Impact of macroeconomic volatility and economic policy uncertainty on credit growth and risk in vietnamese commercial banking sector

This study investigates the impacts of macroeconomic volatility, economic policy uncertainty on short-term, medium-term, and long-term credit growth, as well as the overall financial sta

INTRODUCTION

Research context

The current global macroeconomic environment is increasingly unfavorable, marked by a rise in the frequency, duration, and likelihood of economic shocks Climate change is a significant contributor to this instability, resulting in more extreme weather events that disrupt various sectors, particularly finance and banking Additionally, the ongoing pandemic and the Russo-Ukrainian War are anticipated to further exacerbate this volatility As a result, macroeconomic volatility has surged, with the Eurozone experiencing output growth volatility approximately five times greater than during previous peaks.

2009 Great Recession, and a surge in inflation volatility surpassing levels seen in the 1970s.

Recent global changes and political shifts have contributed to widespread uncertainty, particularly following the escalation of the Russia-Ukraine conflict on February 24, 2022, when Russia launched a military operation against Ukraine This conflict triggered sanctions from countries and organizations, including the EU and the U.S., significantly affecting the global economy and financial markets As a result, there has been a growing interest in researching the effects of Economic Policy Uncertainty (EPU) on the financial system, especially within the banking sector Studies have examined EPU's influence on various factors, including stock market returns, gold, commodities, Bitcoin, corporate decisions, and macroeconomic variables.

Macroeconomic variables significantly influence the economy and the banking sector, leading to both positive and negative effects A major downside is the rise of banking credit risk, which poses a serious threat to financial institutions The banking system is vital for economic growth, as it provides credit and encourages business investments Historical economic recessions, such as the U.S financial decline in the 1980s and the Asian crisis, highlight the critical nature of credit risk in the banking sector.

The credit crises of the 1990s and 2000s, along with the European credit crunch, highlight the widespread impact of financial instability on nations This underscores the critical importance of understanding credit risk and the necessity for thorough research to avert similar occurrences in the future.

Credit growth, which contrasts with credit risk, is influenced by both internal bank factors and macroeconomic conditions These elements play a crucial role in shaping the credit landscape within commercial banks (Huy et al., 2021; Raiter, 2021) For central banks, grasping the effects of macroeconomic instability on credit growth is vital for crafting effective policies and making informed regulatory decisions This knowledge aids in managing fluctuations in economic conditions and macroeconomic variables, ultimately fostering stable credit growth and guiding the central bank's policy-making and economic regulation efforts.

Research questions and objectives

Based on the proposed research rationales, this study poses the following research questions (RQ), aiming to provide a comprehensive insight into the studied subjects:

- RQ1: How does macroeconomic volatility affect the credit growth of commercial banks in Vietnam?

- RQ2: How does macroeconomic volatility affect the credit risk of commercial banks in Vietnam?

- RQ3: How docs economic policy uncertainty affect the credit growth of commercial banks in Vietnam?

- RQ4: How does economic policy uncertainty affect the credit risk of commercial banks in Vietnam?

This research aims to empirically analyze the impact of macroeconomic fluctuations and uncertainties on the credit expansion and financial risk of publicly traded commercial banks in Vietnam from 2012 to 2022 By addressing specific research questions, the study explores how these economic dynamics affect short-term, medium-term, and long-term credit growth.

Research Scope

Research Focus: The study concentrates on short-term credit growth, medium-term credit growth, long-term credit growth, financial stability index, economic policy uncertainty, and macroeconomic volatility.

Geographical Scope: The research encompasses financial indicators of commercial banks that are publicly traded on the Vietnam stock market.

Temporal Scope: The study covers a time frame extending from 2012 to 2022.

Research method

This study employs quantitative methodologies, particularly regression analysis on panel data, to examine how business models affect banking performance in relation to monetary policy and economic policy uncertainty Utilizing the Generalized Method of Moments (GMM), the analysis addresses potential endogeneity issues and ensures model stability.

The research utilizes audited financial statements and data from the General Statistics Office, focusing on 26 Vietnamese commercial banks listed on the HOSE, HNX, and UPCoM stock exchanges This study covers a comprehensive period from 2012 to 2022.

This study analyzes a dataset comprising information from 26 Vietnamese commercial banks that are listed on stock exchanges, utilizing data sourced from their audited financial reports and the General Statistics Office over the specified period.

Research contributions

This study explores the impact of macroeconomic volatility and economic policy uncertainty on credit growth and banking risk, offering significant theoretical and practical insights for the banking sector It develops a theoretical framework that examines the relationship between macroeconomic policies, uncertainty, credit growth, and banking risk, particularly within Vietnamese banks The findings reveal how macroeconomic instability and policy uncertainty affect commercial banks differently across various economic cycles, enhancing the current literature in this field.

This study utilizes the GMM model to analyze the impact of macroeconomic instability and economic policy uncertainty on credit growth and financial stability in commercial banks over short, medium, and long-term periods The findings offer valuable insights for policymakers and financial professionals to better manage macroeconomic and monetary policies, aiming to boost credit growth while mitigating risks in the financial sector Ultimately, this research enhances our understanding of the influence of macroeconomic factors on the operational efficiency of commercial banks.

LITERATURE REVIEWS

Theoretical background

2.1.1 Overview of Vietnam banking system

The State Bank of Vietnam (SBV) reports significant changes and improvements in the country's banking sector over the past few decades Before the 1990s, Vietnam's banking system was primarily state-owned, with the central bank maintaining centralized control and a monopoly on financial services.

In the 1990s, Vietnam initiated banking reforms that facilitated the establishment of both domestic and international banks, alongside the privatization of state-owned banks and the attraction of foreign investments This transformative era led to a remarkable increase in the number of operational banks, soaring from just 2 in 1990 to over 40 by the early 2000s Despite this rapid expansion, the banking sector encountered significant challenges, including poor management, insufficient capitalization, and a high rate of non-performing loans.

In the mid-2000s, the Vietnamese government initiated a comprehensive overhaul of the banking sector to address significant challenges, focusing on enhancing transparency, strengthening supervision, and minimizing the number of financially weak banks This restructuring involved mergers, acquisitions, recapitalization, and reforms aimed at improving management and risk governance As a result, the banking sector has experienced substantial improvements, evidenced by a decrease in non-performing loans from over 17% in 2012 to around 2% in 2020, alongside enhanced profitability and operational efficiency.

The Vietnamese government has actively promoted a diverse financial sector through the establishment of stock and insurance markets Over the past few decades, the banking sector has undergone significant development and progress, and ongoing governmental reforms are expected to further enhance its strength in the coming years.

Economic Policy Uncertainty (EPU) is a critical issue in today's global landscape, stemming from unpredictable changes in government policies, laws, and regulations that can disrupt economic activities and the business environment This uncertainty significantly impacts businesses and economies, particularly within the banking sector, as it affects investment, employment, and production decisions Essentially, EPU creates a policy environment where economic agents struggle to forecast the effects of fiscal, regulatory, monetary, and trade policies The EPU Index, as outlined by Knight, quantifies this uncertainty, highlighting its implications for economic stability.

(1921) and Bloom (2014), is a measurable, aggregate-level indicator that encapsulates both risk and uncertainty, making it a suitable gauge for uncertainty in research studies.

Baker et al (2016) define Economic Policy Uncertainty (EPU) as a metric reflecting the uncertainty faced by businesses, investors, and consumers regarding future economic conditions due to changes in economic policy The EPU is quantified by monitoring news and indicators related to taxation, monetary policy, trade policy, and regulation Understanding EPU is crucial for policymakers and researchers to evaluate the effects of economic policy and make informed decisions Drawing from earlier research, Baker et al developed an EPU Index that measures volatility through economic, market, policy, and news factors, calculated by averaging three components: news reports on economic uncertainty, expiring federal tax code provisions, and variability among economic forecasts They suggest that reputable news coverage on economic policy uncertainty enhances the comprehension of EPU indices.

The EPƯ Index, introduced by Baker et al (2016), represents a crucial development in understanding the connection between Economic Policy Uncertainty (EPU) and the operational efficiency of banks This research aims to determine the extent to which banks' business models affect this relationship.

Knightian uncertainty, defined as the difficulty in predicting future economic events due to a lack of information, significantly impacts banking operations Research indicates that during periods of heightened uncertainty, banks become more risk-averse, reducing their lending and investment activities (Huang, 2011) Additionally, banks may shift their focus from long-term strategies to short-term profit generation (Serra, 2018) Serra's study of 202 banks in the European Union highlighted that Knightian uncertainty negatively affects bank profitability, presenting challenges for banks in sustaining profit levels amid uncertain economic conditions.

The application of Knightian uncertainty theory in banking performance highlights that banks may respond to uncertain economic conditions by reducing lending, focusing on short-term profits, and facing declines in overall profitability Understanding the impact of this uncertainty is essential for policymakers and bank managers to prepare for and mitigate negative effects Research indicates a strong connection between increased economic policy uncertainty and lower bank profitability, alongside heightened banking risks Specifically, there is a negative correlation between economic policy uncertainty and key performance metrics such as return on assets and return on equity, while non-performing loans tend to rise This relationship arises because economic policy uncertainty can drive up funding costs and restrict lending activities, adversely affecting bank profitability and increasing the likelihood of loan defaults.

Macroeconomic volatility, a significant focus in research and monetary policy, is shaped by various factors According to Hammami and Lindahl (2014), the stability of the macroeconomic environment is closely tied to investment, growth, and labor productivity In contrast, Bleaney (1996) and Ismihan et al (2002) argue that volatility increases with fluctuations in critical economic indicators such as inflation rates and deficit-to-GNP ratios Additionally, Sameti et al (2012) define macroeconomic volatility by examining a range of indicators, including growth, budget deficits, current account deficits, inflation, and foreign exchange reserves.

Macroeconomic volatility reflects instability in key economic indicators like inflation, GDP, money supply, and foreign exchange reserves Significant variations between peak and trough values of these indicators signal increased volatility, leading to greater uncertainty in the economy, which can negatively impact overall performance The extent of these fluctuations serves as a measure of uncertainty and volatility, affecting various sectors, especially banking and finance.

In today's finance sector, evaluating credit risk is essential, as it represents the likelihood of a borrower defaulting on loan repayments The loan approval process hinges on various factors, including personal details, credit history, living conditions, and the applicant's loyalty to the lending institution Furthermore, credit risk encompasses the potential for borrowers to miss their financial obligations.

2000) articulates credit risk as "the risk of default on a debt that arises from a borrower failing to make the necessary payments".

A widely used method for estimating a company's bankruptcy risk is the structural approach, which evaluates the probability based on the company's capital structure A prominent example is Merton's Distance to Default (DD), derived from Merton’s 1974 bond valuation model and often utilized in frameworks like Moody's KMV This model innovatively applies Black and Scholes' option pricing theory to gauge bankruptcy risk, viewing a company's equity as an option on its assets.

Credit growth occurs when commercial banks enhance capital mobilization and increase lending, which is closely tied to rising labor income (Hammami & Lindahl, 2014) When credit growth exceeds expectations, it leads to higher worker incomes and stimulates overall economic growth De Nicolo (2000) defines credit growth as the annual change in total outstanding loans per bank, suggesting that a bank's financial stability can be assessed by comparing these loans against bankruptcy risks In essence, credit growth signifies the increase in the total value of loans issued in the current year compared to the previous year.

The theory of credit cycles examines the intricate link between lending growth and non-performing loans, highlighting a positive correlation where increased lending often leads to a rise in bad debts Keeton (1999) notes that during periods of economic expansion, commercial banks typically implement more lenient credit policies, resulting in relaxed credit standards and heightened debt levels Conversely, Jimenez et al suggest that this dynamic can shift during economic downturns, emphasizing the cyclical nature of credit behavior.

Prior studies

Numerous studies have examined the impact of Economic Policy Uncertainty (EPU) on credit growth, revealing varied effects across different contexts Bordo et al (2016) found that EPU negatively affects credit growth, particularly in the US commercial and industrial sectors In Italy, Alessandri and Bottero (2017) discovered that increased EPU results in lower loan application acceptance rates and a slowdown in capital infusion Chi and Li (2017) indicated that EPU raises bad debt rates and concentrates lending, heightening credit risk and reducing loan amounts Berger et al (2018) suggested that banks tend to hold more liquid assets during uncertain times, limiting credit availability for businesses and households Hu and Gong (2019) analyzed EPU's impact on bank credit growth across 19 major economies, finding that EPU decreases credit growth, especially in larger and riskier banks, while well-capitalized banks are less affected Barraza and Civelli (2019) noted that unexpected EPU spikes lead to reduced business loan supply due to decreased capital demand Finally, Nguyen et al (2020) demonstrated that high domestic EPU levels diminish bank credit growth, although fluctuations in EPU volatility may positively influence it.

Recent studies have highlighted the significant impact of macroeconomic factors and bank-specific characteristics on credit growth in Vietnam Research has focused on how macro factors influence banking credit risk, alongside micro factors inherent to individual banks Noteworthy contributions include Ngô Thị Lệ Diem’s (2019) analysis of credit growth determinants using regression models and Phan Quỳnh Linh's (2017) investigation into the factors affecting the credit growth of joint-stock commercial banks.

2.2.J Effects of Economic Policy Uncertainty' on the Credit Risk of Banks

EPU directly influences commercial banks by affecting their behavior in response to macroeconomic policy shocks, which is significant given their vital role in economic activities Banks adjust their lending strategies based on the economic climate, making credit support essential for business development However, there are times when banks cannot meet the funding needs of businesses due to resource constraints As a result, banks must prioritize credit allocation, focusing on customers with the greatest potential for growth and value.

Macroeconomic policies play a crucial role in helping commercial banks identify suitable business clients, as regulators have a broader perspective on economic development and industry strategies than individual banks By recognizing that different sectors contribute differently to economic growth, regulators implement various policies to promote industrial progress Furthermore, they refine these policies to ensure resources are allocated more efficiently to the targeted industries or enterprises, ultimately fostering macroeconomic advancement.

Commercial banks play a crucial role in economic growth by allocating capital resources, but their credit resource allocation is significantly affected by macroeconomic policies While stable economic policies can enhance credit allocation efficiency, frequent and substantial changes create Economic Policy Uncertainty (EPU), leaving banks uncertain about which industries or businesses regulators intend to support This uncertainty complicates the banks' ability to determine the most suitable sectors for credit resource allocation.

In such scenarios, economic policy could inadvertently direct banks to allocate scarce credit to industries or businesses with dim future prospects.

Since 2005, China's central and local governments have enacted various policies to regulate real estate prices, yet commercial banks face challenges in assessing the industry's prospects due to frequent policy changes and local government pushback This situation creates significant information asymmetry for banks in credit resource allocation, raising the risk of bad loans Consequently, an increase in Economic Policy Uncertainty (EPU) can directly heighten credit risk for financial institutions.

Hl Economic Policy Uncertainty negatively affects the credit risk of commercial banks in Vietnam

2.2.2 Effects of Macroeconomic Volatility' on the Credit Risk of Banks

Macroeconomic volatility significantly contributes to credit risk in commercial banks, as evidenced by various studies Research by Jimenez et al (2017) highlights the impact of countercyclical bank capital buffers on credit supply during different economic conditions Dell'Ariccia, Igan, and Laeven (2012) examine the interplay between credit booms and lending standards, revealing a strong correlation with macroeconomic fluctuations, particularly evident during the subprime mortgage crisis Salas and Saurina (2002) clarify the relationship between credit risk and macroeconomic volatility across different institutional contexts Furthermore, Berger and Bouwman (2013) explore how capital adjustments influence bank performance during financial crises, illustrating the banking sector's response to systemic economic changes.

Research underscores the complex interplay between macroeconomic volatility and the credit risk encountered by commercial banks It reveals how economic disturbances impact banking operations, including lending practices, credit allocation, and risk management, thereby shaping the overall economic environment Therefore, grasping these dynamics is essential for developing strategic policies that enhance the stability and resilience of the banking system amidst macroeconomic fluctuations.

H2 Macroeconomic volatility negatively affects the credit risk of commercial banks in Vietnam

2.2.3 Effects of Economic Policy Uncertainty on the Credit Growth of Banks

EPU has a profound effect on credit growth, especially in the commercial and industrial lending sectors in the United States, as identified by Bordo and colleagues in

A study by Alessandri and Bottero in 2017 found that increased Economic Policy Uncertainty (EPU) negatively impacts loan approval rates and delays capital infusion into the economy Chi and Li's 2017 research indicated that EPU leads to higher bad debt rates, increased lending concentration, and a shift in loan types, which raises credit risk and reduces loan sizes Additionally, Berger et al (2018) suggested that banks tend to accumulate cash reserves during uncertain times, holding more liquid assets and consequently limiting credit availability for businesses and households.

Research by Hu and Gong (2019) indicates that Economic Policy Uncertainty (EPU) negatively impacts bank credit growth across 19 major economies, with larger and riskier banks experiencing a more significant decline compared to those with higher liquidity and diversification Additionally, Barraza and Civelli (2019) found that unexpected increases in EPU result in a reduced supply of business loans, primarily due to decreased capital demand, highlighting that EPU shocks restrict funding to the business sector and subsequently affect the broader economy.

In a 2020 study by Nguyen et al., it was found that elevated domestic Economic Policy Uncertainty (EPU) levels are linked to a decline in bank credit growth Conversely, fluctuations in EPU volatility can positively influence credit growth The negative impact of EPU on credit growth is particularly pronounced in banking systems with high liquidity and profitability, while larger, well-capitalized, and riskier banks experience milder effects Additionally, global EPU levels adversely affect credit growth, with emerging economies facing more significant challenges compared to developed nations.

Caglayan and Xu's 2019 study, which analyzed data from 18 countries, revealed that Economic Policy Uncertainty (EPU) negatively affects credit levels Their research found that increased uncertainty leads to higher rates of non-performing loans and greater loan loss provisions, highlighting the detrimental impact of EPU on financial stability.

H3 Economic Policy Uncertainty negatively affects the credit growth of commercial banks in Vietnam

2.2.4 Effects of Macroeconomic Volatility^ on the Credit Growth of Ranks

Macroeconomic volatility significantly impacts the credit growth of commercial banks, often prompting more conservative lending practices during economic disruptions Empirical studies support this view, with Bordo et al (2016) illustrating the negative influence of Economic Policy Uncertainty (EPU) on credit growth, particularly in the commercial and industrial loan sectors in the United States Similarly, Aysan et al (2015) emphasize that key macroeconomic variables like real GDP and inflation critically shape bank lending behavior in Europe, highlighting the strong connection between economic health and credit expansion.

A 2020 study by Alessandri and Bottero highlights that increased Economic Policy Uncertainty (EPU) in Italy reduces loan application approval rates and hampers capital infusion into the economy Similarly, Nguyen and colleagues' research in the Asian context indicates that higher domestic EPU generally constrains bank credit growth, though they note that fluctuations in EPU can occasionally have a positive effect on credit growth under specific conditions.

Understanding the complex relationship between macroeconomic volatility and credit growth requires in-depth analysis across various geographical and empirical contexts This analysis is essential for developing strategies to effectively manage economic instability Additionally, this understanding is vital for enhancing the banking sector's role in sustainable economic development, enabling it to better withstand the fluctuations of macroeconomic conditions.

H4 Macroeconomic volatility negatively affects the credit growth of commercial banks in Vietnam.

RESEARCH DATA AND METHOD

Data

This study utilized a dataset derived from the audited financial reports of 26 commercial banks listed on the Vietnam stock exchange, covering the period from 2012 to 2022 To ensure the reliability of the research findings, banks with incomplete data were excluded, thereby minimizing the influence of the global economic crisis of 2008 on the study's results.

Table 3.1 Variables operationalization Sign Variable Calculation Data source(s) Studies

(ROA 4- Equity Audited financial Phúc el al Multiple)/Std(ROA) statements

Short-term (Credit balance of this credit growth year)/(Credil balance of year t-1)-1

Medium-term (Credit balance of this credit growth

Bordo et al (2016), Danisman et al

Bordo el al year)/(Credit balance of year t-3)-1 statements (2016),

(Credit balance of this Audited financial Bordo et al credit growth year)/(Credit balance of statements (2016), year t-5)-1 Danisman et al

VOLATILI Macroeconomic the three-year standard SBV, General Bordo el al

TY variability deviation of GDP growth, Statistics Office, (2016);

Inflatin, M2 money IMF, World Bank Danisman el supply al (2020)

EPU The Economic logarithm of the EPU http://www.policyun Bordo el al

Policy Uncertainty index certainty.com/ (2016);

CAP Capital Shareholders'Equity/ Audited financial Tuan (2018);

Ownership Total Assets statements Diem (2019);

SIZE Bank Size Ln(Total assets) Audited financial statements

Rabab (2015); Loc(2017); Sharma and Gounder (2012)

World bank Toan et al

STOCK Stock Market (VNI, - VNIt-i)/VNI,-i State bank Dan et al (2022)

GDP GDP Growth (GDP, - GDP,-1)/GDP,-1 World bank Dan et al (2022)

Research model

Drawing on the studies by Phuc el al (2020) and Danismir et al (2020), the authors developed a research framework to investigate the impacts of Economic Policy Uncertainty

(EPU) and macroeconomic volatility (VOLATILITY) on bank lending In this model, various types of credit growth - short-term, medium-term, and long-term (CGS, CGM,

CGL) - along with risk (measured by the Z-score), were used as dependent variables To address the research questions effectively, the authors have formulated two distinct models for analysis.

CREDITi,t=pO+pi CREDIT.,1-1+P2 EPUU-1+P3 VOLATILITY i,M*+CONTROLLi.t-i+Uit Z-Scoreụ = 00+01 Z-Scores,t-i+p2 EPƯU-1+ p3 VOLATILITYi,t-i*+CONTROLLi,t-i+Uit

Based on previous research (Bordo et al., 2016; Barraza and Civelli, 2019; Lee et al., 2017), the authors predict that increased economic policy uncertainty and macroeconomic volatility will hinder credit growth and elevate systemic risks within banks This prediction is summarized in four formulated research hypotheses for the study.

Z-score is a statistical metric used to assess a bank's financial well-being and estimate the probability of bankruptcy It incorporates a variety of financial ratios, such as liquidity, profitability, leverage, and solvency, offering a comprehensive evaluation of a bank's financial robustness (Dan et al., 2022).

Credit growth measures the increase in a bank's outstanding credit debt in terms of amount, quality, velocity, and scope over a specific timeframe This metric is essential for evaluating the year-over-year expansion of a bank's credit balance, providing insights into its lending capacity, customer acquisition effectiveness, and the implementation of its credit strategy.

Credit balance of this year/Credit balance of year T — 1

In which: T has values of 1, 3, 5 representing short-term, medium-term and long term credit growth, respectively.

Economic Policy Uncertainty (EPU), defined by Baker et al (2016), encompasses three main dimensions: the volume of media coverage on economic policy uncertainty, the unpredictability of expected tax code changes, and the variation in economic forecasts among experts EPU aims to measure the ambiguity associated with economic policy decisions, including the actions taken, the responsible entities, their timing, and the potential economic impacts.

Macroeconomic volatility, measured by the three-year standard deviations of GDP growth (GDPV), inflation rate (INFV), and M2 money supply (M2V), is crucial for assessing economic instability These indicators significantly impact the risk levels in banking operations and the broader financial system Variations in these metrics signal economic and financial instability, which can lead to increased risk accumulation within banks.

Banks that maintain a higher Capital Ownership Ratio (CAP) are generally more financially stable and better positioned to handle economic downturns, which can enhance their credit ratings and reduce financing costs However, the necessity for increased capital may lead to diminished returns on equity and lower overall profitability (Rehman et al., 2022).

Larger banks often enjoy economies of scale and possess diverse revenue streams, which can enhance their financial stability However, they also encounter stricter regulatory scrutiny and may be more vulnerable to systemic risks in the financial system (Kouzez, 2022).

The unemployment rate is a key economic indicator that reflects the percentage of the labor force that is currently unemployed It is calculated by dividing the number of unemployed individuals by the total labor force, providing insights into the health of the economy (Xie et al., 2022).

The performance of the stock market significantly influences bank operations by affecting investment values and capital costs A robust stock market can enhance bank valuations and reduce funding expenses, whereas a weak market can lead to diminished valuations and increased funding costs (Garel et al., 2020).

Economic growth significantly impacts banking efficiency by influencing loan demand and credit risk In times of robust economic expansion, borrowing tends to increase, resulting in elevated interest income for banks Conversely, during economic downturns, the risk of credit defaults escalates, which can lead to higher loan losses and diminished profits for financial institutions (Kouzez, 2022).

Econometric methods

The authors conducted a correlation analysis of the research model using a correlation matrix The Variance Inflation Factor (VIF) was employed to examine multicollinearity among the variables.

In panel data analysis, three prevalent methods are utilized: Pooled Ordinary Least Squares (Pooled OLS), Fixed Effects Model (FEM), and Random Effects Model (REM) The authors implemented these methods and subsequently conducted quantitative tests to determine the most suitable model An F-test was performed to compare FEM and Pooled OLS, with the hypothesis favoring Pooled OLS The Breusch-Pagan test was applied to differentiate between REM and OLS, positing that Pooled OLS is more appropriate Additionally, the Hausman test was used to assess the efficiency of REM over FEM.

After selecting the appropriate model, the authors conducted tests for heteroscedasticity, autocorrelation, and multicollinearity to identify and rectify any model deficiencies They utilized the Wooldridge test to detect autocorrelation, as its presence could compromise the reliability of the model results In cases of autocorrelation, they opted for methods that account for this issue to ensure accurate estimations The p-values from these tests were compared against significance levels of 1%, 5%, and 10% For assessing heteroscedasticity, the authors employed the Breusch and Pagan Lagrangian test; if detected, it indicated that the model estimates might be biased and inefficient To mitigate this, they implemented the Robust Standard Errors estimation method.

The authors employed the Feasible Generalized Least Squares (FGLS) method for verification, as established by Westerlund and Narayan in 2012 and 2015 To tackle endogeneity issues and other deficiencies, they utilized the Generalized Method of Moments (GMM) model However, due to study limitations, only the results from the GMM model were presented as the final outcome of the research.

RESEARCH RESULTS

Descriptive statistics

Table 4.1 presents descriptive statistics for the study's data sample, detailing the number of observations, average values, standard deviations, and the range of values for each variable To effectively manage outliers, the authors implemented a data cleansing process utilizing a winsorization technique, setting a threshold at the 5th and 95th percentiles.

Variable Obs Mean Std dev Min Max

Between 2012 and 2022, Vietnam's economy experienced notable fluctuations in credit growth, with average rates of 0.1936 for short-term, 0.7666 for medium-term, and 1.8996 for long-term credit The minimum and maximum values for short-term credit growth ranged from -0.2459 to 1.0682, medium-term from -0.3857 to 4.4494, and long-term from -1 to 11, highlighting the uneven development of the country's commercial banks The average Z-score of 23.7779, with a range of 6.5204 to 93.7675, indicates varying levels of financial stability among Vietnamese banks during this period These findings suggest significant growth potential and reinforce the reliability of the authors' model outcomes, reflecting the dynamic nature of Vietnam's banking sector.

The Economic Policy Uncertainty (EPU) in Vietnam has an average value of 5.219, with fluctuations between 4.71 and 5.68, reflecting an upward trend in 2021 and 2022 due to global and local uncertainties stemming from political, economic, and pandemic factors from 2019 to 2022 Inflation uncertainty averages 7.87%, while money supply volatility and GDP growth volatility stand at 1.70% and 2.63%, respectively, indicating significant instability compared to global benchmarks The ranges for inflation uncertainty (0.28% - 5.21%), GDP growth volatility (3.91% - 16%), and money supply volatility (0.42% - 7.47%) highlight the economic challenges faced by Vietnam between 2012 and 2022 Notably, both dependent and independent variables show considerable variation among commercial banks during this period, enhancing the reliability of the study's findings.

Correlation matrix

Zs CGS CGM CGL LNEPU INFV GDPV M2V SIZE CAP Unem GDP STOCK

Table 4.2 presents the results of the Pearson correlation analysis, indicating that most variable pairs exhibit correlation coefficients below 0.8 Notably, a strong correlation of 0.8574 is observed between macroeconomic variables M2V and INFV, which is expected due to the relationship between increased money supply and inflation The author asserts that this correlation does not undermine the research findings, supported by the absence of significant multicollinearity issues in the models, as noted by Dujarati (2011).

Multicollinearity checks

The multicollinearity test results indicate that all variables have a Variance Inflation Factor (VIF) of less than 10, with the highest VIF recorded at 7.29 This evidence confirms that the model is free from multicollinearity issues (Gujarati, 2011).

Effects of Economic Policy Uncertainty on the Credit Growth and Risks of Banks 31

Table 4.4 Effects of Economic Policy Uncertainty on the Credit Growth and Risks of Banks

The analysis reveals significant relationships between Economic Policy Uncertainty (EPU) and various credit growth metrics, with statistical significance indicated at the 1%, 5%, and 10% levels Specifically, Column 1 illustrates the impact of EPU on the bank's risk (Zs), while Columns 2, 3, and 4 demonstrate its effects on short-term (CGS), medium-term (CGM), and long-term credit growth (CGL), respectively Control variables such as capital (CAP), company size (SIZE), stock growth, GDP growth, and unemployment rate are also considered in the evaluation.

Table 4.5 illustrates the estimated effects of monetary policy uncertainty and economic policy instability on the credit risk and growth of commercial banks, utilizing the modified Generalized Method of Moments (GMM) approach as suggested by Windmeijer.

The analysis reveals that all models exhibit first-order autocorrelation, as indicated by AR(I) test p-values below the 5% significance level, while showing no second-order residual autocorrelation with AR(2) test p-values above 5% Furthermore, the Hansen test results demonstrate p-values exceeding the 10% significance threshold, confirming the appropriateness of the instrumental variables used Consistent with Roodman (2009), the p-values of these models are all above 0.25, and the maximum number of instruments employed is 24, remaining below the number of groups to ensure stability Additionally, all models achieve F-test p-values below 5%, validating their appropriateness Overall, these findings indicate that the conditions for consistency and validity of the models are adequately fulfilled.

The regression coefficient of EPU has a negative impact on the Z-scorc and is statistically significant at the 10% level, supporting the research perspective of Phuc el al.

High economic policy uncertainty negatively impacts the stability of the commercial banking system This uncertainty heightens the risk associated with loan portfolios, resulting in increased bad debts and a greater likelihood of defaults within the banking sector.

The regression coefficient of Economic Policy Uncertainty (EPU) has a significant negative impact on Credit Growth in the short-term, medium-term, and long-term, with statistical significance at the 1% level This finding aligns with the research conducted by Phuc et al (2020) and Gamze et al (2020) It indicates that heightened economic policy uncertainty leads to a decline in credit growth within the commercial banking system, as businesses tend to curtail their investment and borrowing activities during unstable economic periods Consequently, this reduction in credit growth not only affects the short-term but also has a cumulative effect that hampers long-term economic development.

The results from Table 4.5 satisfy the hypotheses (Hl and H3), meeting the expectations and answering the research questions posed by the authors.

Effects of Macroeconomic Volatility on the Credit Growth and Risks of Banks

Table 4.5 Effects of Macroeconomic Volatility on the Credit Growth and Risks of

The analysis presents the effects of Economic Policy Uncertainty (EPU) on various types of credit growth and risk, with statistical significance indicated at different levels Specifically, Column I highlights the influence of EPU on the bank's risk (Zs), while Column II details its impact on short-term credit growth (CGS) Column III examines the relationship between EPU and medium-term credit growth (CGM), and Column IV focuses on the effect of EPU on long-term credit growth (CGL) Control variables included in the study are capital (CAP), company size (SIZE), stock growth, GDP growth, and the unemployment rate.

Table 4.6 outlines the estimated effects of monetary and economic policy uncertainty on the risk and credit growth of commercial banks, employing the modified Generalized Method of Moments (GMM) approach by Windmeijer (2005) The analysis reveals that all models exhibit an AR(1) test p-value below 5% and an AR(2) test p-value above 5%, indicating first-order autocorrelation without second-order residual autocorrelation Moreover, the Hansen test results show p-values exceeding 10%, confirming the appropriateness of the instrumental variables used Consistent with Roodman (2009), the p-values for these models are all above 0.25 The estimation utilizes a maximum of 24 instruments, which is fewer than the number of groups, thereby ensuring model stability Additionally, all models demonstrate an F-test p-value below 5%, validating their suitability Overall, these findings affirm that the models' consistency and validity conditions are satisfactorily fulfilled.

The regression analysis reveals that the coefficients for GDP growth volatility (INFV) and inflation volatility (GDPV) significantly and negatively affect the Z-score at a 5% significance level, indicating that a unit increase in GDP growth volatility decreases bank stability by 36 points, while a unit increase in inflation volatility reduces it by 0.659 points This underscores the detrimental impact of instability in GDP growth and inflation on the commercial banking system Interestingly, contrary to expectations, the coefficient for money supply uncertainty (M2V) also shows a significant negative effect on Z-score, suggesting that a one-unit increase in money supply uncertainty enhances bank financial stability by 0.544 points This indicates that during periods of money supply uncertainty, commercial banks tend to restructure their loan portfolios and adopt a more cautious approach, thereby mitigating systemic banking risk Overall, the findings highlight that GDP growth volatility poses the most significant risk to commercial banks, emphasizing the critical need for growth targets in developing economies such as Vietnam.

The regression analysis indicates that GDP growth volatility has a significant negative impact on credit growth across short-term, medium-term, and long-term horizons, with a minimum statistical significance of 5% Specifically, a 1% increase in GDP growth volatility results in a decrease in short-term credit growth by 1.5%, medium-term by 4.8%, and long-term by 1% This underscores the detrimental effect of economic instability on bank lending Fluctuating GDP growth contributes to inefficient economic operations and low growth, which in turn hampers banks' lending activities due to unstable incomes and borrowers' repayment challenges Consequently, banks must exercise greater caution in customer assessments to mitigate the risk of increased bad debts, leading to lower credit growth compared to periods of economic stability.

Figure 4.1 Total credit and inflation fluctuations

Credit growth and inflation uncertainty are positively correlated, as illustrated in Figure 4.1 Significant fluctuations in inflation uncertainty, particularly during the 2010-2011 period, resulted in notable changes in credit growth; inflation volatility decreased from 8.66% to 6.25%, while credit growth plummeted from 42.3% to 13.9% The regression analysis indicates that inflation volatility (INFV) significantly impacts both short-term and medium-term credit growth at the 5% level, with a 1% increase in CPI volatility leading to a 0.027% rise in short-term credit growth and a 0.115% increase in medium-term credit growth This finding contradicts initial expectations and previous studies, suggesting that higher inflation uncertainty, which signals currency devaluation, makes loans appear less burdensome, thereby encouraging increased borrowing and credit growth.

The regression analysis reveals that M2V negatively influences medium-term credit growth while positively impacting long-term credit growth, significant at the 10% level Specifically, a 1% rise in money supply uncertainty leads to a 0.0549% decline in medium-term credit growth and a 0.14% increase in long-term credit growth These findings indicate that monetary policy uncertainties significantly affect credit growth in the short to medium term, but their impact diminishes over the long term Furthermore, increased monetary policy uncertainty adversely affects the credit growth of commercial banks, as unstable policies can lead to fluctuating loan interest rates, making borrowers more cautious and inclined to borrow only at lower costs Consequently, it is essential for commercial banks to adopt effective strategies to promote credit growth.

In summary, uncertainty in GDP growth significantly affects banks' risk and credit growth, with the strongest impacts observed during the initial phase of macroeconomic instability, particularly in the medium term, while these effects tend to lessen over time The findings presented in Table 4.6 largely confirm the hypotheses (H2 and H4), aligning with the authors' expectations and effectively addressing the research questions.

DISCUSSION

Discussion

The study highlights the relationship between factors affecting credit growth and the risks faced by commercial banks in Vietnam, particularly in light of macroeconomic instability and uncertain economic policies By employing the GMM regression model, the research successfully validated its hypotheses, leading to significant conclusions relevant to the Vietnamese banking sector.

Rising economic policy uncertainty heightens financial risks in the commercial banking sector, negatively affecting banks' risk profiles due to the ambiguity in macroeconomic management policies.

Economic uncertainty leads to reduced credit growth, negatively impacting long-term economic development This relationship is cumulative, worsening over time, with GDP growth identified as the most significant factor influencing credit growth Maintaining economic growth is crucial, particularly in developing economies, as the effects of these variables are most significant in the short to medium term, gradually lessening in the long term.

Limitations and future research directions

While the research offers valuable insights, it faces limitations due to its decade-long timeframe, which may not provide a comprehensive analysis The study's reliance on data from only 26 commercial banks limits its scope in assessing the effects of economic instability and policy uncertainty Future research should aim to expand its duration to 20 to 30 years to capture long-term trends and impacts, and incorporate a larger number of banks Additionally, including various interaction and dummy variables could enhance the analysis, facilitating a more objective exploration of the relevant issues.

Implications from Economic Policy Uncertainty

High Economic Policy Uncertainty (EPU) can hinder banks from growing their revenue streams due to heightened volatility and economic instability To help banks navigate these challenges and boost their revenue in uncertain times, collaboration between the government and commercial banks is crucial.

To address the challenges of high Economic Policy Uncertainty (EPU) levels, the Vietnamese government can foster innovation and entrepreneurship through several strategic initiatives By providing financial incentives such as tax reductions, subsidies, and loans, the government can stimulate new business ventures and drive economic innovation Additionally, implementing policies that reduce bureaucratic barriers, ease trade restrictions, and clarify intellectual property rights will create a more favorable environment for startups Promoting collaboration between startups, entrepreneurs, and established businesses can enhance knowledge sharing and innovation Furthermore, cultivating a culture of innovation and encouraging entrepreneurship as a viable career option, along with promoting creativity and risk-taking in education, will strengthen the entrepreneurial ecosystem Lastly, encouraging banks to develop tailored products and services for businesses and individuals navigating uncertainty can further support this initiative.

The government and central bank play a vital role in providing support and financial assistance to banks and financial institutions during periods of economic uncertainty and instability By implementing measures such as loan guarantees and liquidity support, they can stabilize the financial system and mitigate the adverse effects of economic stress on financial markets and the broader economy These policy interventions are essential for ensuring the resilience of financial institutions in challenging economic conditions.

To address the rising risks of economic instability, banks must enhance their risk management practices, which can significantly mitigate the effects of economic uncertainty Governments and central banks should strengthen the legal framework and supervision to promote these activities, as studies indicate that stricter oversight can reduce the likelihood of bank failures during crises and minimize systemic vulnerabilities In addition to regulatory measures, banks should adopt risk management techniques such as stress testing and scenario analysis Regulatory bodies can further encourage these practices by enforcing stricter risk management regulations, mandating regular stress tests, and providing incentives for effective risk management Ultimately, improving risk management practices is essential for financial institutions to navigate economic uncertainties and ensure financial stability.

Implications from Macroeconomic Volatility

The study reveals that inflation uncertainty notably impacts the rise in credit growth and the risks faced by commercial banks During periods of high inflation, the instability of currency value necessitates increased capital for production investments and consumption, leading to a surge in borrowing Consequently, banks must adopt credit control measures to mitigate the risk of poor-quality loans While inflation volatility can stimulate credit activities, implementing policies to manage inflation at appropriate levels can enhance credit growth and foster sustainable economic development.

Before establishing credit growth targets for the upcoming year, commercial banks must assess the current inflation landscape and project future trends in alignment with the State Bank of Vietnam's economic regulations It is crucial for banks to set appropriate interest rates, as high inflation diminishes currency value and increases the demand for funds among customers for investment and production However, excessively high lending rates may drive customers to seek alternatives to bank loans To mitigate risks, banks should strengthen their credit appraisal processes to verify loan purposes and borrowers' repayment abilities Furthermore, investing in training and improving the quality of their workforce, particularly credit officers, is essential for overall performance.

The State Bank of Vietnam (SBV) should adopt a flexible approach to managing monetary policy while closely coordinating with fiscal and macroeconomic policies to effectively control inflation and support economic growth Additionally, state agencies must proactively create price management plans and monitor price trends to manage inflation efficiently It is crucial for ministries, provincial people's committees, and relevant government bodies to keep a close eye on the price movements of essential goods, enabling timely adjustments to strategies that ensure supply meets demand, particularly during festive seasons, to prevent excessive inflation.

To effectively address the rising risks linked to economic instability, banks must bolster their risk management strategies By improving these practices, financial institutions can better navigate uncertainties and uphold financial stability.

The study reveals that fluctuations in GDP growth negatively impact credit growth and the risk profile of banks As competition intensifies, the demand for loans from businesses rises, prompting commercial banks to adopt strategies that boost competitiveness while effectively managing credit growth To address these challenges, several key recommendations are proposed.

Commercial banks must maintain or moderately increase their capital to ensure a minimum safety ratio while facilitating credit growth To enhance credit quality, it is essential to tighten the credit risk management system and improve the handling of non-performing loans Additionally, expanding distribution channels for consumer credit and diversifying banking products—particularly by focusing on high-tech and unique offerings—can provide a significant competitive advantage.

The State Bank of Vietnam (SBV) must regulate both the growth and quality of credit within the economy, ensuring that prolonged periods of loose monetary policy are avoided, particularly during crises It is essential to implement market-driven strategies that guide credit towards the production sector.

Commercial banks must prioritize effective capital management and high-quality credit services, while the central bank should steer monetary policy to achieve a balance between growth and quality, particularly during periods of economic uncertainty.

Research shows that fluctuations in the money supply significantly affect credit growth and associated risks When the money supply increases, banks are better positioned to broaden their credit offerings to a wider array of businesses and individuals within the economy.

To navigate the fluctuations in money supply dictated by the SBV, commercial banks must strategically and selectively expand credit to optimize loan interest profits and minimize capital waste It is essential for these banks to actively manage their capital and maintain a robust available fund, ensuring that SBV's monetary policies have minimal impact on their operations Additionally, developing effective capital mobilization strategies is crucial for establishing a stable and consistently growing capital base to support ongoing business activities.

The State Bank of Vietnam (SBV) should adopt a flexible approach to monetary policy to maintain money supply stability and foster credit growth This involves establishing clear strategies for adjusting the money supply to enhance credit efficiency while managing monetary tools and liquidity effectively By synchronizing monetary, credit, and liquidity measures, the SBV can ensure a stable money circulation, stabilize markets, and promote sustainable growth Additionally, it is essential for the SBV to monitor market interest rates closely, allowing for timely adjustments that align with macroeconomic conditions and monetary policy objectives Continued efforts to lower loan interest rates will support production and business activities, contributing to a sustainable recovery and improved credit quality while mitigating credit risk.

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Variable Obs Mean std dev Min Max

Zs CGS car CGL LNEPU INFV GDPV M2V SIZE CAP Unempl-t GDP

GMM regression results with independent variable EPU

Dynamic panel-data estimation, two-step system GM'-i

Number of obs = Number of groups =

Obs per• group: min = avg = max = t p>ltl [95% conf interval]

Warning: Uncorrected two-step standard errors are unreliable.

Instruments for first differences equation

D.(SIZE CAP Unemployment GDP EPU)

GMM-type (missing=0, separate instruments for each period unless collapsed) L7.(L2.Zs L3.STOCK)

SIZE CAP Unemployment GDP EPU

GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(L2.Zs L3.STOCK)

Arellano-Bond test for AR(1) in first differences: z = -1.03 Pr > z = 0.035 Arellano-Bond test for AR(2) in first differences: z = 0.91 Pr > z = 0.361

Sargan test of overid restrictions: chi2(4) = 1.30 Prob > chi2 = 0.862 (Not robust, but not weakened by many instruments.)

Hansen test of overid restrictions: chi2(4) = 7.11 Prob > chi2 = 0.130 (Robust, but weakened by many instruments.)

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

Hansen test excluding group: chi2(l) = 0.03 Prob > chi2 = 0.873Difference (null H = exogenous): chi2(3) = 7.09 Prob > chi2 = 0.069

Number Number obs per of obs = of groups = ' group: min = avg = max =

CGS Coefficient std err t p>ltl [95% conf interval]

Gi-W-type (missing=0, separate instruments for each period unless collapsed) L(7/8).(CGS L2XGS L2.LNEPU)

GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(CGS L2.CGS L2.LNEPU)

Hansen test excluding group: chi2(8) = 16.58 Prob > chi2 = 0.035 Difference (null H = exogenous): chi2(8) = 3.20 Prob > chi2 = 0.921 iv(GDP STOCK)

Hansen test excluding group: chi2(14) = 16.68 Prob > chi2 = 0.273Difference (null H = exogenous): chi2(2) = 3.09 Prob > chi2 = 0.214

Dynamic panel-data estimation, two-step system GMM

Number of obs = Number of groups = obs per group: min = avg = max =

CGM Coefficient std err z p>|z| [95% conf interval]

D.(SIZE CAP Unemployment GDP STOCK)

GMM-type (missing=0, separate instruments for each period unless collapsed) L7.(CGM L2.CGM L2.LNEPU)

SIZE CAP Unemployment GDP STOCK

GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(CGM L2.CGM L2.LNEPU)

Hansen test excluding group: chi2(6) = 9.88 Prob > chi2 = 0.130 Difference (null H = exogenous): chi2(8) = 8.09 Prob > chi2 = 0.424 iv(SIZE CAP Unemployment GDP STOCK)

Group variable: id Number of obs = 257

Time variable : Year Number of groups = 26

Number of instruments = 22 Obs per group: min = 8

CGL Coefficient std err z p>|z| [95% conf interval]

D.(SIZE CAP Unemployment GDP STOCK)

GrY^-type (missing=0, separate instruments for each period unless collapsed) L7.(CGL L2.CGL L2.LNEPU)

SIZE CAP Unemployment GDP STOCK

GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(CGL L2.CGL L2.LNEPU)

Arellano-Bond test for AR(1) in first differences: z = -2.10 Pr > z = 0.035 Arellano-Bond test for AR(2) in first differences: z = -1.90 Pr > z = 0.058

Hansen test excluding group: chi2(6) = 7.30 Prob > chi2 = 0.294 Difference (null H = exogenous): chi2(8) = 13.01 Prob > chi2 = 0.111 iv(SIZE CAP Unemployment GDP STOCK)

Hansen test excluding group: chi2(9) = 8.29 Prob > chi2 = 0.506 Difference (null H = exogenous): chi2(5) = 12.03 Prob > chi2 = 0.034

GMM regression results with independent variable Economic voltality

Number of obs = Number of groups = Obs per group: min = avg = max =

Zs Coefficient std err t p>ltl [95% conf interval]

IO./0/0 zz.Dơ/zo Ơ 03 0.41/ -z/.300^0 OD.±zz±/

GMM-type (missing=0, separate instruments for each period unless collapsed) L(7/8).(Zs L2.Zs L2.INFV L2.CAP L2.M2V)

GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(Zs L2.ZS L2.INFV L2.CAP L2.M2V)

Number of obs Number of groups obs per group: min avg max

CGS Coefficient std err t p>ltl [95% conf interval]

GMM-type (missing=0, separate instruments for each period unless collapsed) L7.(CGS L.CGS L.GDPV L.INFV L.M2V)

GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(CGS L.CGS L.GDPV L.INFV L.M2V)

Number of obs = Number of groups = obs per group: min = avg = max =

CGM Coefficient std err t p>ltl [95% conf interval]

GMM-type (missing=0, separate instruments for each period unless collapsed) L7.(CGM L.CGM L.INFV L.M2V L.GDPV)

GMM-type (missing=0, separate instruments for each period unless collapsed) DL6.(CGM L.CGM L.INFV L.M2V L.GDPV)

Hansen test of overid restrictions: chi2(12) = 6.76 Prob > chi2 = 0.873(Robust, but weakened by many instruments.)

Number Number obs per of obs = of groups =

CGL Coefficient std err t p>ltl [95% conf interval]

Tiêu đề	Impact of macroeconomic volatility and economic policy uncertainty on credit growth and risk in vietnamese commercial banking sector
Trường học	Trường Đại Học Kinh Tế TP. Hồ Chí Minh
Chuyên ngành	Tài chính - Ngân hàng
Thể loại	Báo cáo tổng kết
Năm xuất bản	2024
Thành phố	Ho Chi Minh

Định dạng
Số trang	64
Dung lượng	1,37 MB