et al (2017) on 280 banks in the euro area from 2003 to 2013 indicated that rising inflation decreases the real value of loans and lowers default rates, thereby reducing credit risk Conversely, Le Ba Truc (2015) analyzed 35 banks in Vietnam between 2006 and 2012 and found that inflation increases credit demand due to rising costs of materials, goods, and services, leading to higher interest rates and increased debt service costs, which heightens credit risk Additionally, some studies, including those by Chaibi et al (2015) and Dao Thi Thanh Binh and Do Van Anh (2013), found no significant correlation between inflation and credit risk.
Research gap
In summary, an overview of studies on factors affecting the credit risk of joint-stock commercial banks shows that
Numerous research studies globally have sought to identify the factors influencing credit risk in joint-stock commercial banks, but their findings often vary This inconsistency highlights the unique characteristics of each country's economic policies and banking supervision mechanisms, which contribute to specific factors impacting credit risk As a result, the conclusions drawn from these studies may not align with the practical realities observed in Vietnam.
In Vietnam, while numerous studies have identified both macro and micro factors influencing the credit risk of joint-stock commercial banks, most research has primarily focused on credit risk management within specific branches or banks, often relying on qualitative methods or survey data There is a notable lack of econometric models that objectively analyze these factors through quantitative studies This article aims to fill that gap by empirically examining and quantifying the impact of various factors on credit risk in Vietnamese joint-stock commercial banks, utilizing up-to-date data.
Third, there are almost no studies examining the impact of the Covid-19 pandemic on credit risk, especially in Vietnam
RESEARCH METHOD
Research process
The topic uses the regression estimation method with panel data to analyze internal factors as well as external factors affecting credit risk at Vietnamese joint- stock commercial banks Specifically
In this study, we conducted a thorough analysis of descriptive statistics to determine the mean, standard deviation, maximum, and minimum values for each research variable This approach allowed us to effectively summarize the fundamental characteristics of the data collected from our research sample, providing essential insights into the overall profile of the study participants.
- Correlation analysis to test the relationship between independent and dependent variables
Regression analysis is a powerful statistical method used to assess the significant or negligible effects of independent variables on a dependent variable By utilizing this technique, researchers can determine the direction and magnitude of the influence each independent variable has on the dependent variable, providing valuable insights for data-driven decision-making.
- The results of the model are tested and compared to find the most suitable model for studying the factors affecting credit risk at Vietnamese joint-stock commercial banks
The experimental research process can be summarized through the following steps:
Step 1: Determine the determinants of the credit risk of commercial banks
Step 2: Collect data of Vietnamese joint-stock commercial banks from 2007-
Step 3: Encrypt the independent variable
Step 4: Analyze the data to check the satisfaction of the model's hypotheses Step 5: Build the correlation coefficient matrix
Step 6: Estimating the initial model Estimating the models in turn:
- Pooled OLS conventional linear regression model
- F-test, Hausman test to choose between the Pool OLS model, the random effect model, and the fixed effect model
- Test of variance of variable error, a test of autocorrelation
- Fix GLS Check the significance of the regression coefficients in the model Step 8: Summarize the results and write the conclusion
Research method
The research utilizes a comprehensive panel dataset derived from the year-end consolidated audited financial statements of Vietnamese joint-stock commercial banks This dataset encompasses cross-sectional observations from 18 banks, alongside time-series data collected from 2007 to 2021, providing insights into the financial performance of these institutions over multiple years The financial statements were sourced from the banks' official websites and stock exchanges, ensuring the reliability of the data used for analysis.
Auditors must gather sufficient and reliable data for their research, utilizing macroeconomic information from the General Statistics Office of Vietnam and the World Bank This data is verified and organized in Excel for analysis with Stata software Descriptive statistical analysis is conducted using Excel tables and graphs, providing key statistics such as mean, standard deviation, and minimum and maximum values to characterize the variables studied Additionally, various tests are performed to assess the appropriateness of the models utilized in the research.
- Linear relationship between the independent variable and dependent variable
- Random error with a normal distribution N (0, σ2)
- The variance of random error is uniform and not correlated with each other
- There is no correlation between random error and independent variable
If there is a violation in the model's assumptions, one of the possible solutions is to transform the data to ensure the reasonableness of the estimated model
The study analyzes the correlation between independent variables and the dependent variable, as well as among the independent variables themselves, to assess their impact on credit risk in Vietnamese joint-stock commercial banks It selects representative variables based on the principle of choosing only one variable from each factor that closely relates to credit risk In cases where two variables within the same factor show a strong correlation with credit risk, the variable with the closer relationship is prioritized.
Most practical investigations in economics aim to clarify the relationship between a dependent variable Y and one or more independent variables (X1, X2, X3, etc.) Understanding the impact of these explanatory variables is essential for achieving this goal.
To accurately assess the impact of variable X on variable Y, it is essential to account for both the direction and magnitude of this effect An unbiased estimate necessitates controlling for noisy variables, which can be either observable or unobservable For observable noisy variables, a classical multivariable linear regression (MCLR) model is appropriate In contrast, to address unobserved confounding variables, researchers can utilize either fixed effect or random effect regression models, depending on the distinct characteristics of the subjects and the time period under study.
This is a multiple regression analysis methods based on the principle of least squares to find out the relationship between the dependent variable and the independent variables
The Pooled OLS method is utilized to assess the model's constants and parameters, with the significance coefficient (P-value) indicating the degree of influence that individual variables exert on the dependent variables Common thresholds for statistical significance include 1%, 5%, and 10%, corresponding to confidence levels of 99%, 95%, and 90%, respectively Additionally, the R-squared (R²) and adjusted R-squared values from the analysis reveal the effectiveness of all independent variables in explaining the variations in Non-Performing Loans (NPL) and Loan Loss Reserves (LLR) within the regression model.
The cross-data OLS model is a dependable approach for estimating the linear relationship between dependent and independent variables However, its limitations arise from the model's rigid constraints in both area and time, leading to constant regression coefficients This inflexibility can obscure the true impact of independent variables on the dependent variable, rendering the model's results ineffective in real-world scenarios.
With the assumption that separately entity has unique aspects that can influence the explanatory variables, FEM scrutinizes this correlation between the
The estimated model analyzes the residuals of each entity and utility variables, effectively isolating the influence of individual characteristics of the explanatory variables over time This approach allows for a clear evaluation of the net effects of the explanatory variable on the dependent variable.
Yit : dependent variable with i: business and t: time (year)
Xit : independent variable Ci (i=1…n): intercept coefficient for each research entity β: slope for factor X uit: remainder
The model incorporates an index "i" for the intercept "c" to differentiate the intercept coefficients of individual enterprises This variation may arise from the unique characteristics of each business or from differences in their management strategies and procedures.
The key distinction between the random-effects model and the fixed-effects model lies in how they treat variation between entities In the fixed-effects model, this variation is assumed to be correlated with the independent variable, also known as the explanatory variable Conversely, the random-effects model posits that the variation between entities is random and not correlated with the explanatory variables.
If the differences between entities influence the dependent variable, the Random Effects Model (REM) is more suitable than the Fixed Effects Model (FEM) In this context, the residuals of each entity, which are uncorrelated with the explanatory variable, are treated as new explanatory variables.
The basic idea of the random effects model also starts with the model:
In the REM model, unlike the previous approach where Ci is constant, it is treated as a random variable with a mean of C1, along with an intercept value that is defined in the following manner.
Substituting the model we have:
The equation Yit = Ci + βXit + i + uit, or alternatively Yit = Ci + βXit + wit (where wit = i + uit), represents a statistical model that accounts for both individual and time-related errors in enterprise characteristics In this model, εi denotes the component error associated with the unique characteristics of each enterprise, while uit captures the errors stemming from the combined effects of individual attributes over time.
The choice between Fixed Effects Model (FEM) and Random Effects Model (REM) in research largely hinges on the correlation between the error term (εi) and the explanatory variables (X) If no correlation is assumed, REM is preferred; otherwise, FEM is more suitable The Hausman test serves as a key method for selecting between these models Consequently, this analysis will evaluate the regression results using Pooled Ordinary Least Squares (OLS), FEM, and REM to determine the most appropriate model.
When determining the appropriate model for analysis, one must consider whether to use Pooled OLS, Fixed Effects Model (FEM), or Random Effects Model (REM) The effectiveness of both the random and fixed effects models is validated through a comparison with initial rough estimations.
Determinants affecting credit risk at commercial banks and hypothesis
Numerous scholars have long examined the relationship between unemployment and the credit risk faced by banks, consistently finding that higher unemployment rates positively correlate with increased credit risk (Salas and Saurina, 2002) This phenomenon can be explained in two primary ways: first, as noted by Dimitrios et al (2016), Klein (2013), and Louzis et al (2012), rising unemployment often serves as a justification for the deterioration in loan quality, as it adversely impacts a nation's production and business activities Second, Ghosh (2015) and Lawrence highlight additional factors that contribute to this relationship.
In 1995, research revealed that low-income workers are at a higher risk of job loss, which subsequently hampers their ability to meet salary obligations This situation exacerbates existing debt issues, contributing to a rise in the non-performing loan ratio.
Research indicates that individuals with low income face higher interest rates on bank loans due to an increased risk of default, as noted by a study in 1995 This situation not only diminishes their income but also elevates their risk of falling into debt Subsequent studies by Rinaldi and Sanchis-Arellano (2006), Dimitrios et al (2016), and Jabbouri & Naili further explore these financial challenges faced by low-income borrowers.
Kuzucu & Kuzucu (2019) expanded on the theory regarding non-performing loan ratios, highlighting that low income and employment status are significant macroeconomic factors This study will empirically test these assertions through the proposed hypothesis.
H1: Credit risk represented by the non-performing loan ratio is negatively affected by the unemployment rate
Inflation significantly influences credit risk, prompting scholarly interest in this relationship; however, research findings vary widely (Amuakwa-Mensah et al., 2017; Ghosh, 2015; Gulati et al., 2019; Nkusu, 2011; Us, 2017) The studies reveal four distinct outcomes regarding this interplay.
Rising inflation significantly heightens credit risk by diminishing the real value of bank customers' incomes, which can impair their ability to meet debt obligations, as highlighted in a study by Rinaldi and Sanchis-Arellano (2006) This relationship has been further substantiated by research conducted in CESEE countries from 1998 to 2011, reinforcing Klein's (2013) findings In an inflationary environment, customers with floating-rate loans face substantial challenges, as noted by Amuakwa-Mensah et al (2017) and others Le Ba Truc (2018) also pointed out that inflation devalues currency and reduces overall returns Additionally, rising prices increase the demand for credit due to higher costs for materials, supplies, energy, and labor, leading to tighter monetary policies that elevate interest rates Consequently, both businesses and individuals may struggle with debt repayment as the costs of servicing loans rise.
Recent studies by Makri et al (2014) and Nkusu (2011) indicate an inverse relationship between inflation and credit risk This phenomenon occurs because inflation diminishes the real value of debt, as noted by Wiem Ben Jabra et al (2017) and Nkusu.
2011) The bonus is that the full fulfillment of credit terms by individual customers
The Guyanese banking industry has shown that increased inflation can lead to higher wages for workers, which strengthens the foundation for debt repayment (Khemraj & Pasha, 2009) Additionally, research from the Indian banking sector indicates that inflation periods correlate with a decreased risk of bank defaults (Gulati et al., 2019) This suggests that both corporate and individual customers may find banking more feasible in inflationary environments.
Research indicates that the relationship between inflation and credit risk varies significantly across different regions In developing markets, inflation tends to negatively affect credit risk, whereas in developed countries, rising inflation is associated with an increase in credit risk (Kuzucu and Kuzucu, 2019).
Fourth, some scholars have come to the interesting conclusion that the inflation rate does not affect credit risk A typical example is the study of data from
Between 2006 and 2013, Tanaskovic and Jandric (2015) conducted a study on a sample of Central, Eastern, and Southeastern European (CESEE) countries, while Peric and Konjusak (2017) examined similar relationships in European countries from 1999 to 2013, yielding comparable results However, the conflicting findings highlight the necessity for a more in-depth investigation, leading to the hypothesis of this study.
H2: A bank's credit risk as measured by its non-performing loan ratio is affected by inflation
State debt, or government debt, significantly impacts a country's economy, particularly following the 2009 European sovereign debt crisis In their 2011 study, Reinhart and Rogoff analyzed 290 banking crises, highlighting the profound effects of such debt on financial stability.
209 government defaults in 70 developed and developing countries between 1800 and 2009 They unearthed a close relationship between government debt and banking crises
Increased public debt often leads to higher tax collection efforts by countries, creating uncertainty for individuals and organizations regarding their financial circumstances, as noted by Perotti (1996).
Perotti (1996) highlighted that rising public debt may lead governments to reduce public spending and support for welfare and salary funds, resulting in slower bank obligation fulfillment by individuals and heightened credit risk Investigations suggest that public debt can lead to the collapse of public finances, severely damaging the central bank's reputation and prompting proposals for maximum payouts This situation creates liquidity issues for banks, forcing them to restructure their deposit and lending capital, which often results in reduced credit availability and challenges in refinancing or disbursing old loans Consequently, borrowers face repayment difficulties, leading to an increase in non-performing loans (Reinhart & Rogoff, 2011).
Research by Louzis et al (2012), Makri et al (2014), and Ghosh (2015) indicates that a reduction in public debt correlates with enhanced loan quality in banks These findings underscore the significant role of government debt in influencing credit risk, leading to the hypothesis of this study.
H3: Bank's credit risk is represented by the ratio of non-performing loans affected by public debt
Researchers are increasingly focused on how internal factors within a bank influence non-performing loans, as these elements can significantly contribute to variations in credit risk.
Starting with the Ghosh studies (2017), surveyed the period 1992-2016 at
RESEARCH RESULTS
Regression results
Table 2 Summarizing the results of the regression
Determinant Proxy Symbol Sample of theliterature Expected Result Meaning
Ratio of non- performing loans
(NPLs) to total gross loans
NPL (Ghosh, 2017; Louzis et al.,
2012; Salas and Saurina, 2002; Shehzad et al., 2010;
Zribi, Nabila và cộng sự
(2011), Wiem Ben Jabra và cộng sự (2017), Nguyễn Thùy Dương & Trần Thị Thu Hương (2017)
Percentage growth of total loans between two consecutive years
DIV (Ghosh, 2017; Koju et al.,
Bank performance ROE = Net income / Total equity
ROE (Louzis et al., 2012; Makri et al., 2014; Jabbouri and El Attar, 2018b; Benrquia and Jabbouri, 2021)
Natural log of total assets
LSIZE (Albaity et al., 2019; Zhang et al., 2016)
Koju et al., 2018; Louzis et al., 2012; Ozili, 2019;
Percentage (%) of unemployment in year t
UEM (Lawrence, 1995; Louzis et al., 2012; Rinaldi and Sanchis-Arellano, 2006)
Gross government debt as % of GDP
DEB (Louzis et al., 2012; Makri et al., 2014)
Basel dummy variable, year with Basel value 1, year without Basel value 0
(recorded the first Covid-19 case in Vietnam is 2020) 2020 and 2021 get the value 1, the remaining years get the value 0)
Table 3 Statistical summary of variables sum NPL CAR CPA GRO DIV ROE LSIZE CIR INF UEM DEB BAE C19
Variable | Obs Mean Std Dev Min Max -+ - NPL | 243 1.669288 9699684 08 8.8 CAR | 229 11.98301 3.960922 6.5 45.89 CPA | 265 5.525083 4.316635 04 17 GRO | 255 22.43722 16.98243 01 145.78 DIV | 266 23.12491 12.08086 49 75.09 -+ - ROE | 260 15.64591 7.238318 0 44.49 LSIZE | 264 14.26532 5192549 8.47 15.25 CIR | 265 48.14285 17.65325 24.3 255.13 INF | 270 6.936674 6.109529 6312009 23.11545 UEM | 270 1.7198 5812353 1 3.22 -+ - DEB | 270 53.74667 6.584659 43 63.7 BAE | 270 1703704 376656 0 1 C19 | 270 1333333 3405659 0 1
Table 3 provides an overview of the variables used in the model, revealing that the non-performing loan ratio of commercial banks during the investigation period is relatively low, averaging 1.669% Additionally, there is minimal variation among the banks, indicated by a standard deviation of only 0.9699% This suggests that credit risk remains within a safe threshold, likely due to stringent credit management practices, classification, and assessment.
corr NPL CAR CPA GRO DIV ROE LSIZE CIR INF UEM DEB BAE C19
| NPL CAR CPA GRO DIV ROE LSIZE CIR INF UEM DEB BAE C19 -+ - NPL | 1.0000
Table 5 Coefficient of variance inflation
Multicollinearity between variables is tested by two methods: correlation coefficient matrix and coefficient of variance inflation As a result, there is no
49 multicollinearity phenomenon because the correlation coefficients are less than 0.8 and the variance inflation coefficients are all less than 10, specifically less than 4
To choose between OLS and FEM, run the F test F test checks whether fixed effects =0 or not Since p-value