1. Introduction
The global economy has witnessed sharp increases in inflation rates since 2022, leading to a proactive response from central banks worldwide. To combat rising inflation, major central banks have raised their policy rates, marking a departure from the low-interest-rate environments observed prior to the ongoing Ukraine–Russia conflict. Notably, the South African Reserve Bank (SARB) raised its policy rate from 3.5% in July 2020 to 7.25% in January 2023, with projections indicating a continued interest rate hiking cycle due to persistent high inflation (
Majola 2023). This shift in policy has sparked debates regarding the influence of interest rates on bank risk-taking, particularly in an economic environment characterized by elevated inflation and subdued economic activity. These discussions have been further fueled by the recent collapses of Silicon Valley Bank (SVB) and Signature Bank in the United States.
Given the inherent operational structure of banks, which involves short-term borrowing and long-term lending (
Bednar and Elamin 2014), knowing the impact of rising interest rates on risk-taking behavior for South African banks is of concern considering the surge in government securities holdings by commercial banks since the 2008/2009 global financial crisis which exposes these banks to financial risks (
Hesse and Miyajima 2022). Further considering that the SARB has included macroprudential policies amongst its monetary policy toolbox, knowing whether these policies are achieving their purpose of safeguarding financial stability in the banking sector amidst the changes in interest rates, is important for policymakers, regulators and market participants.
However, the current academic literature gives contradicting insights into the relationship between higher interest rates and bank risk-taking behavior. On one hand, some authors argue that an increase in interest rates increases banking risks via its negative effects on the balance sheets and income statements of banks, as liabilities rise while the value of assets declines (
Porcellacchia 2020). On the other hand, others argue that increased interest rates lead to higher bank revenues and expected net returns on safe assets (such as government bonds) which could minimize investments in riskier assets (
De Nicolò et al. 2010;
Claessens et al. 2018). Moreover, the existing empirical literature mostly focuses on the impact of low-interest-rate environments on bank risk-taking behavior in advanced economies that have implemented zero-interest-rate policies (ZIRPs). Notably, these studies use linear econometric tools in their empirical analysis despite evidence of the impact of interest rates on bank risk-taking varying between different monetary policy stances and levels of bank capitalization or leverage effects (
Dell’Ariccia and Marquez 2013;
Dell’Ariccia et al. 2014;
Buch et al. 2014;
Jiménez et al. 2014;
Özşuca and Akbostancı 2016;
Chen et al. 2017;
Bonfim and Soares 2018;
Brana et al. 2019;
Bubeck et al. 2020).
Our study investigates the nonlinear relationship between interest rates and bank risk-taking in South Africa, an emerging market economy that has not implemented a ZIRP (zero-interest-rate policy) and lacks empirical research on the subject for the country. To motivate our study,
Figure 1 shows the trends in the lending rate (proxy for interest rates) and non-performing loans, liquid asset as a ratio of short-term liabilities and unsecured lending which are bank risk-taking indicators. Although we observe that liquid asset ratios (non-performing loans and unsecured lending) have generally decreased (increased) between the GFC and the more recent COVID-19, which indicates an increase in bank risk-taking behavior, we observe some asymmetric properties in the data that require empirical attention. Firstly, we observe outliers in the data, particularly during periods of crisis, which are indicative of location asymmetries. Secondly, we observe different cyclical patterns in the data, with liquid asset ratios and non-performing loans (unsecured lending) having sharper and shorter (smoother and longer) cycles. Therefore, accounting for location and cyclical asymmetries is crucial when investigating the impact of interest rates on bank risk-taking behavior.
To explore the nonlinear effects of interest rates on bank risk-taking in South Africa, we use two econometric models that capture different forms of asymmetries. Firstly, we use the nonlinear ARDL (NARDL) technique proposed by
Shin et al. (
2014). This technique allows for the decomposition of the independent variable of interest (interest rates, in this case) into positive and negative components, enabling an investigation of the impact of declining and rising interest rates on bank risk-taking, i.e., cyclical asymmetries. Secondly, the quantile ARDL (QARDL) of
Cho et al. (
2015) is employed to capture the differential impact of interest rates on various levels of risk-taking, recognizing the variations in risk-taking environments, i.e., location asymmetries. Notably, these nonlinear econometric models are preferred over other existing nonlinear models in that they are compatible with both stationary and non-stationary data as well as address endogeneity concerns such as simultaneity and reverse causality.
To the best of our knowledge, our study is the first to investigate the influence of cyclical and location asymmetries on the interest-rate–bank-risk-taking relationship. Our analysis serves to enlighten South African monetary policymakers on whether their current path of raising interest rates in an already high-risk environment poses a threat to the financial system through excessive bank risk-taking behavior. Financial regulators would also be interested in knowing the extent to which macroprudential policies have protected the banking sector against the risk inherent to changes in interest rates. Investors and fund managers could also use our results to make more informed decisions concerning risk management and diversification strategies.
The remainder of the study is structured as follows:
Section 2 reviews the theoretical and empirical literature;
Section 3 describes the empirical framework;
Section 4 presents the data and empirical results; and
Section 5 concludes the study.
4. Data and Empirical Results
4.1. Data Sources
The time-series variables used in our study are collected from three sources: (i) the International Monetary Fund (IMF) financial soundness database, (ii) the South African Reserve Bank (SARB) online database and (iii) the South African National Credit Regulator (NCR) online database.
Table 2 links each variable used in the study to its source. All time-series data were collected at a quarterly frequency, spanning the period from 2008:q1 to 2022:q3.
4.2. Descriptive Statistics, Correlation Matrix and Unit Root Tests
Table 3 reports the descriptive statistics of bank risk-taking measures and the regressors. Non-performing loans average 4.11% as a percentage of GDP which is lower than the 5% that is regarded as elevated by the International Monetary Fund (IMF). However, it should be noted that during periods of economic crises, non-performing loans reached elevated levels. Liquid assets average 40.37%; however, a decline was observed since 2008. Standard deviations of most of the variables (except for unsecured lending) are low, implying minimal variability in the data.
Table 4 presents the correlation matrix to show the degree of linear association between the variables and to detect severe multicollinearity. The lending rate is positively and significantly correlated with liquid assets, while its relationship with non-performing loans and unsecured lending is negative and significant. The implication is that higher interest rates are associated with lower risk-taking. The correlations between the regressors are less than 0.8 which is an indication of the absence of severe multicollinearity.
Table 5 presents the results of the ADF, PP and DF-GLS unit root test performed with an intercept (Panel A) and with an intercept and trend (Panel B). Notably, the risk-taking variables (LOANS, LENDING, LIQUID) fail to reject the unit root hypothesis when the test is performed with an intercept (Panel A), whereas the independent variables generally confirm stationary at an I(0) level. However, when the unit root tests are performed using first differences, all series confirm the I(1) process with the exception of the LIQUID variables when the DF-GLS test is performed with an intercept only. Our overall findings suggest that none of the variables is integrated with I(2) which is a crucial condition for ensuring the compatibility of the time series with our ARDL, NARDL and QARDL models.
4.3. ARDL Results
We start our empirical analysis with the estimation of linear ARDL models across three regressions, each using a distinct bank risk measure as the dependent variable, as reported in
Table 6.
In the non-performing loans model, the long-run coefficient for the interest rate variable attains significance, with a positive indication that higher (lower) interest rates correspond to increased (decreased) bank risk-taking. Control variable estimates suggest a positive influence of GDP (ROE) on non-performing loans (unsecured loans), while the pandemic exacerbates bank illiquidity and unsecured lending. Conversely, in the short run, the interest rate variable’s significance is confined to the LIQUID variable, with a positive coefficient indicating that higher (lower) interest rates reduce (increase) bank risk-taking by minimizing (augmenting) the asset–liability mismatch.
4.4. NARDL Results
Next, we examine the results from the NARDL models, specifically segregating the impact of increasing interest rates (contractionary monetary policy) from decreasing interest rates (expansionary monetary policy) on bank risk-taking behavior. The NARDL estimates are reported in
Table 7.
In the long term, the coefficients reveal cyclical asymmetries in the associations between interest rates and bank risk, while the estimates on the other control variables remain consistent with those in
Table 6 for linear ARDL estimates. The INT+ coefficient is only significant for the LIQUID regression, indicating that raising interest rates (contractionary monetary policy) heightens bank risk-taking. Conversely, the INT- coefficient is only significant for the LOANS and LENDING regressions, with positive (negative) estimates for the former (latter). Overall, our findings suggest that both contractionary and expansionary monetary policies escalate bank risk-taking by exacerbating bank liquidity (i.e., ‘search-for-yield’ effects of
Rajan (
2006)) and non-performing loans (i.e., ‘risk-shifting’ effects of
De Nicolò et al. (
2010)), respectively.
In the short run, both contractionary and expansionary policies are found to exacerbate bank risk-taking, except that contractionary (expansionary) policy exacerbates non-performing loans (bank liquidity). The negative and significant error correction terms in all three models confirm that short-run dynamics transition into long-term effects. Additionally, our bounds test for asymmetric cointegration, along with tests for long-run and short-run asymmetries (except for the unsecured loans variable), verifies significant cyclical asymmetries between interest rates and bank risk-taking behavior. Diagnostic tests further validate that the estimated nonlinear regression adheres to classical regression assumptions.
4.5. QARDL Results
Lastly, we examine the results from the QARDL regressions to identify location asymmetries, that is, to see if the behavior of interest rates on bank risk-taking behavior differs between ‘high-risk’, ‘medium-risk’ and ‘low-risk’ environments. The percentiles chosen for this study are the 10th, 25th, 50th, 75th and 90th.
Table 8 and
Table 9 show the results of the long-run and short-run coefficients of the QARDL regressions, respectively.
The findings reveal several key observations. Firstly, in the LIQUID regressions, a positively significant coefficient on the interest rates variable is evident at the 50th quantile and above. This implies that higher (lower) interest rates diminish (boost) risk during periods characterized by medium-to-high levels of capital liquidity. Secondly, negative and significant estimates on the interest rates variable are observed in the LOANS regressions at the 10th and 75th quantiles. This suggests that higher (lower) interest rates reduce (increase) risk during periods featuring moderately high and extremely low levels of non-performing loans. Thirdly, negative and significant estimates also appear in the interest rate variable for the LEND regressions at the 50th quantile and below, indicating that higher (lower) interest rates reduce (increase) risk during periods with moderate- to low-risk levels of unsecured lending. Lastly, the short-term estimates generally mirror those of the long-term, with negative and significant error correction terms (ECTs) verifying cointegration effects at different quantiles. The relationships between the variables are ‘more solid’ over the long run than short run. Furthermore, the results suggest that banks alter their lending behavior in response to macroeconomic variables in the long run compared to the short run mostly in response to changes in profit levels.
In summary, our QARDL regression results propose that higher interest rates mitigate risk, particularly for banks with relatively high levels of capital liquidity and low levels of non-performing loans/unsecured lending. These findings align with the theoretical assumptions of
Dell’Ariccia et al. (
2014), emphasizing the dependence of interest rates on bank risk based on the level of bank capitalization and leverage effects. Contrary to the prior empirical findings of
Delis and Kouretas (
2011),
Buch et al. (
2014),
Jiménez et al. (
2014),
Özşuca and Akbostancı (
2016),
Chen et al. (
2017),
Bonfim and Soares (
2018),
Brana et al. (
2019) and
Bubeck et al. (
2020), we observe that the impact of interest rates on risk-taking activity is more prominent during periods when banks are more capitalized or less risky, challenging previous assertions. Following
Dell’Ariccia and Marquez (
2013), we attribute this to less (more) risky banks or environments being less (more) monitored by financial regulators, allowing less (more) risky banks to engage in more (less) risky investments.
5. Conclusions and Recommendations
We examine the nonlinear relationship between interest rates on bank risk-taking behavior in South Africa between 2008:q1 and 2022:q3 using a family of ARDL models, i.e., linear ARDL, NARDL and QARDL models. Firstly, we estimate a linear ARDL model and find a positive long-run relationship between interest rates and non-performing loans, whereas positive short-run relations are only observed between interest rates and bank liquidity/unsecured lending. Next, we estimate NARDL models to give further information on the different relationships during upswings or downswings of the interest rate variable, and we find that falling (rising) interest rates decreased non-performing loans and yet increased unsecured lending (decreased bank liquidity). Lastly, we estimate QARDL models to segregate the effects of interest rates on bank risk at different levels of risk and find that the relationship is stronger in ‘medium-to-high’ risk environments for non-performing loans and unsecured lending and in ‘medium-to-low’ risk environments for liquidity.
Given South Africa’s current environment characterized by increasing interest rates and elevated risk levels since the COVID-19 period, our findings imply that contractionary policies may heighten financial risk by encouraging banks to invest in more illiquid assets. Conversely, a shift to decreasing interest rates or implementing expansionary monetary policy could elevate unsecured lending. Moreover, the QARDL results suggest that the Reserve Bank can effectively mitigate risk only when risk levels are moderate or low, becoming less effective at higher risk levels, a crucial observation given the sustained high levels of bank risk-taking in South Africa post-COVID-19.
Altogether, our results suggest that SARB cannot use its policy rate in isolation to curb the already high levels of bank risk-taking behavior. We therefore provide the following recommendations based on these observations. Firstly, the Central Bank should reassess its macroprudential toolkit, considering modifications to capital buffers, loan-to-value ratios and countercyclical capital requirements. Macroprudential regulation should be centered around limiting sovereign risk which can impact negatively the profitability of banks in the event of a decline in government bond yields, thus encouraging risk-taking. Secondly, enhancing communication channels through forward guidance measures could help shape bank sector expectations. Lastly, intensifying supervision and monitoring functions, including more frequent stress tests on the banking sector, is essential to effectively manage and curb heightened levels of bank risk-taking behavior.
The limitations/delimitations of the study are as follows: Firstly, the sample period chosen for the analysis was 2008:q1 to 2023:q3 which coincided with the 2008/09 global financial crisis. Data for the period prior to the global financial crisis was not employed as the impact of interest rates on bank risk-taking had not gained traction. Secondly, the study focused on the entire banking sector in South Africa due to the unavailability of data. Aggregated data prevent the analysis of the effect of interest rates on individual banks. Future research studies should focus on the impact of interest rates on individual banks. This will highlight the impact of rising and declining interest rates on risk-taking in banks of different sizes.