The Impact of Macroeconomic Factors on Financial Stability: Evidence from OECD Countries

The financial system is considered the main pillar of an economy’s functioning. If the financial system faces risks or simply does not operate properly, the consequences can manifest as problems throughout the entire economy, potentially leading to financial crises and slowing economic growth. For this reason, researchers have consistently devoted special attention to the financial stability of countries [1]. For countries aiming to achieve sustainable economic development, promoting financial stability is one of the main priorities [2].

In most countries, the banking sector is a key component of the financial system and plays a crucial role in sustainable economic development. Therefore, financial stability depends largely on the stability of the banking system, which is often measured using sustainability indicators. Among these, the Bank Z-score is one of the most widely recognized measures for assessing financial stability. This indicator estimates the probability of a bank’s bankruptcy and helps identify institutions facing financial difficulties [3]. Additionally, the Bank Z-score is used to evaluate banking efficiency: banks with lower Z-score values tend to be less efficient in managing and allocating their capital. Conversely, higher Z-score values indicate a lower risk of bankruptcy, reflecting a more stable banking system that is more resilient to financial shocks.

Given the great importance of this topic, numerous theoretical and practical studies have been conducted to analyze the relationship between macroeconomic factors and financial stability. According to the literature, some of the main factors that affect financial stability include inflation, interest rates, exchange rates, the non-performing loans ratio, and other economic factors. Although a significant number of studies exist on this topic, most have not focused comprehensively on OECD countries.

Diaconu and Oanea [4] studied the factors influencing the stability of the banking system in Romania during the period 2008–2012, using the Z-score index as an indicator of banking stability. The results indicated that GDP growth had a positive and statistically significant impact on banking system stability, suggesting that a growing economy fosters more favorable conditions for the financial system. Similarly, Abbas et al. [5] examined the relationship between GDP and financial stability in Pakistan during the period 2010–2016. Their findings also revealed a positive and significant relationship, indicating that increases in GDP contribute to improved financial stability within the banking sector.

A study conducted in India by Dhal et al. [6] analyzed the relationship between macroeconomic factors and financial stability. The findings indicate that high levels of inflation can negatively affect financial stability by creating uncertainty and impairing the performance of the banking sector. Similarly, Ullah et al. [7] argue that a stable financial system depends on a stable macroeconomic environment characterized by low and sustainable inflation rates, as large fluctuations can increase the risks faced by financial institutions. Another study examining the impact of inflation on financial stability is that of Bittencourt [8], conducted in Brazil, which found that high inflation rates had adverse effects on the country’s financial development during the period 1985–2002. Likewise, Alsamara et al. [9] emphasized that high inflation poses a potential threat to the financial stability of banks, as it undermines their ability to manage assets and liabilities efficiently.

Another macroeconomic factor that significantly influences financial stability—particularly in the banking sector due to the nature of banks' operations—is the interest rate. A study conducted in Indonesia by Hudaya and Firmansyah [10] concluded that interest rates have a positive effect on financial stability. According to their findings, an increase in interest rates can enhance financial stability by boosting banks’ profitability and reducing the risks they face. However, Apostolakis and Papadopoulos [11] found a negative correlation between interest rates and financial stability, suggesting that rising interest rates may lead to increased uncertainty within banking institutions. In line with this, Rajha [12] argues that high lending rates impair borrowers' ability to meet their financial obligations, resulting in a higher level of non-performing loans. This, in turn, poses a major threat to financial stability, as it adversely affects the liquidity and overall financial performance of banks.

The exchange rate is another key macroeconomic factor that significantly influences financial stability, particularly within the banking sector. A study conducted in Indonesia by Winpor and Hidayet [13] confirmed a positive relationship between exchange rate stability and financial stability. A stable exchange rate supports effective risk management, preserves bank liquidity, and fosters a predictable environment for international transactions. Conversely, sharp fluctuations in the exchange rate can heighten uncertainty and increase risk exposure for banks holding foreign currency liabilities, thereby undermining their financial soundness.

Another important factor affecting financial stability is the unemployment rate. High levels of unemployment can undermine financial stability by reducing individuals' disposable income, which may, in turn, lead to higher levels of non-performing loans and lower consumer spending. A study by Elia et al. [14], which examined the impact of macroeconomic variables on the stability of the Lebanese banking system during the period 2009–2018, concluded that such macroeconomic indicators play a crucial role in determining financial stability. Among these factors, the unemployment rate has been found to be negatively associated with financial stability, as measured by the Z-score. This is primarily because rising unemployment reduces individuals’ income streams, limiting their ability to repay loans. Consequently, the risk of default increases, leading to a higher level of non-performing loans, which in turn undermines the financial stability of banks. An increase in non-performing loans (NPLs) weakens the performance of financial institutions, thereby hindering economic growth. A rising level of NPLs can trigger instability in the banking sector and contribute to poor economic performance. It has been suggested that NPLs are among the primary causes of economic failure, as each non-performing loan represents a potential loss for the financial system [15]. Furthermore, an increase in NPLs may lead to higher interest rates on loans, which can reduce bank profitability and increase borrowing costs [16]. The effects of NPLs extend beyond the banking system, impacting the economy as a whole. If the initial adverse effects of these loans are not properly managed, they may escalate into full-blown financial crises. Additionally, NPL-induced increases in interest rates can reduce credit access, raise operational costs, and contribute to inflationary pressures.

The paper examines the impact of several macroeconomic factors—namely Gross Domestic Product (GDP), inflation, interest rates, unemployment rate, and exchange rate—on the financial stability of the banking sector, measured using the Z-Score indicator. Additionally, the analysis includes the non-performing loan ratio, which is considered one of the key factors affecting banks’ financial stability.

4.1 Descriptive statistics

The variables included in the study were analyzed over a 16-year period, from 2008 to 2023. The study sample comprised 27 OECD countries, resulting in a total of 432 observations.

Table 2 below presents descriptive statistics for the study variables, including the mean, minimum, maximum, and standard deviation values.

Table 2. Descriptive statistics for study variables

Source: Author's calculations

In this study, financial stability is measured through the Bank Z-score, which recorded an average value of 16.07 during the analyzed period. The lowest value observed was -0.3, while the maximum reached 57.4.

Economic growth, represented by GDP, averaged 1.87% during the study period. The GDP of the studied countries fluctuated between a minimum of -10.84% and a maximum of 24.62%.

Inflation averaged 3.13%, with a minimum value of -4.45% and a maximum value reaching as high as 72.31%.

The annual change in the exchange rate averaged 2.33%, with fluctuations ranging from a minimum decrease of -96.62% to a maximum increase of 87%.

The unemployment rate averaged 7.63%, ranging from a minimum of 2.02% to a maximum of 27.69%.

Non-performing loans averaged 3.90%, with values ranging from a minimum of 0.1% to a maximum of 46%.

4.2 Interpretation of the results of the econometric model

To assess the impact of macroeconomic variables on the financial stability of OECD member countries, this study used panel data and applied several econometric models, namely the Fixed Effects Model (FEM), the Random Effects Model (REM) and the Panel Corrected Standard Errors Model (PCSE). Initially, the FEM and REM models were implemented, the results of which are presented in Table 3. After performing diagnostic tests, the presence of autocorrelation and heteroscedasticity was identified, therefore, robust versions of these models were used in order to address these issues and increase the reliability of the results.

The following table (Table 3) presents the results of diagnostic tests, which reveal the presence of autocorrelation and heteroskedasticity in the model. This indicates that the model residuals are not independent and exhibit unstable variance, potentially compromising the accuracy and reliability of econometric estimates. Therefore, it is necessary to address these issues by applying more robust analytical methods.

Table 3. Diagnostic test results

Source: Author's calculations

The data obtained from these improved model versions, along with the comparison between them, are presented in the Table 4.

According to the results of the Fixed Effects Model, the R² value is 0.0248, indicating a low explanatory power of the independent variables on the dependent variable, i.e., a limited ability of the model to explain changes in financial stability. Although the R² values (~0.03) from the FEM and REM models are low, this may result from omitted relevant variables, measurement errors, or the indirect effects of macroeconomic factors on financial stability.

Despite the limited explanatory power, the analysis focuses on the statistical significance of the coefficients to capture meaningful relationships. However, the analysis of the variable coefficients provides valuable insights. Gross Domestic Product (GDP) shows a positive relationship with financial stability, consistent with existing literature, suggesting that economic growth strengthens the financial sector. Conversely, inflation, interest rates, and unemployment exhibit a negative impact, reflecting the destabilizing effects these variables may have on the banking system. Meanwhile, the exchange rate and the non-performing loan ratio are statistically insignificant in this model. The Random Effects Models confirm similar general directions of influence but differ in the statistical significance of some variables — specifically, the exchange rate shows a significant positive impact, while the negative effect of interest rates is not statistically significant. These differences emphasize the importance of careful model selection when interpreting the results.

Table 4. Summary of results from FEM and REM methods

Note: *p<0.1; **p<0.05; ***p<0.01. Source: Author's calculations in Stata program

Table 5. PCSE model results

Note: *p<0.1; **p<0.05; ***p<0.01.

Source: Author's calculations in Stata program

In both the Fixed Effects and Random Effects models, the low R² values did not indicate a strong fit for the econometric specification. Moreover, diagnostic tests revealed the presence of heteroskedasticity across panels and first-order autocorrelation within panels. While the Random Effects Model (REM) assumes homoskedasticity and no autocorrelation, the Panel-Corrected Standard Errors (PCSE) approach is specifically designed to address such violations by producing unbiased coefficient estimates and robust standard errors. PCSE is particularly well-suited for panels where the cross-sectional dimension (N) exceeds the time dimension (T), as is the case with our dataset (27 countries over 16 years). By correcting for both heteroskedasticity and autocorrelation, PCSE improves the efficiency and reliability of the estimates, thereby enabling a clearer and more accurate interpretation of the relationship between macroeconomic variables and financial stability. The results obtained using PCSE are presented separately in Table 5, and the study’s conclusions are based on these results due to their greater methodological robustness.

Based on the results of this method, the econometric model presented at the beginning looks as follows:

Z – score = α + .025658GDPGrowth_it -0781337CPI_it - .1100211InterestRate_it -.2426356ExchRateChange_it + .0301087UnempRate_it -.0083296NPL_it + ε_it

The results show a positive relationship between GDP and financial stability, indicating that an increase in GDP tends to improve financial stability. However, according to our study, this relationship is not statistically significant, meaning that GDP cannot be considered a key variable in our analysis.

The results of the analysis show a significant negative relationship between inflation and financial stability. This means that rising inflation is associated with a deterioration in financial stability, implying that high inflation levels can destabilize the banking system. This can be explained by the fact that increasing inflation raises the cost of financing, leading to higher loan defaults and harming the financial performance of banks. These findings are consistent with the majority of studies conducted on this topic [7, 9, 19, 20, 25].

Interest rates also show a negative relationship with financial stability, meaning that an increase in interest rates is associated with a decrease in financial stability. Higher interest rates impose a greater financial burden on borrowers, thereby increasing the likelihood of loan defaults. This negatively impacts the financial performance of banks by increasing the volume of non-performing loans and contributing to the destabilization of the banking system. This finding, which highlights a negative relationship between interest rates and financial stability, is widely supported in the existing literature and by most studies in the field [11, 12]. However, some studies have found this relationship to be positive [10].

Another macroeconomic variable, the unemployment rate, has also shown a negative relationship with financial stability, which is expected since unemployment reduces individuals’ ability to repay loans. This situation adversely affects the financial performance of banks and contributes to the destabilization of the banking system [21].

The exchange rate has shown a positive relationship with the financial stability of banks, indicating that favorable changes in the exchange rate help banks better manage currency risks. This reduces the costs associated with volatility and creates a more stable financial environment, thereby increasing investor and depositor confidence, which in turn enhances banks' financial performance. This positive relationship is supported by a growing body of literature [13].

Non-performing loans, as one of the most important specific factors affecting the financial stability of commercial banks, have resulted in our study to have a negative relationship with financial stability, although this relationship is not statistically significant. This result may be related to the characteristics of the study sample, as in the countries included, the ratio of non-performing loans has generally been low, which suggests that commercial banks have adopted effective credit risk management policies. However, such a negative relationship between non-performing loans and bank financial stability has broad support in the existing literature [14-16].

After interpreting the analysis results, the model’s suitability is evaluated through diagnostic tests.

To assess the overall goodness of fit of the model, a joint significance test (Wald test) was conducted. This test evaluates whether a set of coefficients are equal to zero. The hypotheses tested are as follows:

Null hypothesis (H₀): All coefficients of the variables selected in the study are equal to zero, meaning they have no significant impact on the model.

Alternative hypothesis (H₁): At least one of the coefficients is different from zero, indicating a significant impact on the model.

According to the table below, which presents the test results, the Chi²(6) value of 130.59 demonstrates strong evidence to reject the null hypothesis. Furthermore, the p-value less than 0.05 confirms that the null hypothesis can be rejected with a high level of confidence.

Table 6. Model fit test

Source: Author's calculations in Stata program

The results of this test, presented in Table 6, show that the variables collectively have a significant impact on the dependent variable. According to the results shown in Table 5, four out of the six study variables have a statistically significant impact on the model. Therefore, the model is valid and appropriate for analyzing the impact of macroeconomic variables on the financial stability of OECD countries.

After conducting the Wald test, which confirmed that the coefficients of the independent variables were significant and different from zero, a multicollinearity test among the independent variables was also performed. This test was carried out using Variance Inflation Factors (VIF) to ensure that there was no high correlation between the independent variables that could compromise the accuracy of the model.

The results of this test, presented in Table 7, indicate that there is no presence of multicollinearity among the independent variables. All VIF values are below 10, which means that each independent variable can contribute independently to explaining the dependent variable.

Table 7. Multicollinearity test

Source: Author's calculations in Stata program

The summary of the Wald test and multicollinearity test results confirms that the PCSE method is appropriate for our study’s data, ensuring accurate and reliable modeling, with analysis results that are valid and supported by the literature.