




































74 

 

Finance, Accounting and Business Analysis 
Volume 6 Issue 1, 2024 

http://faba.bg/       
ISSN  2603-5324 

 

Do Economic and Geopolitical Risks Matter for Banks’ Lending 

Decisions, Credit Risk, Performance, and Stability in South Africa? 

 

Damien Kunjal1* , Ananda Rao Suvvari2  

School of Accounting, Economics and Finance, University of KwaZulu-Natal, Durban, South Africa1 

Indian Institute of Technology, Goa, India2 

 
Info Articles   Abstract 

 
 

History Article: 

Submitted 05 April 2024 

Revised 10 May 2024 

Accepted 11 May 2024 

 

 Purpose: The objective of this study was to investigate the effect of 

economic and geopolitical risks on the lending decisions, credit risk, 

performance, and stability of banks in South Africa. 

Design/Methodology/Approach: To achieve this objective, ten 

banks were assessed for the period ranging from 2013 to 2022, and 

panel regressions were estimated with cross-sectional fixed effects. 

Findings: The results show that economic policy uncertainty (EPU) 

decreases credit risk and increases stability in the South African 

banking sector whilst geopolitical risk (GPR) increases credit risk 

and decreases stability. Further, it was found that these effects are 

more pronounced in banks with smaller market capitalizations and 

higher equity capitalizations. Moreover, global GPR has a 

destabilizing effect on South African banks. Remarkably, both EPU 

and GPR do not significantly influence lending decisions and 

performance by banks in South Africa. 

Practical Implications: This research enables a greater 

understanding of the determinants of banks’ lending decisions, 

credit risk, performance, and stability which is essential for devising 

governance policies and regulations to reduce fragilities in the 

banking system. 

Originality/Value: Given the scarcity of banking sector research in 

emerging markets, this study contributes to the existing literature by 

investigating the role of EPU and GPR on banking sector dynamics 

which remains understudied in South Africa. 

Keywords: Bank lending; bank stability; credit risk; economic 

policy uncertainty; geopolitical risk. 

Paper Type:  Research Paper  

 

Keywords:  

Bank lending; bank 

stability; credit risk; 

economic policy 

uncertainty; geopolitical 

risk. 
 

 

JEL: F5, G21, G28  

Address Correspondence:  

 Email:  KunjalD@ukzn.ac.za1 

  suvvari.anand@gmail.com2 
 

 

  

mailto:%20KunjalD@ukzn.ac.za1
https://orcid.org/0000-0002-3121-6969
https://orcid.org/0000-0002-8890-3348


Damien Kunjal, Ananda Rao Suvvari / Finance, Accounting and Business Analysis, Volume 6, Issue 1, 2024 

 

75 

 

INTRODUCTION 

 

Around the world, the efficiency of a country’s financial system largely depends on the stability of its 

banking system. The primary role of a bank is to act as an intermediary between depositors, who have surplus 

funds, and borrowers, who require funds. In addition, banks promote liquidity creation, economic growth, 

financial inclusion, and sustainable development (Alkhazaleh 2017; Léon and Zins 2020; Kahn and Wagner 

2021). However, like any business, banks can also fail. Failures in the banking sector could disrupt the flow 

of funds, subsequently, having a negative impact on economic activity and financial systems as a whole 

(Khan et al. 2017; Daly et al. 2019; Liu et al. 2021). Furthermore, bank failures could lead to contagion and 

systemic risks which lead to the failure of other financial institutions (Baumöhl et al. 2020). Thus, instability 

in the banking system is often cited as one of the main causes of the 2008 global financial crisis (Yeoh 2010; 

Ozili 2021). Fragility in the banking sector is caused by several factors, including economic and geopolitical 

risks. 

Economic policy uncertainty (EPU) relates to the inability to correctly predict when and how the 

government will revise its current policies (Wen, et al., 2021). Uncertainties related to economic policies not 

only affects the real economy, but also influences the operational strategies of banks. In particular, banks 

assist governments in achieving economic control by implementing macroeconomic policies which may 

require an adjustment in deposit rates and reserve requirement ratios, subsequently, influencing the 

operational strategies of banks (Chi and Li 2017). For instance, economic risks which relate to EPU may 

influence banks’ strategies for lending. Bordo et al. (2016) reported that EPU in the United States lead to a 

reduction in the growth of loans in order to reduce credit risk exposure, however, the effects of EPU is more 

prominent for larger, less liquid, and less capitalised banks. Furthermore, EPU leads to a growth in non-

performing loans (Chi and Li 2017) and bad debts begin to increase as borrowers become unable to service 

their debt (Hamdi and Hassen 2022), subsequently, exposing banks to greater credit risk. However, the effect 

of EPU on credit risk is more pronounced for banks with lower profitability and lower solvency in the United 
States (Orden-Cruz et al. 2023). The vulnerability of banks to EPU could also influence their performance. 

Athari (2021) found that EPU has a negative effect on the performance of Ukrainian banks as measured by 

their profitability. Additionally, EPU creates informational asymmetries and resource allocation 

inefficiencies which adversely impacts the stability of banks (Desalegn et al. 2023). On the contrary, Nguyen, 

et al. (2021) reported that increased EPU is associated with better cost management efficiency and 

profitability for Indian banks. Given the inconsistencies in existing research, it becomes critical to further 

explore how economic risk, captured by EPU, impacts the banking sector. 

Another key risk impacting the banking sector is geopolitical risk (GPR). By definition, GPR relates 

to the uncertainties associated with terrorism, wars, and any conflict between political entities that disturb 

the normal and peaceful course of international relations (Caldara and Iacoviello 2022). Therefore, while 

EPU relates to risks associated with economic stability and policy interventions, GPR relates to risks 

associated with war-like conflicts that are exogenous to business cycles and economic conditions. GPR may 

create informational asymmetries between borrowers and banks, causing banks to increase the cost of loans 

(Nguyen and Thuy 2023). As a result, higher GPR leads to a reduction in consumer and mortgage loans 

because fear causes consumers to delay spending and investment (Demir and Danisman 2021). This credit 

shrinkage deteriorates the performance of banks (Yildirim and Berkman 2022). Overall, GPR is associated 

with negative investor sentiments, reduced credit growth, increased default risk, and higher profit variations 

which lead to increased fragility in the banking sector (Phan, et al., 2022). However, research on the impact 

of geopolitical risk on the banking system remains scanty, especially in the South African context.  

On this background, the objective of this study is to investigate the effect of economic risk, captured 

through EPU, and geopolitical risk on the lending decisions, credit risk, performance, and stability of banks 

in South Africa. The motivation for concentrating on the South African banking sector stems from the high 

levels of concentration in this sector. In particular, five prominent banks (ABSA, Capitec, First National 

Bank, Nedbank, and Standard Bank) control more than 90% of the market share (Ngonisa et al. 2023). Such 

high levels of concentration exposes the banking sector to fragilities and makes it more vulnerable to external 

shocks. A common shock impacting the banking sector is EPU (Desalegn, et al. 2023). In recent years, South 

Africa has experienced high uncertainty regarding economic policies primarily due to weak growth 

projections, high interest rates, low business confidence, and uncertainty regarding public finances (Burger 

2023). Another factor influencing the stability of the banking sector is GPR (Phan et al. 2022). Recently, 

South Africa has been involved in geopolitical conflicts including its geopolitical tensions with the United 

States over South Africa’s perceived support for Russia in the Russia-Ukraine war (Burger 2023). Further 

geopolitical tensions could stem from the South African Parliament’s vote to suspend diplomatic ties with 

Israel amid the Israel-Palestine war (Panchia 2023). Given the rising economic and geopolitical risks in 

South Africa, it is vital to understand how these risks impact the banking sector, which is needed to foster 

economic growth and efficiency in the financial system. 



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This study contributes to existing literature in several ways. According to Orden-Cruz et al. (2023), 

research on the effect of EPU on the credit risk of banks is scanty. This study extends research in this domain 

by exploring how EPU impacts banks’ credit risk as well as lending decisions, performance, and stability. 

Likewise, Phan et al. (2022) mention that there has not been much research on the influence of geopolitical 

risk on banks’ stability. As such, this study contributes to existing literature on the effect of geopolitical risk 

on various aspects of the banking sector. Naili and Lahrichi (2022) acknowledge that there is also a scarcity 

of banking sector research in emerging markets. Accordingly, this study sheds light on banks in emerging 

markets, particularly, from the South African context. The objectives of this research also enable a greater 

understanding of the determinants of banks’ lending decisions, credit risk, performance, and stability which 

is essential for devising governance policies and regulations to reduce fragilities in the banking system. 

This paper is structured as follows: Section 2 reviews existing literature on the effects of EPU and 

GPR on banks. Section 3 outlines the data and methodology employed. Section 4 presents the results, and 

Section 5 concludes the study. 

 

LITERATURE REVIEW 

 

The effects of EPU on banks 
Existing research suggests that EPU impacts various bank-related aspects. One of the earliest studies 

exploring the effect of EPU on the banking sector was conducted by Bordo et al. (2016). Bordo et al. (2016) 

discovered that EPU decrease bank loan growth in the United States. Further, it was reported that this effect 

is stronger for banks that are larger, less capitalised, and less liquid. With a larger sample of 19 countries 

(excluding South Africa), Hu and Gong (2019) reported that there is a negative relationship between EPU 

and credit growth so that banks can reduce their exposure to credit risk. This effect was stronger for larger 

and riskier banks but weaker for banks with more liquidity and greater diversification. Similar findings were 

reported by Danisman et al. (2020) for European banks. Notably, Nguyen et al. (2020) reported that the 

negative effect of EPU on bank credit growth is more pronounced in emerging markets relative to developed 

markets. 

Regarding bank credit risk, Chi and Li (2017) found that EPU increases loan concentrations, loan 

migration rates, and non-performing loan ratios, subsequently, leading to an increase in the credit risk of 

Chinese commercial banks. Similarly, Karadima and Louri (2021) reported that EPU increases non-

performing loans in Italy, France, Spain and Germany, however, this effect is moderated by the level of bank 

concentration. On the contrary, Ozili (2022) discovered that EPU and non-performing loans are negatively 

correlated for European Union and G7 countries. Hamdi and Hassen (2022) reported that EPU increase 

banks’ credit risk but decrease loan sizes and performance in Tunisia. Similar findings are reported by Mendy 

et al. (2023) for U.S banks. On the contrary, Nguyen et al. (2021) found that EPU improves cost efficiency 

and enhances the profitability of Indian banks. 

With regards to bank stability, Nguyen (2021) reported that EPU adversely impacts bank stability in 

European countries. The negative effect is attributed to higher bank risk-taking which increase the chances 

of corporate failure. According to Nguyen (2021), the higher bank risk-taking amid EPU is induced by the 

adverse effect of EPU on various economic activities, the reduction in banks’ profitability, and herd 

behaviour. Similarly, Phan et al. (2021) reported that EPU negatively influences financial stability at both 

country- and bank-levels for 23 countries excluding South Africa. According to Phan et al. (2021), this 

negative effect is because EPU creates uncertainty which leads to informational asymmetries that make it 

difficult for banks to understand the characteristics of borrowers, subsequently, making it challenging for 

banks to distinguish credit risks during periods of uncertainty. In addition, Shabir et al. (2021) found that 

EPU negatively influences bank stability, regardless of the levels of income and development in countries. 

EPU is also seen to affect other aspects of the banking sector. Ashraf and Shen (2019) found that 

higher EPU brings about an increase in the prices of bank loans in order to compensate banks for additional 

default risk. Tran (2020) discovered that banks reduce dividend payouts and share repurchases amid EPU 

to be more precautionary. Tran, et al. (2021) found that U.S banks are more likely to diversify their income 

streams into non-interest income generating streams during periods of high EPU, which may positively 

contribute to bank performance. On the contrary, Boungou and Mawusi (2022) reported that EPU has no 

significant effect on banks’ non-interest income activities in Germany, Greece, France, Japan, Italy, Ireland, 

Spain, Sweden and the Netherlands. Berger et al. (2022) found that EPU promotes liquidity hoarding in 

order for banks to protect themselves against funding difficulties and liquidity shocks.  

 

The effects of GPR on banks 

Unlike EPU, research on the impact of GPR on the banking sector is relatively limited. Demir and 

Danisman (2021) reported that GPR leads to a decrease in consumer and mortgage loans because GPR 

induces fear among consumers causing them to delay spending and investment. However, the authors found 



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that corporate loans are not significantly influenced by GPR. Nguyen and Thuy (2023) found that GPR 

increases the cost of loans in the U.S in order to compensate banks for the additional risk of default. With 

regard to bank performance, Alsagr and Hemmen (2020) discovered that GPR has a significant, negative 

effect on the profitability of banks in 19 emerging countries. Similarly, Yildirim and Berkman (2022) reported 

that GPR has a negative effect on the profitability of banks in G7 countries. The authors attribute this finding 

to the credit shrinkage, amid geopolitical uncertainty, which negatively impacts performance.  

Regarding bank stability, Phan, et al. (2022) found that an increase in GPR is associated with a 

decrease in the stability of U.S banks, and this effect is weaker for large and more capitalised banks. 

According to Phan et al. (2022), this finding may be attributed to reduced liquidity provision as a result of 

the panic caused by increased GPR and uncertainty. Furthermore, GPR fosters negative investor sentiments 

causing investors to become concerned about losing money, thus, moving their investments from risky to 

safer assets which may lead to a reduction in liquidity among banks. Phan et al. (2022) also mention that 

GPR induces a reduction in loan growth and an increase in credit risk as well as profit variations which 

expose banks to increased fragility. Likewise, Shabir et al. (2023) reported that geopolitical uncertainties 

induce herding behaviour among banks because of the lack proper guiding risk framework. In addition, 

geopolitical uncertainties promote risk-taking behaviour to search for yield. However, this herd and risk-

taking behaviour does not generate the desire outcomes, instead it generates higher risk and instability for 

banks (Shabir et al. 2023). The limited research on the impact of GPR on banks highlights the need for 

further research in this domain. 

 

DATA AND METHODOLOGY 

 

Data sample 
The sample of this study includes ten South African banks which are locally-controlled, namely; Absa 

Bank Limited, Bidvest, Capitec Bank Limited, FirstRand Limited, Grindrod Bank Limited, Investec, 

Nedbank, Sasfin Bank Limited, The Standard Bank of South Africa Limited, and Ubank. The study excludes 

foreign-controlled banks, mutual banks, and state-owned banks, and only includes banks with complete data 

for the full sample period. The ten-year sample period varies from 2013 to 2022. This study relies on financial 

statements which are reported annually. Accordingly, the frequency of data employed is annual. 

Furthermore, the use of annual data is supported by Makrelov et al. (2023) who mention that annual data 

allows for comparison with existing research on the banking sector which typically uses annual data and 

removes the noise that is present in monthly data. 

 

Computation of variables 
In order to compute the variables used in this study, financial statements are obtained from the 

BankFocus database. In line with existing research, banking lending decisions are proxied by the growth 

rate of loans while credit risk is measured by the non-performing loans ratio. Additionally, bank performance 

is measured by the return on average assets and bank stability is computed using the Z-score. Noteworthy is 

that an increase in the Z-score is associated with a reduction in the probability of bank insolvency and, thus, 

an increase in bank stability (Shabir et al. 2021). 

Regarding the independent variables, economic risk is captured by EPU. Following Demir and 

Danisman (2021) and Udeagha and Muchapondwa (2022), the World Uncertainty Index (WUI), created by 

Ahir et al. (2018), is used as a proxy for EPU. The index is based on the frequency of the term “uncertainty” 

in the country reports by the Economist Intelligence Unit (Ahir et al. 2018). The index is rescaled and 

normalised, and an increase in the index value signifies an increase in the level of uncertainty. Following a 

myriad of recent literature, geopolitical risks are captured using the geopolitical risk (GPR) index created by 

Caldara and Iacoviello (2022). The GPR index is based on the frequency of articles in leading newspapers 

in the United States, Canada, and the United Kingdom that discuss adverse geopolitical events and threats 

(Caldara and Iacoviello 2022). Like the WUI, the GPR index is normalised and an increase in the index 

value signifies an increase in geopolitical risk and uncertainty. Data on the WUI for South Africa is obtained 

from https://worlduncertaintyindex.com/ while GPR index data is obtained from 

https://www.matteoiacoviello.com/gpr.htm. In addition, macroeconomic data is obtained from the South 

African Reserve Bank (SARB) to compute some of the control variables. Table 1 summarises the 

computation of the variables in this study based on existing literature. 
  

https://worlduncertaintyindex.com/
https://www.matteoiacoviello.com/gpr.htm


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Table 1. Variables used in this study   

Variable Symbol Computation Source 

Dependent variables 

Growth rate of 

loans 

GRL 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑙𝑜𝑎𝑛 𝑎𝑚𝑜𝑢𝑛𝑡𝑠 𝑖𝑛 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑦𝑒𝑎𝑟

𝐿𝑜𝑎𝑛 𝑎𝑚𝑜𝑢𝑛𝑡 𝑖𝑛 𝑝𝑟𝑒𝑣𝑖𝑜𝑢𝑠 𝑦𝑒𝑎𝑟
 

Hamdi and Hassen 

(2022) 

Non-performing 

loans ratio 

NPL 𝑁𝑜𝑛 − 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑖𝑛𝑔 𝑙𝑜𝑎𝑛𝑠

𝑇𝑜𝑡𝑎𝑙 𝑙𝑜𝑎𝑛𝑠
 

Chi and Li (2017) 

Return of 

average assets 

ROA 𝑁𝑒𝑡 𝑖𝑛𝑐𝑜𝑚𝑒

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠
 

Alsagr and 

Hemmen (2020) 

Stability Z-score 𝑆𝑢𝑚 𝑜𝑓 𝑅𝑂𝐴 𝑎𝑛𝑑 𝑒𝑞𝑢𝑖𝑡𝑦 − 𝑡𝑜 − 𝑎𝑠𝑠𝑒𝑡𝑠 𝑟𝑎𝑡𝑖𝑜

𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑅𝑂𝐴 𝑜𝑣𝑒𝑟 𝑝𝑎𝑠𝑡 3 𝑦𝑒𝑎𝑟𝑠
 

Phan et al. (2022) 

Independent variables 
Economic risk EPU 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑊𝑜𝑟𝑙𝑑 𝑈𝑛𝑐𝑒𝑟𝑡𝑎𝑖𝑛𝑡𝑦 𝐼𝑛𝑑𝑒𝑥 Demir and 

Danisman (2021) 

Geopolitical risk GPR 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝐺𝑒𝑜𝑝𝑜𝑙𝑖𝑡𝑖𝑐𝑎𝑙 𝑅𝑖𝑠𝑘 𝐼𝑛𝑑𝑒𝑥 Phan, et al. (2022) 

Bank-specific control variables (𝑩𝑪𝒐𝒏𝒕𝒓𝒐𝒍) 

Size SIZE 𝑁𝑎𝑡𝑢𝑟𝑎𝑙 𝑙𝑜𝑔𝑎𝑟𝑖𝑡ℎ𝑚 𝑜𝑓 𝑡𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 Chi and Li (2017); 

Alfadli and Rjoub 
(2020); Orden‐
Cruz et al. (2023) 

Financial 

leverage 

LEV 𝑇𝑜𝑡𝑎𝑙 𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠

𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠
 

Capital 

adequacy ratio 

CAR 𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠

𝑅𝑖𝑠𝑘 − 𝑤𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝑠𝑢𝑚 𝑜𝑓 𝑎𝑠𝑠𝑒𝑡𝑠
 

Diversification DIV 𝑁𝑜𝑛 − 𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑖𝑛𝑐𝑜𝑚𝑒

𝑇𝑜𝑡𝑎𝑙 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑖𝑛𝑐𝑜𝑚𝑒
 

Market-specific control variables (𝑴𝑪𝒐𝒏𝒕𝒓𝒐𝒍) 

Gross domestic 

product 

GDP 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝑅𝑒𝑎𝑙 𝐺𝐷𝑃 Orden-Cruz et al. 

(2023) 

Inflation INF 𝐶ℎ𝑎𝑛𝑔𝑒 𝑖𝑛 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑃𝑟𝑖𝑐𝑒 𝐼𝑛𝑑𝑒𝑥 (𝐶𝑃𝐼) Iqbal et al. (2020) 

Source: Authors’ own compilation. 

 

Methodology 
Based on a review of existing literature, the following panel regressions are estimated to achieve the 

objectives of this study: 

𝑌𝑖,𝑡 = 𝛼0 + 𝛽1𝐸𝑃𝑈𝑡−1 + ∑ 𝛾𝑘𝐵𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡−1 + ∑ 𝛿𝑗𝑀𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡−1

𝐽=2

𝑗=1

𝐾=4

𝑘=1

+ 휀𝑖,𝑡 (1) 

𝑌𝑖,𝑡 = 𝛼0 + 𝛽1𝐺𝑃𝑅𝑡−1 + ∑ 𝛾𝑘𝐵𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡−1 + ∑ 𝛿𝑗𝑀𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑡−1

𝐽=2

𝑗=1

𝐾=4

𝑘=1

+ 휀𝑖,𝑡   (2) 

 

 

In Equation (1) and (2) above, 𝑌𝑖,𝑡 represents the dependent variable, which is GRL, NPL, ROA, or 

Z-score. The main variables of interest are 𝐸𝑃𝑈 which represents economic policy uncertainty or economic 

risk and 𝐺𝑃𝑅 which represents geopolitical risk in Equations (1) and (2), respectively. To control for 

alternative explanations of the dependent variables, bank-specific control variables (𝐵𝐶𝑜𝑛𝑡𝑟𝑜𝑙) which 

include SIZE, LEV, CAR, and DIV are included along with market-specific control variables (𝑀𝐶𝑜𝑛𝑡𝑟𝑜𝑙) 
which include GDP and INF. All of the explanatory variables are lagged by one period to avoid the 

endogeneity problem (Chi and Li 2017; Alfadli and Rjoub 2020). Furthermore, to control for unobserved or 

omitted variables, bank-specific (that is, cross-sectional) effects that are either fixed or random are included 

in the model. The choice between the fixed-effects or random-effects estimators is based on the Hausman 

test. Furthermore, prior to estimating the panel regressions, tests for cross-sectional dependence between the 

series are conducted. Thereafter, if cross-sectional dependence is absent, first-generation panel unit root tests 

are employed to examine the stationarity of the variables; however, if cross-sectional dependence is present, 

second-generation panel unit root tests are employed. 

 

RESULTS AND ANALYSIS 

 

Preliminary Analysis  

Table 2 provides a summary of the descriptive statistics for the dependent and independent variables 



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in their raw form. The average, annual growth in loans for banks in South Africa is 10.08% while the average, 

annual non-performing loans is 7.49%. The banks have also generated a return on assets of 1.69% on 

average, with a Z-score of 76.04. The respective standard deviations suggest that there is a high variation in 

these measures, as can be seen from the large ranges between the minimum and maximum values. These 

high variations may be attributed to extreme market events, such as the 2008 Global Financial Crisis and 

the COVID-19 Pandemic, which had significant implications on the banking sector. The EPU and GPR 

ratings exhibited an annual mean of 0.70 and 0.04, respectively, indicative of relatively stable economic 

policy and geopolitical conditions. It is important to note that the subsequent estimations employ the 

variables in their natural logarithmic form to ensure easy comparison. 

 

Table 2. Descriptive statistics 

 GRL NPL ROA Z_SCORE EPU GPR 

Mean 10.0772 7.4932 1.6901 76.0413 0.7048 0.0429 

Max. 87.8990 47.6780 6.4370 437.2452 1.8211 0.0874 

Min. -18.2085 0.4697 -2.3918 2.1797 0.1364 0.0146 

Std. Dev. 15.5403 8.3605 1.7406 71.8178 0.4723 0.0223 

Skewness 2.0896 2.4273 0.9955 2.1296 1.0665 0.4600 

Kurtosis 9.8959 9.5844 3.8507 9.5100 3.6421 2.3131 

       

Jarque-Bera 270.9091 278.8404 19.5317 252.1697 20.6756 5.4924 

Prob. 0.0000 0.0000 0.0001 0.0000 0.0000 0.0642 

       

Obs. 100 100 100 100 100 100 

Source: Authors’ own compilation. 

 

Table 3. Correlation statistics 

 GRL NPL ROA Z_SCORE EPU GPR 

GRL 1.0000      

NPL -0.3552 1.0000     

ROA 0.4529 -0.1410 1.0000    

Z_SCORE 0.0708 -0.3821 0.0759 1.0000   

EPU 0.0487 -0.0182 0.1455 0.0542 1.0000  

GPR 0.1486 0.0680 -0.0012 -0.3072 -0.1839 1.0000 

Source: Authors’ own compilation. 

 

The correlation statistics between the variables of interest are provided in Table 3. Interestingly, the 

correlation statistics imply that EPU exhibits a positive impact on the banking sector by enhancing growth 

in loans, performance (ROA), and stability (Z-score) whilst reducing credit risk (NPL). On the contrary, 

GPR increases the growth in loans, but also increases credit risk whilst reducing performance and stability. 

However, correlation does not imply causation, therefore, there is a need for further regression analysis. In 

terms of the regression analysis, the unreported results of cross-sectional dependence tests indicated that 

cross-sectional dependence was present, hence, second-generation panel unit root tests were used to detect 

stationarity. The second-generation, CIPS unit root test confirmed the stationarity of the variables in their 

natural logarithmic form. Further, the unreported Hausman test results confirmed that the cross-sectional 

fixed effects models are more appropriate for assessing the banking sector, consistent with existing literature. 

The next section presents and discusses the main results obtained from the cross-sectional fixed effects panel 

regressions. 

 

Baseline Results 
Tables 4 and 5 display the effects of EPU and GPR on the South African banking sector, respectively. 

The dependent variables are presented horizontally while the explanatory variables are presented vertically. 

The respective R-squared and F-stat for each model confirms that the models are a good fit. In terms of EPU, 

the results indicate that EPU significantly influences only NPL and Z-score. Specifically, EPU leads to a 

decrease in the non-performing loans of South African banks while increasing their Z-score. This would 

imply that EPU exhibits a positive impact on the South African banking sector by reducing credit risk and 

improving stability. The negative relationship between EPU and NPL is consistent with the findings of Ozili 

(2022) who reported a similar negative effect in G7 and EU countries. However, the positive effect of EPU 

on bank stability contradicts the existing findings of Nguyen (2021) and Phan et al. (2021) who did not 

consider the South African banking sector.  



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Contrary to the effects of EPU, GPR exhibits a positive effect on NPL but a negative effect on Z-

score. This implies that GPR increases credit risk and decreases stability in the South African banking sector. 

Similar findings are reported by Phan et al. (2022) who found that GPR decreases the stability of U.S banks. 

Notably, the contrasting effects of EPU and GPR on the South African banking sector may be attributed to 

the negative correlation between these two variables – as reported in Table 3. Remarkably, both EPU and 

GPR do not significantly impact the lending decisions (GRL) and performance (ROA) of banks in South 

Africa. 

 

Table 4. Panel regression estimations for the effect of EPU 

 GRL NPL ROA Z_SCORE 

CONSTANT -300.4006 42.7177 55.9807 -147.2027 

EPUt-1 1.3507 -0.2020*** -0.0054 0.2102* 

SIZEt-1 -9.4304 0.7779** -0.1815 -1.0190* 

LEVt-1 -122.0800*** -1.2753 -4.8759*** 2.7759 

CARt-1 0.6082 0.5305 1.1933** -2.1409** 

DIVt-1 1.4983 0.0709 -0.1928 -0.4719** 

GDPt-1 66.7311 -3.3578 -2.1173 10.7612* 

INFt-1 -0.4336 -0.0093 0.0417 -0.0108 

     

R-squared 0.4400 0.7762 0.6885 0.5408 

F-stat 3.5845 15.8238 10.0827 5.3738 

Prob (F-stat) 0.0001 0.0000 0.0000 0.0000 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively.  

Source: Authors’ own compilation. 

 

Table 5. Panel regression estimations for the effect of GPR 

 GRL NPL ROA Z_SCORE 

CONSTANT 673.6634 -175.0009 39.1983 338.4778** 

GPRt-1 -2.2010 0.4762** 0.0351 -1.0242*** 

SIZEt-1 -9.5140 0.6948* -0.1984 -0.5887 

LEVt-1 -121.1453*** -1.2122 -4.8432** 1.9806 

CARt-1 1.7811 0.1835 1.1578* -1.1626 

DIVt-1 1.4238 0.0692 -0.1948 -0.4237** 

GDPt-1 2.2141 11.1129 -0.9967 -21.6407** 

INFt-1 -0.1951 -0.0494 0.0400 0.0470 

     

R-squared 0.4360 0.7736 0.6886 0.6059 

F-stat 3.5265 15.5873 10.0895 7.0141 

Prob (F-stat) 0.0001 0.0000 0.0000 0.0000 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively. 

Source: Authors’ own compilation. 

 

Further Analysis 
Existing literature suggests that the effects of EPU and GPR may differ across banks of different sizes 

(or market capitalisations) as well as banks of different equity capitalisations (total equity to total assets). On 

this basis, further analysis is conducted on the effect of EPU on banks of different sizes and equity 

capitalisations. In addition, the effect of global EPU and GPR is examined. Tables 6 and 7 suggest that the 

directions of the effects of EPU and GPR on NPL and Z-score remain the same as the baseline results, 

however, the magnitude and significance of the effects indicate that the effects of EPU and GPR are more 

pronounced in small banks. Further, Tables 8 and 9 suggest that the direction of the effects of EPU and GPR 

on NPL and Z-score remain the same regardless of equity capitalisation, however, the magnitude and 

significance of the effects indicate that the effects of EPU and GPR are more pronounced in highly 

capitalised banks. Together, these findings indicate that the effects of EPU and GPR are stronger in banks 

with smaller market capitalisations and higher equity capitalisations. With regards to the global ratings, 

Tables 10 and 11 suggest that global EPU significantly influences only ROA, in which case the effect is 

positive, while global GPR only impacts the Z-score, in which case the effect is negative. These findings 

indicate that global economic policy uncertainty improves the performance of South African banks, 

however, global geopolitical risk decreases the stability of South African banks. More importantly, these 

findings imply that the South African banking sector is not immune to the effects of global conditions 



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because global EPU and GPR spills over to the South African market and significantly influences local 

banks.  

 

Table 6. Effect of EPU on banks of different sizes 

 LARGE SMALL 

 GRL NPL ROA Z_SCORE GRL NPL ROA Z_SCORE 

CONSTANT 502.047 22.247 -114.583 -359.467* -256.501 25.887 43.501 -217.09 

EPUt-1 0.797 -0.082* 0.050 0.083 1.828 -0.306** -0.030 0.318** 

SIZEt-1 3.180 0.717** -0.890 -2.146** -27.885* 0.874 -0.458 0.297 

LEVt-1 -330.66 3.533 12.929 44.265** -40.094 -3.379 -4.001 -4.800 

CARt-1 14.364 -0.670 -0.286 0.174 -6.260 1.255 1.300 -1.533 

DIVt-1 1.049 -0.056 0.076 -1.029* -2.890 0.335 -0.299 0.078 

GDPt-1 58.210 -3.229 4.889 13.948 61.648 -1.871 -1.267 15.672 

INFt-1 -0.034 0.003 0.073* -0.034 -1.186 -0.005 0.002 0.034 

         

R-squared 0.373 0.898 0.810 0.520 0.540 0.780 0.583 0.583 

F-stat 1.784 26.345 12.768 3.245 3.527 10.631 4.200 4.196 

Prob (F-stat) 0.098 0.000 0.000 0.004 0.002 0.000 0.001 0.001 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively.  

Source: Authors’ own compilation. 

 

Table 7. Effect of GPR on banks of different sizes 

 LARGE SMALL 

 GRL NPL ROA Z_SCORE GRL NPL ROA Z_SCORE 

CONSTANT -67.216 -34.364 -155.84 57.100 274.546 -349.94 90.359 233.662 

GPRt-1 1.401 0.138 0.101 -1.019* -6.818 0.854** -0.106 -1.024*** 

SIZEt-1 1.997 0.720** -0.970* -1.778** -27.707* 0.901 -0.418 0.296 

LEVt-1 -351.96 2.925 11.460 53.602* -36.553 -3.998 -4.078 -4.169 

CARt-1 12.518 -0.841 -0.419 1.473 -1.298 0.738 1.456 -0.847 

DIVt-1 0.527 -0.052 0.041 -0.879* -3.254 0.393 -0.295 0.016 

GDPt-1 103.86 0.701 8.167 -16.936 -138.00 23.090* -4.394 -14.277 

INFt-1 0.035 -0.011 0.077* 0.002 -0.775 -0.066 0.001 0.102 

         

R-squared 0.370 0.891 0.809 0.635 0.551 0.789 0.584 0.621 

F-stat 1.758 24.608 12.704 5.212 3.674 11.189 4.215 4.909 

Prob (F-stat) 0.103 0.000 0.000 0.000 0.002 0.000 0.001 0.000 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively.  

Source: Authors’ own compilation. 

 

Table 8. Effect of EPU on banks of different equity capitalisations 

 HIGH LOW 

 GRL NPL ROA Z_SCORE GRL NPL ROA Z_SCORE 

CONSTANT 51.273 -19.413 83.269 -248.73* -244.037 55.153 -148.11 -373.49* 

EPUt-1 2.430 -0.316** -0.004 0.261 -0.228 -0.085* 0.003 0.090 

SIZEt-1 -4.500 0.841 0.026 -0.472 -18.526*** 0.897*** -1.206** -2.350** 

LEVt-1 -124.17* -3.861 -5.165* -2.078 -294.806 4.221 16.260 41.193 

CARt-1 -1.878 1.242 1.231 -1.641 3.677 -0.186 0.706 1.180 

DIVt-1 0.661 0.353 -0.286 -0.063 3.220 -0.083 0.096 -0.895*** 

GDPt-1 8.074 1.279 -4.047 17.866* 126.95* -5.932* 6.361 15.817* 

INFt-1 -0.688 -0.009 0.016 -0.010 -0.152 -0.005 0.062 -0.003 

         

R-squared 0.450 0.723 0.729 0.520 0.489 0.881 0.530 0.511 

F-stat 2.454 7.835 8.079 3.255 2.866 22.247 3.390 3.130 

Prob (F-stat) 0.023 0.000 0.000 0.004 0.009 0.000 0.003 0.005 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively.  

Source: Authors’ own compilation. 

 

 
  



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Table 9. Effect of GPR on banks of different equity capitalisation 

 HIGH LOW 

 GRL NPL ROA Z_SCORE GRL NPL ROA Z_SCORE 

CONSTANT 36.419 -41.69** 132.47 206.71 -207.62 -15.719 -248.34 90.218 

GPRt-1 -6.868 0.850** -0.103 -0.976** 3.711 0.151 0.202 -0.943*** 

SIZEt-1 -3.196 0.691 0.076 -0.224 -21.429*** 0.886** -1.386** -1.656** 

LEVt-1 -122.92* -4.040 -5.214* -2.041 -296.16 4.781 16.059 41.272* 

CARt-1 2.208 0.753 1.336 -0.970 -1.036 -0.377 0.450 2.377 

DIVt-1 0.488 0.376 -0.285 -0.081 2.841 -0.074 0.070 -0.809*** 

GDPt-1 -203.84 27.456** -7.338 -12.487 25.419** -1.392 13.292 -15.814 

INFt-1 -0.180 -0.074 0.018 0.050 -0.303 -0.020 0.056 0.040 

         

R-squared 0.454 0.730 0.730 0.564 0.520 0.876 0.543 0.622 

F-stat 2.490 8.113 8.122 3.888 3.249 21.161 3.558 4.95 

Prob (F-stat) 0.021 0.000 0.000 0.001 0.004 0.000 0.002 0.000 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively.  

Source: Authors’ own compilation. 

 

Table 10. Panel regressions for the effect of Global EPU 

 GRL NPL ROA Z_SCORE 

CONSTANT -176.3969 80.0342 -23.1808 -243.6236** 

EPUt-1 -25.8532 -2.4508 8.9992* 9.0631 

SIZEt-1 -10.3850 1.0575* -0.3705 -1.4511* 

LEVt-1 -119.0072*** -1.9546 -4.5785** 3.7094 

CARt-1 -0.7837 0.7397 1.1972** -2.3597** 

DIVt-1 1.3086 0.1183 -0.2189* -0.5409** 

GDPt-1 84.9001 -3.5122 -5.7987 8.2795 

INFt-1 -0.2363 -0.0325 0.0322 0.0070 

     

R-squared 0.4335 0.7534 0.7028 0.5265 

F-stat 3.4918 13.9373 10.7899 5.0731 

Prob (F-stat) 0.0001 0.0000 0.0000 0.0000 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively.  

Source: Authors’ own compilation. 

 

Table 11. Panel regressions for the effect of Global GPR 

 GRL NPL ROA Z_SCORE 

CONSTANT -342.9091 56.7587 62.8985 -44.7467 

GPRt-1 2.9456 -0.0761 0.3085 -0.8708** 

SIZEt-1 -10.5507 0.9942* -0.1342 -1.0062* 

LEVt-1 -120.0506* -1.8192 -5.0951* 2.6294 

CARt-1 -0.7563 0.7385 1.2021** -2.0007** 

DIVt-1 1.2933 0.1093 -0.1852 -0.4636** 

GDPt-1 69.6164 -4.3826 -2.6499 4.3159 

INFt-1 -0.4441 -0.0302 0.0220 0.0218 

     

R-squared 0.4360 0.7530 0.6958 0.5462 

F-stat 3.5269 13.9062 10.4360 5.4921 

Prob (F-stat) 0.0001 0.0000 0.0000 0.0000 

Notes: ***,**,* denotes significance at a 1%, 5%, and 10% level of significance, respectively. 

Source: Authors’ own compilation. 

 

CONCLUSIONS 

 
The survival of a country’s financial system largely depends on the stability of its banking system. 

However, in recent years, countries around the world have been exposed to greater economic and 

geopolitical risks as a result of rising wars and tensions amongst nations. On this background, the objective 

of this study was to investigate the effect of economic (measured through economic policy uncertainty) and 



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geopolitical risks on the lending decisions, credit risk, performance, and stability of banks in South Africa. 

To achieve this objective, 10 South African banks were assessed for the period ranging from 2013 to 2022, 

and panel regressions were estimated with cross-sectional fixed effects. The findings of this study indicated 

that EPU decreases credit risk and increases stability in the South African banking sector whilst GPR 

increases credit risk and decreases stability. Further, it was found that these effects are more pronounced in 

banks with smaller market capitalisations and higher equity capitalisations. Moreover, it was found that 

global GPR has a destabilising effect on South African banks. Remarkably, the findings indicated that both 

EPU and GPR do not significantly influence lending decisions and performance by banks in South Africa. 

However, contrary to the findings of domestic EPU, global EPU exhibited a significant, positive effect on 

bank performance. 

These findings have important implications for various stakeholders. For managers of banks, these 

findings imply that geopolitical risk is an important risk factor that needs to be acknowledged in the risk 

management framework of banks. In particular, banks need to devise risk management frameworks to 

mitigate the negative impact of geopolitical risk on banks’ credit risk and stability. In addition, banks should 

also devise strategies to capitalise on the positive effects of EPU. For policymakers and regulators, these 

findings imply that it is important to maintain stability in EPU rather than attempting to reduce EPU as a 

reduction in EPU could have unintended, adverse effects on banks’ credit risk and stability. More 

importantly, it is vital that governments and policymakers improve geopolitical conditions in order to 

mitigate the negative effects of GPR on banks. Such policies could relate to reducing wars and conflicts 

among countries. It is also important for bank regulators and policymakers to acknowledge that banks are 

not immune to global economic and geopolitical conditions. Therefore, it is essential to implement policies 

that protect banks from unexpected spill-overs of economic and geopolitical risks.  

 

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