Exploring the Impact of Country Risk on Banking Sector Stability: Evidence from the MENA Region

Abstract

This paper examines the impact of country risk on banking sector stability, employing the CAMELS framework, within 13 Middle Eastern and North African (MENA) countries for 1984–2024. The analysis exploits the impact of political, economic, and financial risk dimensions on 102 publicly listed banks using two-way random effects models and one-step dynamic panel data estimations. The findings reflected a significant inverted U-shaped nexus between country risk and the stability of the banking sector, addressing how high-country risk deteriorates banking resilience, whereas low country risk improves it. Political risk has the strongest impact with a similar nonlinear relationship. Conversely, economic and financial risks consistently have reverse linear effects. These findings signify the structural vulnerability of MENA banks to political, economic, and financial turmoil and address the urgent need for robust frames of risk management and fiscal discipline. This investigation extends sovereign risk theory, which explains the ability to maintain financial stability by integrating three core dimensions—political, economic, and financial risk—into a comprehensive empirical model that directly relates them to MENA banking stability and provides crucial insights for banking institutions, policymakers, and regulators in a highly volatile atmosphere.

1. Introduction

The banking sector is one type of financial intermediary that enables indirect finance transactions between both depositors and borrowers. Thereby, banks, in turn, can enhance the efficiency of capital allocation, risk management practices, and financial welfare by supporting the aggregate growth of various economic activities within nations (Belkhir et al., 2019). According to Čihák and Schaeck (2010), banking sector stability refers to the ability of banks to efficiently perform their core functions over time, especially during shocks or crises, without major disruptions to the real economy. Financial stability of banks is critically important due to the need for sustainable growth, economic resilience, shock containment, and crisis handling. Younsi and Nafla (2017) mentioned that banking stability acts as a cornerstone for economic solidity and potential growth. However, Khanchel and Ben Cheikh (2023) highlighted that banking sector stability in the Middle East and North Africa’s (MENA) region suffers from several challenges to overcome, despite its potential growth. Furthermore, numerous studies (e.g., Bouzidi & Mejri, 2019; Tagourti & Hossam, 2020; El Said & Soliman, 2021; Maghrebi & Loukil, 2022; Ben Naceur & Omran, 2023) identified the MENA region with several country risks, including political unrest, economic downturns, financial uncertainties, and escalated debt burdens. The interaction of these risks produces a very sophisticated atmosphere for all operating banks within the MENA zone. This situation highlights how vulnerable the MENA banks are to country risks.

According to Eaton and Gersovitz (1981), sovereign risk theory elaborates and resolves numerous questions regarding the relationship between country risk and the financial resilience of the banking sector. As far as banking success and sustainable existence are concerned, it is crucial to properly handle the country risk dilemma, a problem triggered by political tensions, economic fluctuations, currency devaluation, and supranational sanctions. Accordingly, Beri and Schclarek (2019) identified country risk as a wide range of probable threats to financial and economic stability, encompassing political, economic, and financial risk. Moreover, Damodaran (2023) mentioned that country risk is made up of scores allocated to political, economic, and financial dimensions released by International Country Risk Guidance (ICRG). Political risk includes political tension, government instability, corruption, and other factors that may jointly weaken investors’ confidence and discourage investments. Economic risk is attached to macroeconomic fluctuations, inflationary stresses, and declined economic growth, which harm banking stability in terms of poor asset quality and increase the Non-Performing Loans (NPLs) ratio. The vulnerability of banks to external shocks, such as foreign debts, exchange rate stability, and balance of payment deficit, is associated with financial risk. Additionally, several studies (e.g., Kumar & Sayani, 2015; Babu & Kumar, 2017; Nguyen & Vo, 2018; Al-Moussawi et al., 2018; Anggono, 2020) collectively validated the usage of the CAMELS model to evaluate banking sector stability. The CAMELS framework consists of six main elements (i.e., capital adequacy, asset quality, management quality, earnings, liquidity, and sensitivity to market risk); each component is represented by the initial letter. This paper employed the CAMELS methodology to convey a proper systematic analysis of banking stability within the MENA area.

Several studies (e.g., Beri & Schclarek, 2019; Schmidt & Raza, 2020; Huang & Lin, 2021; Semerad, 2023; Zhang & Wang, 2023) examined the potential influence of country risk, with its political, economic, and financial elements, on banking stability. The findings of these studies collectively address that country risk and its components have a crucial role in determining banking stability. However, they usually focus on a single dimension, a certain banking context, or linear influences. Therefore, the current study contributes to the literature through examining all three country risk dimensions at the same time, identifying nonlinear relationships, and offering empirical evidence covering a longer period of time to comprehensively understand how country risk shapes banking resilience across MENA countries, which were previously underexplored. Moreover, the outcomes highlighted that reducing country risks enhances the financial rigidity of banks, cross-border banking transactions, and intercountry capital inflows. While escalated country risks can lead to over-the-counter markets replacing official markets, this shift increases the risk of banking insolvency. Awan et al. (2023) highlighted that the financial robustness of banks is highly impacted by country risk dimensions. Investigating the impact of country risk on banking sector stability is critically important; however, less attention is paid to the MENA region despite its unique political, economic, and financial domains. Dramatic crises can render banking institutions defenseless due to their high reliance on financial leverage. Thereby, country risk can deteriorate banking stability, particularly in countries with fragile economic pillars.

The present study investigates the impact of country risk on banking sector stability within the MENA region. Furthermore, it addresses the impact of each stand-alone element of country risk (i.e., political, economic, and financial risks) as potential restrainers of banking soundness. High volatility characterizes banking institutions within MENA territory, thereby providing more in-depth inferences for numerous stakeholders.

Additionally, this study relies on both statistical significance and effect size not only to highlight the key drivers of country risk that can influence banking stability but also to reinforce the results and reveal the most significant country risk mechanisms, thus acting as the underlying foundation for effective risk strategy formulation, policy implementation, and practical applications.

This study was applied to all publicly listed banks in MENA countries with active stock markets, where the key study objectives encompass the following:

• Identify the impact of country risk as a composite on banking sector stability.
• Comprehend the impact of political risk on banking sector stability.
• Understand the impact of economic risk on banking sector stability.
• Elaborate on the impact of financial risk on banking sector stability.
• Discuss how the results in the MENA context compare with the concluded findings of previous studies in other countries and regions.
• Compare the outcomes of various models to obtain more solid conclusions.

Most of the previous studies focused on developed countries, and less attention was given to how country risk influences banking stability within the MENA region. Thus, there is a lack of profound understanding regarding how country risk and its political, economic, and financial dimensions uniquely influence the financial rigidity of banks in the MENA region. Additionally, with respect to the knowledge gap, empirical studies that have tested the impact of country risk on banking stability in different contexts have reached inconclusive findings. Aburiya and El-Sherbiny (2023) mentioned that the MENA zone has both a complicated and problematic environment for banking functions because of the political complexity of the region, low economic diversification, and financial volatility. These exceptional features provide a motivation for the current study on MENA banking stability. Thereby, the aim of this study is to reach a comprehensive evaluation of how MENA banking stability responds to dynamic dimensions of country risk, political, economic and financial risk. That is why this study can narrow the gap in the banking literature, which is highly focused on developed economies rather than MENA emerging markets. As a result, the findings of this study can enhance financial knowledge by using robust economic analysis to provide strong evidence while emphasizing the fiscal resilience of MENA banks in relation to country risk. The paper empirically identifies the individual and collective effects of country risk components on MENA banking resilience, examines the presence of nonlinear relationships between country risk elements and the stability of banks, and finally explores cross-country variations in banking sector responses to fluctuated country risk.

The findings are expected to have lasting consequences for different stakeholders and drive depositors, borrowers, and investors to make more informed financial decisions based on more accurately forecasted bank stability. The results can motivate banking executives to sufficiently formulate bank-specific responses to mitigate the adverse effect of heightened country risks. Finally, the results can incentivize both policymakers and regulators to pay more attention to the country risk practices adopted by banks, which can be viewed as a crucial instrument to boost banking stability.

Based on the previous discussion, it can be concluded that improved country risk practices can significantly yield numerous positive outcomes for banks in the MENA region. For instance, enhanced country risk practices can lead to efficient capital allocation, increased investment, the development of rigorous regulatory frameworks, robust crisis responses, and greater stability for banks that can withstand country-specific risks.

The structure of the paper is as follows: the next part reflects the findings of previous studies that examined the impact of country risk on banking stability in different contexts. Section 3 elaborates on the study methodology. Section 4 encompasses the outcomes of the analysis and results discussion. Finally, Section 5 represents the conclusion and recommendations.

2. Literature Review and Hypotheses Development

This section is divided into three main parts. The first part reflects an overview of country risk; the second part represents an outlook for banking stability; and finally, the third part summarizes previous studies examining the impact of country risk, with its three main components, on banking stability.

2.1. Country Risk in MENA Region

There are different views of country risk, as there is no unified definition referring to various studies (e.g., Cosset & Roy, 2014; Alam, 2016; Ahuja et al., 2017). Cosset and Roy (2014) highlighted country risk as an assessment of political, economic, and financial aspects and their interactions in shaping a country-specific risk profile. Alam (2016) demonstrated that country risk is the probability of a country being exposed to fiscal loss due to political instability, macroeconomic conditions, and financial risks in global markets. Ahuja et al. (2017) defined country risk as a thorough assessment of systematic risks stemming from public debt, financial, economic, and institutional factors. Olalere and Peterson (2023) categorized elements of country risk as political, economic, and financial risk, which impact the banking sector at varying levels. Although the country risk concept is broad, the political, economic, and financial dimensions are the most crucial elements that can be employed as a proxy for country risk assessment.

Wafo and Christian (2018) identified several political risk mechanisms, which include corruption, democratic accountability, socioeconomic status, military involvement in politics, law and order, ethnic tensions, religious tensions, internal and external conflicts, the stability of political decisions, and the quality of bureaucracy. Adrian and Emily (2022) underscored that the economic risk elements consist of real GDP growth rate, inflation rate, GDP per capita, country budget balance, and current account. Accordingly, a stable political environment, economic welfare, and financial stability in the country will minimize the overall country’s risk profile.

Triki et al. (2017) identified that the less developed countries suffer from high political instability, volatile exchange rates, negative economic growth, hyperinflation, and financial uncertainties. Al-Shboul et al. (2020) demonstrated that countries with a high degree of stability can solidify the country’s financial atmosphere. Furthermore, Athari et al. (2023) demonstrated that high-income countries have less exposure to country risk. However, countries with emerging markets face significant uncertainties regarding their political, economic, and financial stability. Khanchel and Ben Cheikh (2023) underscored the prevalence of political instabilities, conflict, tensions, and other political factors in certain countries within the MENA region.

2.2. Banking Sector Stability

Belkhir et al. (2019) reported that the banking sector is one of the cornerstones of a country’s economic growth. According to Athari et al. (2023), stability in the banking sector is the state in which banks collectively maintain financial resilience against both internal and external shocks while efficiently performing their core functions. Therefore, to maintain banking stability, banks must maintain sufficient capital, liquidity, and earnings, among other factors, to absorb losses during times of crisis and operate within a manageable risk environment. Al-Ghussin and Nashwati (2014) demonstrated that the CAMELS model can collectively combine variables of banking stability. Anggono (2020) explains the origination of the CAMELS framework dates back to 1979 and was operationalized by the American Federal Council for the examination of banking institutions. The USA applied the CAMELS model for assessing banks after repeated bank collapses and bailouts, as Rajveer et al. (2017) demonstrated. Gaul and Jones (2021) identified CAMELS by the initial letters of the model’s elements, including six proxies: capital adequacy, asset quality, management, earnings, liquidity, and sensitivity to market risk. Several studies (e.g., Kumar & Sayani, 2015; Babu & Kumar, 2017; Nguyen & Vo, 2018) validated the use of the CAMELS model for a proper assessment of the stability of banking institutions in a timely fashion. Likewise, Soni and Devarakonda (2023) verified the vital function of the CAMELS methodology in reflecting the detailed financial resilience of banks. Nowadays, the CAMELS methodology has evolved as an effective assessment tool for the long-term stability of banking sectors and is extremely crucial in identifying strengths and any potential shortcomings. That is why the CAMELS model enables policymakers to closely monitor banking stability and make informed decisions that aim to reinforce banking firmness.

Gaul and Jones (2021) addressed that banks can be categorized into 5 main categories based on the assessment score assigned per bank. A score of one is allocated to the best case, while a score of five is assigned to the worst case. Score (1) means the bank has solid financial stability in all aspects, score (2) reflects satisfactory financial rigidity, score (3) indicates moderate financial soundness and recommends monitoring one or more elements of the CAMELS model, score (4) means the bank has a weak financial situation, and score (5) demonstrates a high probability of bank failure. Neykov and Stoyanov (2016) proposed that supervisory authorities and regulators are recommended to follow up on the soundness of banks because CAMELS categorization can act as an early warning tool for different stakeholders. Furthermore, Tijjani et al. (2020) advocated for central banks to conduct financial comparisons among various banks, thereby facilitating the development of control mechanisms for those with lower scores. As a result, the CAMELS approach has been widely employed by central banks in the last decade and represents a vital measurement tool of banks’ financial situations.

2.3. Country Risk and Banking Sector Stability

Numerous studies (e.g., Bouzidi & Mejri, 2019; Tagourti & Hossam, 2020; El Said & Soliman, 2021; Maghrebi & Loukil, 2022; Awan et al., 2023; Ben Naceur & Omran, 2023) pinpointed the critical effect of country risk on banking sector stability across MENA countries. Triki et al. (2017) uncovered a negative relationship between the country’s risk and the financial strength of African banks due to capital fragility, sensitivity to failure, and high non-performing loans (NPLs). In a Chinese context, Lee and Lee (2019) highlighted that country risk escalation can effortlessly and negatively influence the CAMELS score used for assessing banking stability. Rezgallah et al. (2019) cited that banks seek to compensate for earnings uncertainty or potential losses during ascending country risk, which in turn financially destabilizes banks. Athari et al. (2020) recognized that elevated country risk negatively impacts competitiveness levels among banks, which generate financial inefficiencies. Fiordelisi et al. (2020) noted that elevated country risk has contributed to some past bank failures. Huang and Lin (2021) found a stronger correlation between elevated country risk levels and lower bank stability in emerging economies compared to developed economies. Obalade et al. (2021) demonstrated that an increase in country risk has a negative impact on the quality of banking assets in Nigeria. Furthermore, Damodaran (2023) proposed a direct link between country risk and the future outlook for banking robustness. The present literature concludes that country risk inevitably exposes the banking sector.

2.3.1. Political Risk and Banking Sector Stability

Various studies (e.g., Hassan et al., 2019; Athari, 2022; Saliba et al., 2023) asserted that country risk aspects consist of political, economic, and financial risks. The worsened country risks can dramatically impact almost every single array of banks’ financial stability. Chen et al. (2015) revealed a negative relationship between escalated political instability and the stability of East Asian banks via raised cost of funds and reduced credit supply. Mokatsanyane (2016) reported that long-term political risks, particularly conflicts, have an adverse impact on banking stability in South Africa. Ghosh (2016) pinpointed a negative relationship between political transition and banking resilience in the MENA region due to increased borrowers’ default probability during the Arab Spring period. Şanlısoy et al. (2017) addressed how insufficient political stability negatively impacts banking stability through elevated NPL ratios, which decrease earnings. Compaoré et al. (2020) noted that political uncertainties in 92 developing countries can increase the probability of default, potentially leading to widespread financial crises. Likewise, Al-Shboul et al. (2020) indicated that MENA political risks have a statistically significant negative impact on bank earnings, which reflects high banking delicateness. Uddin et al. (2020) declared growing corruption and ineffective legislation can push banks to get involved in destructive loan disbursements, which can raise the degree of insolvency. Athari (2022) found out that increased political risk of a country leads to fluctuations in bank earnings and increases the financial fragility of banks. Zhang and Wang (2023) displayed that political uncertainties can make banks unable to sufficiently assess the borrowers’ creditworthiness. Kirikkaleli and Kayar (2023) signified political stability directly linked to sustainable banking activities within the Balkan countries. In summary, the stability of banks has greater dependency on political risk parameters.

2.3.2. Economic Risk and Banking Sector Stability

Bocola (2016) confirmed that macroeconomic factors can broaden the connection between country risk and banking stability. Montes et al. (2021) similarly asserted that the current and anticipated economic conditions of the country contribute to banking stability. Sufian (2012) showed that a country’s economic uncertainty negatively affects the soundness of 77 banks in Pakistan, Sri Lanka, and Bangladesh. Chi and Li (2017) underlined that asymmetric information produced by economic vagueness will harm the financial rigidity of banks. Podstawski and Velinov (2018) revealed that economic fluctuations can destabilize the banking sector, particularly in Less Developed Countries (LDCs). Moreover, the global financial crisis revealed that developed countries suffered from dramatic economic uncertainties, which negatively impacted the soundness of banks (Brůha & Kočenda, 2018). Wu et al. (2020) signified that upheaved economic variabilities such as lower productivity, declined GDP growth, high unemployment, and others may generate a credit crunch and raise de-leverage degrees. Saliba et al. (2023) indicated growing economic risks can motivate banks to get involved in high-risk activities, which presents a threat to the stability of banks within BRICS countries. Zhang and Wang (2023) highlight how economic shocks negatively influence the stability of banking earnings via declined market confidence and increased opacity. There is clear evidence that economic risk has a critical part in formulating the fiscal steadiness of banks across countries.

2.3.3. Financial Risk and Banking Sector Stability

According to Albertazzi et al. (2014), banks have significant investments in several public debt instruments issued by the government, and any financial variation in public debt can undermine the financial resilience of the banking sector. Engler and Große-Steffen (2016) indicated that countries with a high probability of defaulting on their debts can reduce banking stability, as these countries cannot default on their home currency but are likely to default on foreign currencies. Caldara et al. (2016) verified the inverse linkage between a country’s financial risk and banking stability, as growing financial uncertainties will tighten credit offerings. Ali (2022) explained that enlarged debts owed by the government, a high probability of country default, and an expanded deficit in the balance of payment negatively impact the stability of banks. Altavilla et al. (2017) highlighted how the heavy dependence of European banks on debt instruments during the euro debt crisis weakened the financial resilience of European banks. Likewise, Brůha and Kočenda (2017) demonstrated financial instabilities will increase the cost of borrowing, which negatively influences the NPL level of banks in the Eurozone. Andreeva and Vlassopoulos (2019) cited that downgraded country creditworthiness can oppositely influence banking stability. Furthermore, Fiordelisi et al. (2020) elaborated that increased financial uncertainties in a country in the form of fluctuated exchange rates and higher debt service can exacerbate the financial instabilities of banks. Makrelov et al. (2023) stated that debt burdens and deficits, along with other fiscal dilemmas, inversely impact banking health. Kirikkaleli and Kayar (2023) stated that countries with financial stability are more likely to have stable banks, which in turn contributes to sustainable growth. The empirical evidence suggested that increased financial risks in the country can lead to a drastic decline in the financial stability of banks.

After reviewing the literature review, it is clear that country risk—including political, economic, and financial risk indexes—is a critical determinant of banking rigidity. The previous studies constantly exhibited that political tensions, macroeconomic fluctuations, and fiscal imbalances can deteriorate fiscal stability of banks, yet the nature of this effect is not linear all the time. Mild enhancements in economic conditions and fiscal discipline can advance bank robustness, while excessive degrees of risk deepen the financial distress of banks through escalated non-performing loans, worsening earnings, and elevated insolvency risk, which collectively intensify the financial fragility.

In conclusion, the stability of the banking sector within the MENA zone can be jeopardized by elevated standalone country risk elements (e.g., political, economic, and financial risk) or their combined influence. Thereby, it is tremendously crucial to adopt proper practice for country risk management, which can aid in stabilizing the banking sector and reducing the degree of banking risk in the MENA context. The current study aims to examine the country risk influence on banking vulnerabilities and identify the main driver of country risk that impacts the stability of banks. Accordingly, based on the unique nature and characteristics of the MENA zone and the findings of previous studies, many advocate a negative impact of country risk and its political, economic, and financial dimensions on bank stability. Based on these insights and building on sovereign risk theory, this study hypothesizes that country risk significantly impacts MENA banking stability. To further explore this relationship, the following main hypothesis has been formulated:

H₁. 

There is a negative impact of country risk on banking sector stability.

Afterwards, for in-depth insights, the main hypothesis will be subdivided into three sub-hypotheses:

H_1.1. 

There is a negative impact of political risk on banking sector stability.

H_1.2. 

There is a negative impact of economic risk on banking sector stability.

H_1.3. 

There is a negative impact of financial risk on banking sector stability.

3. Methodology

3.1. Data

The study’s sample comprises a comprehensive census of 102 publicly enlisted banks in active stock markets across 13 MENA nations covering a retrospective period of 41 years from 1984 to 2024. This time frame yields a total of 533 annual observations, so this study employed balanced panel data to improve the statistical power, reliability, and robustness of its findings (Young et al., 2017). The study period can be rationalized by the need for solid analysis of trends and policy responses to cyclical influences of country risk (e.g., political changes, economic fluctuations, and financial shocks) on banking stability during this period. In addition to the shift from state-run banks to market-oriented banks, there is finally the adoption of the Basel Accords, which are expected to have a considerable effect on listed banks’ stability. Several MENA countries were excluded since their stock markets are dormant, while other countries have no stock market at all. This study excluded numerous banks because they delisted from the stock market during the tested period. Appendix A lists the publicly enlisted banks examined in this study, along with each standalone country representation, in Table A1. Secondary data was employed in this study. Bank-level data, including annual reports and financial statements, was obtained from the BankScope database and www.investing.com (accessed on 13 June 2025). Country risk scores were collected from the International Country Risk Guide (ICRG) official website, www.icrgonline.com (accessed on 16 June 2025). which encompasses scores of political, economic, and financial risks besides the country composite risk score.

3.2. Empirical Model

Country risk has three main components (i.e., political, economic, and financial risks); therefore, this study will test the impact of country risk on banking sector stability by employing two different models. Based on literature, the first empirical model was formulated to test the main hypothesis of this study and can be expressed as follows:

$${lnCAMELS}_{it}={\alpha }_0+{\alpha }_1{lnICRG}_{it}+{\sum }_{i=1}^{N}country+{\sum }_{t=1}^{n}years+{\mathsf{\epsilon }}_t$$

(1)

where (${CAMELS}_{it}$) reflects the dependent variable, signifying the banking stability degree within MENA nations at time $t$ (where t = 1, 2, …, n), ${\alpha }_0$ denotes the intercept, and ${\alpha }_1$ reflects the independent variable coefficient—namely, the numerical measure of country risk (${ICRG}_{it}$) in the MENA zone at time $t$. The terms ${\sum }_{i=1}^{N}country$ and ${\sum }_{t=1}^{n}years$ reflect vectors of fixed effects for both countries and time, respectively. Lastly, ${\mathsf{\epsilon }}_t$ mirrors the error term.

The second empirical model will test the three sub-hypotheses generated by the main hypothesis. This model is formulated similarly to the first model, which is presented in its logistic form as follows:

$${lnCAMELS}_{it}={\beta }_0+{\beta }_{1}ln{PR}_{it}+{\beta }_{2}lnER+{\beta }_3{lnFR}_{it}+{\sum }_{i=1}^{N}country+{\sum }_{t=1}^{n}years+{\mathsf{\epsilon }}_t$$

(2)

This indicates that banking sector stability in a country $i$ at a time $t$ is a function of the numerical measures of political risk (${PR}_{it}$), economic risk (${ER}_{it}$), and financial risk (${FR}_{it}$) in the identical country and time. Additionally, the model includes fixed effects vectors for countries (${\sum }_{i=1}^{N}country$) and time periods (${\sum }_{t=1}^{n}years$).

Theoretically, it is anticipated that the composite index of country risk, which includes political, economic, and financial dimensions, will negatively impact the financial stability of the banking sector in MENA nations. Escalated degrees of country risk, regardless of whether they are political, economic, or financial in nature, can undermine the stability of the banking sector by influencing liquidity, asset quality, and earnings—all of which are part of the CAMELS indicators used to assess bank stability. Thus, coefficients (${\alpha }_1$) in model (1), and coefficients (${\beta }_1$), (${\beta }_2$), and (${\beta }_3$) in model (2) are supposedly anticipated to hold a negative sign.

3.3. Variables of This Study

3.3.1. Dependent Variable

The dependent variable is the stability of the banking sector, which refers to the extent to which banks can withstand shocks because banks are vulnerable to absorbing various risks (Anggono, 2020). Many studies (e.g., Kumar & Sayani, 2015; Babu & Kumar, 2017; Nguyen & Vo, 2018) laid the foundation for measuring banking stability depending on the CAMELS framework. Table 1 illustrates six key proxies of the CAMELS model: capital adequacy, asset quality, management, earnings, liquidity, and sensitivity to market risk.

Table 1. Calculation of CAMELS elements with reference.

Element	Measurement
Capital Adequacy (C)	${\displaystyle \frac{\mathrm{R}\mathrm{e}\mathrm{g}\mathrm{u}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{y} \mathrm{c}\mathrm{a}\mathrm{p}\mathrm{i}\mathrm{t}\mathrm{a}\mathrm{l}}{\mathrm{R}\mathrm{i}\mathrm{s}\mathrm{k} \mathrm{W}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{e}\mathrm{d} \mathrm{A}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$
Assets Quality (A)	${\displaystyle \frac{\mathrm{N}\mathrm{o}\mathrm{n}\text{-}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{L}\mathrm{o}\mathrm{a}\mathrm{n}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{l}\mathrm{o}\mathrm{a}\mathrm{n}\mathrm{s}}}$
Management Quality (M)	${\displaystyle \frac{\mathrm{O}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{E}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{I}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$
Earnings (E)	${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t} \mathrm{I}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{t} \mathrm{M}\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{i}\mathrm{n}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{I}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}}$
Liquidity (L)	${\displaystyle \frac{\mathrm{C}\mathrm{a}\mathrm{s}\mathrm{h} \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{D}\mathrm{u}\mathrm{e}\mathrm{s} \mathrm{f}\mathrm{r}\mathrm{o}\mathrm{m} \mathrm{B}\mathrm{a}\mathrm{n}\mathrm{k}\mathrm{s}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{A}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$
Sensitivity to Market Risk (S)	${\displaystyle \frac{\mathrm{B}\mathrm{a}\mathrm{n}\mathrm{k}’\mathrm{s} \mathrm{S}\mathrm{e}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{e}\mathrm{s} \mathrm{P}\mathrm{o}\mathrm{r}\mathrm{t}\mathrm{f}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{o}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{A}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$

3.3.2. Independent Variables

Country risk is the main independent variable, which is provided by the International Country Risk Guide (ICRG) country composite score assigned for each country and published by the Political Risk Services (PRS) Group. Kalkan and Çam (2021) highlighted that country risk score is measured in terms of a composite of three risk subcategories: political, economic, and financial risk scores. According to ICRG (2018), the political risk assessment is based on a scale of 100 points; on the other hand, the economic and financial risk assessments are based on 50 points each. The total scores of the three indices are divided by two to obtain the aggregate country risk score. Thereby, the political risk score contributes 50% to the composite score, while the financial and economic scores each contribute 25%. The following formula is employed to calculate the total political, financial, and economic risk scores, which represent the entire country risk composite rating:

ICRG (country X) = 0.5 (PR_it + ER_it + FR_it)

(3)

where

• CPFER = Composite Political, Financial, and Economic Risk ratings (max. 100 points), PR = Total political risk indicators (max. 100 points), ER = Total economic risk indicators (max. 50 points), and FR = Total financial risk indicators (max. 50 points).

In theory, composite scores range from 0 to 100 points so that greater risk is represented by fewer points, whereas lower risk is reflected by greater points. Table 2 presents a summary of all variables (i.e., dependent and independent) of this study with their associated abbreviations and measurements.

Table 2. Measurement of variables.

Type	Name	Abbreviation	Measurement
Dependent Variable	Banking Sector Stability	${CAMELS}_{it}$	Measured in terms of 6 CAMELS dimensions: Capital Adequacy, Asset Quality, Management Quality, Earnings Quality, Liquidity, and Sensitivity to Market Risk.
Independent Variable	Country Risk aggregate	${ICRG}_{it}$	Integrates 3 main dimensions: political, economic, and financial risk. Country risk composite score. ICRGit (country X) = 0.5(PRit + ERit + FRit)
	Political Risk	${PR}_{it}$	Include government stability, bureaucratic quality, corruption, internal conflict, military involvement in politics, law and order, religious and ethnic tensions, and coups. (Max. 100 points).
	Economic Risk	${ER}_{it}$	Encompasses GDP per capita, real GDP growth rate, inflation rate, current account balance, and unemployment rate. (Max. 50 points).
	Financial Risk	${FR}_{it}$	Consists of balance as a percentage of GDP, total external debt, debt service, foreign reserves, and the structure of external debt. (Max. 50 points).

4. Results and Discussion

4.1. Descriptive Analysis

To fully comprehend the nature and characteristics encompassed in this study, proper descriptive statistics were employed, such as the mean, median, standard deviation, minimum, maximum, and finally the normality test. Table 3 presents the results of the descriptive statistics for all study variables.

Table 3. Descriptive summary statistics, 1984–2024 (n = 13).

	Obs.	Mean	Median	Std. Dev.	Min	Max	Normality Test
Dependent Variable:
Banking sector stability (CAMELS Rating)	533	3.8143	4	0.3893	3	4	240.34 ***
Banking sector stability (CAMELS Score)	533	0.2684	0.2708	0.0182	0.1782	0.2971	764.90 ***
Independent Variables:
Country risk aggregate (ICRG)	533	30.135	27.854	10.467	12.417	68.958	68.887 ***
Political risk (PR)	533	38.909	35.550	13.190	20.667	91.5	131.00 ***
Economic risk (ER)	533	60.987	59.583	6.9021	46.333	85	64.986 ***
Financial risk (FR)	533	60.927	60.750	7.1751	42.417	85.917	19.547 ***

Note: *** indicates significance at 1%.

Table 3 presented a substantial variation in the observed measures of both the stability of banks and the dimensions of country risk, which is deep-rooted in the results of the normality test. This variance is statistically significant at the 1% level. The average CAMELS rating of 3.81 (standard deviation = 0.39) and the average CAMELS score of 0.268 (standard deviation = 0.018) reflect moderate financial stability in the banking sector across MENA countries. On the other hand, the widespread range between minimum and maximum values reflects considerable differences in the degree of banking resilience. Moreover, the results pointed to the average country risk composite (ICRG) being 30.14, which generally represents a moderate risk atmosphere. Risk dimensions demonstrated that political risk is relatively high (mean = 38.91), while economic risk (mean = 60.99) and financial risk (mean = 60.93) are boosted, proposing that MENA banks are more highly exposed to economic and financial fluctuations than political risk. This statement applies specifically to the country’s composite risk. The outcomes asserted risk exposure heterogeneity across MENA countries and their related banking sector, which highlights keeping in mind the multidimensions of country risk while assessing banking stability determinants.

4.2. Variance Analysis

The results of the Kruskal–Wallis test are presented in Table 4, which reveals statistically significant differences among MENA countries across all variables of this study at the 1% level, highlighting heterogeneity in both the stability of the banking sector and the dimensions of country risk.

Table 4. Analysis of variance between MENA Countries.

	CAMELS Rating	CAMELS Score	ICRG	PR	ER	FR
Bahrain	3.9756	0.2731	26.792	36.000	60.840	56.660
Egypt	3.7805	0.2667	32.340	41.660	64.458	57.167
Iraq	3.1707	0.2474	43.292	63.917	59.570	65.000
Jordan	3.8537	0.2674	27.225	35.010	58.550	61.000
Kuwait	3.8293	0.2777	25.583	30.292	64.083	46.583
Lebanon	3.7805	0.2648	29.458	44.120	65.083	62.000
Morocco	3.9024	0.2779	21.285	31.792	57.870	53.667
Oman	4	0.2822	20.055	27.792	55.292	61.417
Palestine	3.5122	0.2599	40.458	38.750	72.750	71.958
Qatar	4	0.2849	20.521	28.000	55.610	58.708
Saudi Arabia	4	0.2683	26.583	33.750	54.750	61.917
Tunisia	3.7805	0.2711	30.320	34.860	65.000	62.840
UAE	4	0.2825	20.130	24.083	54.833	61.167
Kruskal–Wallis	83.775 ***	198.92 ***	252.79 ***	236.35 ***	327.33 ***	258.65 ***

Note: *** indicate significance at 1%.

Saudi Arabia, Qatar, and the UAE have the highest CAMELS ratings, with a score of 4, reflecting a high degree of banking stability due to a strong regulatory framework, institutional resilience, financial abundance, and strong integration with global markets. In contrast, Palestine and Iraq conveyed significantly lower degrees of stability, highlighting a noteworthy gap in the banking robustness and capacity of risk management. This divergence underlines the basic disparities in the efficiency of banking supervision and the wider political and economic context of each country. Remarkably, country risk varies, as Palestine and Iraq have the highest aggregate ICRG points (40.46 and 43.29 points, respectively), whereas Oman, Qatar, and the UAE have the lowest. Political risk had high severity in Lebanon and Iraq, which is consistent with instabilities, while Qatar and the UAE recorded the lowest scores, representing strong political stability. Lebanon, Tunisia, and Palestine suffer from growing economic variabilities, mirroring poor economic growth and financial distresses. Meanwhile, Saudi Arabia and the UAE have more favorable economic situations. Iraq and Palestine recorded the highest financial risk, mirroring debt problems, fiscal shortages, and aggregate spending inefficiencies. Conversely, Morocco and Kuwait have the lowest financial risk scores. Generally, the results uncovered cross-country dissimilarities that represent political, macroeconomic, and financial heterogeneity within MENA countries.

4.3. Correlation Analysis

The correlation analysis of Pearson’s zero-order was employed to test the hypothesized relationship between study variables, as demonstrated in Table 5. The findings highlighted the structural verification of the proposed framework.

Table 5. Correlation matrix between study variables, 1984–2024 (n = 13).

		1	2	3	4	5	6
CAMELS Rating	(1)	1
CAMELS Score	(2)	0.743 ***	1
Country risk aggregate	(3)	−0.616 ***	−0.700 ***	1
Political risk	(4)	−0.588 ***	−0.715 ***	0.880 ***	1
Economic risk	(5)	−0.389 ***	−0.428 ***	0.679 ***	0.425 ***	1
Financial risk	(6)	−0.411 ***	−0.452 ***	0.636 ***	0.473 ***	0.351 ***	1

Note: *** indicates significance at 1%.

The correlation matrix identifies a strong positive correlation between the quantitative CAMELS score and the ordinal CAMELS rating, which is statistically significant at the level of 1% with a correlation coefficient of 74.3%. This result indicates the internal consistency of both approaches in capturing the banking stability phenomenon across MENA countries. From a regulatory perspective, this convergence offers policymakers and banking supervisory authorities more methodological flexibility; either measure can be employed based on data availability and assessment objectives. Finally, the results underline the robust validity of the CAMELS model to reflect a comprehensive assessment of the financial stability of banks.

Across MENA countries, the findings address the solid negative association between country risk, employing the ICRG methodology, and bank stability. Correlations with the CAMELS score (−61.6%) and the CAMELS rating are stronger (−70%), which are statistically significant at the level of 1%. These results empirically support the main hypothesis of this study. Particularly, political risk has the strongest negative correlation with stability at −58.8% and −71.5% for quantitative and ordinal CAMELS, respectively. Therefore, political stability plays a critical role in driving banking resilience. Although economic risk is significant, it has a relatively weaker influence (−38.9% and −42.8%), suggesting that exchange rate volatility and sluggish growth can undermine banking stability. Finally, both quantitative and ordinal CAMELS measures showed that financial risk has a moderate negative effect on banking stability, with correlation coefficients of −41.1% and −45.2%, respectively. This conclusion implies that foreign debt, exchange rate volatility, and deficit can jeopardize the stability of banks. Conclusively, the results assert that escalated political uncertainty, economic imbalances, and financial fragility can undermine the soundness of the banking sector. Therefore, integrating country risk indicators into early warning frameworks is an important step for risk mitigation and regulatory bodies in volatile surroundings. This incorporation can be operationalized through several mechanisms, such as country credit ratings, CDS spreads, recommended early warning systems (EMS) by the Basel Committee and IMF, and stress tests with different scenarios of country risk to continuously monitor political, economic, and financial risks to automatically alter banks when the level of country risk exceeds predetermined thresholds.

The correlation among country risk dimensions—political (PR), economic (ER), and financial risk (FR)—ranked from weak to moderate and remained below the 0.70 threshold proposed by Anderson and Gerbing (1990) for multicollinearity problems. This result validates the structural soundness of the main study model and reinforces the confidence in the subsequent regression analysis.

4.4. Regression Analysis

4.4.1. Estimation Strategy

For properly assessing the impact of country risk on banking stability, this study employs the Random Effects Model (REM) moving toward Dynamic Panel Data (DPD). The aim of this two-phase study is to provide an initial benchmark of the country risk role in shaping banking stability in addition to assessing whether the internal instruments used subsequently in the DPD technique exert direct impacts on the stability of banks or whether their effects are conditional on country-specific risk. The REM begins with the following equation:

$$Y_{it}={\beta }_{1i}+{\beta }_2{X}_{2it}+\cdots +u_{it}$$

Instead of dealing with ${\beta }_{1i}$ as a constant (as in the Fixed Effects Model), the REM assumes ${\beta }_{1i}$ as a random variable with an expected value of ${\beta }_1$ (without the subscript $i$). The intercept for any country can hence be formulated as follows:

$${\beta }_{it}={\beta }_1+{\epsilon }_{i}i=1,2,\dots ,N$$

where ${\epsilon }_i$ represents the term of the random error term with an expected zero value and a variance equal to ${\sigma }_{\epsilon }^2$ which implies that each country in this study is sharing a common expected intercept value ${\beta }_1$. Accordingly, the differences between standalone countries in the intercept are represented by the error element ${\epsilon }_i$. Therefore, the regression can be expressed as follows:

$$\begin{array}{ll}{Y}_{it}& ={\beta }_{1i}+{\beta }_2{X}_{2it}+\cdots +{\epsilon }_i+u_{it}\\ & ={\beta }_{1i}+{\beta }_2{X}_{2it}+\cdots +{\omega }_{it}\end{array}$$

where

$${\omega }_{it}={\epsilon }_1+u_{it}$$

The composite error term ${\omega }_{it}$ contains two elements: ${\epsilon }_1$ captures the unobserved country-specific error component, while $u_{it}$ represents idiosyncratic error associated with the combination of time series with cross-sectional data. Therefore, the model designation as an “error components model” mostly stems from the error term of the composite ${\omega }_{it}$, which consists of two (or more) discrete error elements. When time has a statistically significant influence on regression, the estimation methodology naturally expands to the two-way REM specification (Gujarati, 2003).

4.4.2. Regression Statistics

Based on the nature of the data, the analysis was employed via the statistical packages E-Views 13 and Gretl 2025. The regression analysis reveals a significant difference in how effectively country risk explains the stability of MENA banks. Adjusted R² indicates that country risk, measured by ICRG, has robust prediction power, approximately explaining 32.7% and 42.1% of the variance in the ordinal and numerical CAMELS models, respectively, as exhibited in Table 6. Moreover, country risk is decomposed into political, economic, and financial dimensions; country risk collectively explains 30.7% and 53.6% of the variance in ordinal and numerical CAMELS-based measures. The results are confirmed by Fisher’s F-tests, which are statistically significant at the 1% level across ordinal and numerical models, which reinforces the validity and strength of the association between country risk factors and banking stability.

Table 6. Country risk and Banking sector stability: REM.

Dependent Variables: Banking Sector Stability
Method: 2-Way Random Effects Model (with White Diagonal Standard Error)
	CAMELS Ratings		CAMELS Score
	Reg (1)	Reg (2)	Reg (3)	Reg (4)
ln Country risk	1.5575 [6.967] ***		0.8431 [4.438] ***
ln Country risk squared	−0.2563 [−7.475] ***		−0.1455 [−4.970] ***
ln Political risk		1.6947 [8.381] ***		1.2682 [5.585] ***
ln Political risk squared		−0.2468 [−8.597] ***		−0.1893 [−5.958] ***
ln Economic risk		−0.1707 [−3.057] ***		−0.1280 [−4.436] ***
ln Financial risk		−0.1683 [−3.586] ***		−0.0733 [−3.293] ***
Constant	1.5575 [6.967] ***	−0.1527 [−0.280]	−2.4931 [−8.110] ***	−2.5841 [−6.004] ***
Key Regression Statistics
R-squared	0.3296	0.3123	0.4230	0.5395
Adjusted R-squared	0.3271	0.3071	0.4209	0.5360
DW stats.	0.8635	0.8455	1.0945	1.1687
Fisher test ($F$-stats.)	130.29 ***	59.946 ***	194.30 ***	154.67 ***
Practical significance for Banking Sector Stability determinants: Effect Size
ln Country risk	0.6058		0.3859
ln Country risk squared	−0.6500		−0.4322
ln Political risk		0.7302		0.4865
ln Political risk squared		−0.7490		−0.5191
ln Economic risk		−0.2663		−0.3865
ln Financial risk		−0.3124		−0.2869

Note: *** indicates significance at 1%. t-Statistic in parentheses.

REM can be significantly biased in its estimates due to simultaneity between country risk and banking stability. Consequently, the Dynamic Panel Data (DPD) technique, developed by Arellano and Bond (1991), was utilized to support findings while addressing individual differences.

The diagnostic tests validate the robustness of the DPD estimate because the Sargan test confirms the validity of instruments as exhibited in Table 7. In the meantime, the Wald test sets up the mutual statistical significance of the explanatory variables at a 1% degree. The results of DPD are highly stable, robust, and consistent with the random effects model, with fewer changed outcomes across all regression models, meaning the DPD model offers a more precise reflection of the relationship’s real nature. From the policy standpoint, the persistent inverse influences of economic and financial risks on banking stability underline the crucial need for solid macroeconomic policies and institutional governance for mitigating vulnerabilities and maintaining banking resilience in MENA countries. Thus, central banks and regulatory authorities are required to adopt a comprehensive approach when evaluating country risk.

Table 7. Country risk and Banking sector stability: GMM.

Dependent Variables: Banking Sector Stability
Method: 1-Step Dynamic Panel Data (with Asymptotic Standard Errors)
	CAMELS Ratings		CAMELS Score
	Reg (5)	Reg (6)	Reg (7)	Reg (8)
ln Banking sector stability (−1)	0.6246 [24.610] ***	0.6268 [25.37] ***	0.5515 [19.48] ***	0.4241 [15.44] ***
ln Country risk	0.5947 [4.484] ***		0.2471 [3.067] ***
ln Country risk squared	−0.1006 [−5.027] ***		−0.0477 [−3.925] ***
ln Political risk		0.7896 [5.104] ***		0.5745 [6.055] ***
ln Political risk squared		−0.1161 [−5.478] ***		−0.0891 [−6.803] ***
ln Economic risk		−0.0823 [−3.733] ***		−0.0654 [−4.848] ***
ln Financial risk		−0.0471 [−2.318] ***		−0.0406 [−3.277] ***
Constant	−0.3465 [−1.652] *	−0.2919 [−1.057]	−0.9374 [−6.497] ***	−1.2301 [−6.697] ***
Key Regression Statistics
No. of instruments	283	283	456	456
Sargan over-identification	375.44 ***	374.29 ***	380.33 ***	400.29
Wald (joint) test	1729.90 ***	1753.60 ***	1416.50 ***	1635.40 ***
Wald (time dummies)	102.76 ***	100.42 ***	62.88 ***	69.519 ***
Practical significance for Banking Sector Stability determinants: Effect Size
ln Country risk	0.4110		0.2812
ln Country risk	−0.4608		−0.3598
ln Political risk		0.4689		0.2812
ln Political risk squared		−0.5032		−0.3598
ln Economic risk		−0.3429		0.2812
ln Financial risk		−0.2129		−0.3598

Note: *** and * indicates significance at 1% and 10%, respectively. t-Statistic in parentheses.

Results of Impact of Country Risk on Banking Sector Stability

The results of the first regression model, which corresponds to the initial empirical model, showed that country risk, as measured by ICRG, significantly impacts banking stability, assessed using the ordinal CAMELS index in MENA nations. Both the coefficient and squared terms of country risk are statistically significant at the 1% degree, reflecting the existence of a nonlinear association between country risk and the stability of banks. Particularly, this nonlinear relationship takes the shape of an inverted U-shaped relationship, indicating the impact of country risk on banking stability varies across various risk exposure degrees. The impact is positive at a relatively low country risk level, underlining the enhancement in the political, economic, and financial condition, measured by the ICRG composite, appearing to improve banking stability via increased investor confidence, fostering capital inflows, and refined credit allocation. However, beyond a specific threshold, the impact reverses to become negative because higher macro-political and economic instabilities erode the creditworthiness of banks, subsequently deteriorating banking stability.

The Sasabuchi–Lind–Mehlum (SLM) test was employed to validate this nonlinear association as exhibited in Table 8. The results of the test were consistent with the null hypothesis of an inverse U-shaped nexus; in addition, the estimated turning point of country risk was 20.9 (or 3.0389 in logarithmic form), which falls inside the observed range of ICRG index data. Remarkably, 22.1% of the observations fall beneath this threshold, inferring quite low degrees of country risk positively impact the banking stability, whereas 77.9% of the observations exceeded this threshold, suggesting, in the majority of cases, high country risk negatively influences the stability.

Table 8. Sasabuchi–Lind–Mehlum test for an inverse U-shaped relationship of country risk with banking stability.

		CAMELS Ratings		CAMELS Score
		Reg (1)	Reg (2)	Reg (3)	Reg (4)
		Country Risk	Political Risk	Country Risk	Political Risk
${\mathrm{X}}_{\mathrm{i}}$	$\widehat{\mathsf{\beta }}$ =	1.5575 [6.967] ***	1.69465 [8.381] ***	0.84312 [4.438] ***	1.26822 [5.585] ***
${\mathrm{X}}_{\mathrm{i}}^2$	$\widehat{\mathrm{y}}$ =	−0.25626 [−7.475] ***	−0.24683 [−8.597] ***	−0.14551 [−4.970] ***	−0.18931 [−5.958] ***
Interval	Xi (max) =	2.519	3.0285	2.519	3.0285
	Xh (max) =	4.2335	4.5163	4.2335	4.5163
Slope at Xi	$\widehat{\mathsf{\beta }+2}\widehat{\mathrm{y}\mathrm{X}\mathrm{i}}$	2.519 [3.846] ***	0.1996 [5.617] ***	0.11 [3.465] ***	0.11 [3.465] ***
Slope at Xh	$\widehat{\mathsf{\beta }+2}\widehat{\mathrm{y}{\mathrm{X}}_{\mathrm{h}}}$	−0.612 [−4.876] ***	−0.535 [−4.378] ***	−0.389 [−4.612] ***	−0.442 [−4.328] ***
Sasabuchi test (t-value)		[1.056]	[0.946]	[1.207]	[1.307]
Extremum Point	$\widehat{-\mathsf{\beta }/2(}\widehat{\mathrm{y})}$	3.0389	3.4328	2.897	3.3496
		Extremum inside interval	Extremum inside interval	Extremum inside interval	Extremum inside interval

Note: *** indicates significance at 1%.

Moreover, the robustness of the results was authenticated by the third regression model, which employed the numerical CAMELS index as an alternative measurement tool of banking stability. Grippingly, the findings confirmed the existence of a nonlinear, inverse U-shaped association between country risk and banking sector stability. The assurance between numerical and ordinal CAMELS suggests the observed shape reflects a dynamic pattern linking country risk and banking sector stability. The consistency of results across both models reinforces the estimates’ credibility and underlines the strategic importance of country risk as a structural determinant of the financial resilience of the banking sector. MENA countries, which are exposed to significant fluctuations in their political and economic stability, find this data particularly relevant. These results assert the great need to systematically embed the assessment of country risk in the core essence of banking risk management, especially within volatile or fragile atmospheres.

Results of Impact of Country Risk Dimensions on Banking Sector Stability

The second empirical model of this study, which is represented by the second regression model, examines how the three dimensions of country risk (i.e., political, economic, and financial) impact banking stability in MENA nations using the ordinal CAMELS index, revealing different patterns across risk categories. The results uncovered an inverse linear influence of both economic and financial risks. Based on the regression coefficient, a 1% increase in economic risk is attached to a 17.1% banking stability reduction, whereas a 1% increase in financial risk is associated with a 16.8% decrease in stability. These results address the direct vulnerability of the banking sector to deteriorated macroeconomic conditions and fiscal pressures or imbalances, all of which can destabilize asset quality, undermine loan recovery rates, and weaken confidence in the financial system.

Conversely, the political risk demonstrated a different dynamic via a nonlinear inverted U-shaped association with banking stability. This suggests that at a relatively low political risk level, political stability has a positive influence on banking stability via enhanced investor confidence, stronger financial oversight, and improved banking efficiency. However, if the political risks rise above the critical threshold, their influence becomes negative; therefore, banks can face several pressures in terms of escalated funding costs, reversed gains, eroded credibility, and worsened overall banking resilience.

The Sasabuchi–Lind–Mehlum (SLM) validated the existence of this nonlinear nexus (inverted U-shaped curve), as demonstrated in Table 9, with an estimated turning point of 30.97 (3.4327 in logarithmic terms), which lies inside the actual data range. Approximately 34% of the observations fell below this threshold, indicating that low political risk positively influences banking stability. While 66% of the observations exceed this threshold, it implies that elevated political risk adversely impacts banking soundness in most cases.

Table 9. Sasabuchi–Lind–Mehlum test for an inverse U-shaped relationship of political risk with banking stability.

		CAMELS Ratings		CAMELS Score
		Reg (5)	Reg (6)	Reg (7)	Reg (8)
		Country Risk	Political Risk	Country Risk	Political Risk
${\mathrm{X}}_{\mathrm{i}}$	$\widehat{\mathsf{\beta }}$ =	0.59473 [4.484] ***	0.78964 [5.104] ***	0.24709 [3.067] ***	0.57453 [6.055] ***
${\mathrm{X}}_{\mathrm{i}}^2$	$\widehat{\mathrm{y}}$ =	−0.10057 [−5.027] ***	−0.1161 [−5.478] ***	−0.04765 [−3.925] ***	−0.08913 [−6.803] ***
Interval	Xi (max) =	2.519	3.0285	2.519	3.0285
	Xh (max) =	4.2335	4.5163	4.2335	4.5163
Slope at Xi	$\widehat{\mathsf{\beta }+2}\widehat{\mathrm{y}\mathrm{X}\mathrm{i}}$	0.0881 [4.327] ***	0.0864 [3.297] ***	0.007 [4.028] ***	0.0347 [3.289] ***
Slope at Xh	$\widehat{\mathsf{\beta }+2}\widehat{\mathrm{y}{\mathrm{X}}_{\mathrm{h}}}$	−0.257 [−3.469] ***	−0.259 [−3.079] ***	−0.156 [−3.279] ***	−0.231 [−5.479] ***
Sasabuchi test (t-value)		[0.967]	[1.001]	[0.634]	[1.634]
Extremum Point	$\widehat{-\mathsf{\beta }/2(}\widehat{\mathrm{y})}$	2.9568	3.4007	2.519	3.2229
		Extremum inside interval	Extremum inside interval	Extremum inside interval	Extremum inside interval

Note: *** indicates significance at 1%.

The fourth regression model results, using the numerical CAMELS index, reinforced the second model’s reliability. The findings emphasized the existence of an inverted U-shaped nexus between political stability and the stability of banks, besides the persistent negative linear relationship between economic and financial risks and banking stability. This consistency moves beyond ordinal toward numerical measures, underscoring the credibility of the empirical models and robustness of results. This implies a structural, rather than methodological, dynamic association between country risk dimensions and banking stability regardless of the metric used.

Collectively, these findings reveal that the potential positive impact of a low degree of political risk can quickly reverse in the face of escalated political instability. This requires adaptive regulatory response and flexible banking oversight to quickly respond to political atmosphere shifts. Furthermore, the sustained negative impact of economic and financial risks asserts the necessity of strengthening the capability of banks to absorb economic turmoil, preserve asset quality from deficit-related volatility, and effectively handle pressures caused by inflation. The recurrence of these patterns across different models highlights the crucial role of incorporating political and economic risk assessments into bank planning and wider economic policymaking to maintain the long-term robustness of banks, especially in MENA, which is characterized by high volatility in political and economic conditions.

4.4.3. Discussion

The empirical evidence indicates that the country risk composite negatively impacts banking stability with an inverted U-shaped association. The results are correspondent to the findings of Huang and Lin (2021), who identified that a low level of country risk improves banking stability within emerging countries, whereas a high degree of country variability can deteriorate financial robustness. In the same vein, Fiordelisi et al. (2020) demonstrated that the inverse impact of country stress on bank stability is nonlinear and becomes exaggerated beyond certain systematic shocks, such as political, economic, or fiscal turmoil. With relatively low country risk, the enhancements in political stability, economic growth, and fiscal structures boost efficient capital allocation by banks, attract capital inflows, and maintain investor confidence. Nevertheless, after a certain threshold, escalated country instability depresses earnings, deteriorates credit quality, and amplifies the probability of insolvency. Both institutional adaptation and market dynamics during elevated country risk can properly explain the inverted U-shape. At low country risk, banking institutions are typically able to amend regulations to absorb shocks and capitalize on moderate-risk practices to improve banking stability. This surrounding atmosphere incentivizes reasonable risk-taking activities, financial innovation, and credit allocation. However, as risk exceeds the critical threshold, banking stability can be jeopardized by elevated country risk due to low confidence of investors, capital flights, higher cost of borrowing, and escalated risk premiums, which complicate the fiscal situation of banks. Therefore, the stabilizing influence of country risk on banking stability can be reversed and make banks more fragile to country risks. Thus, the inverted U-shaped pattern highlights how country risk can act both as a stabilizer and a destabilizer for the banking sector in MENA nations, depending on the level of country risk.

The findings address the fact that political risk is the dominant determinant of banking steadiness through nonlinear channels and agree with Chen et al. (2015), who highlighted that high political insecurity increases the probability of Asian banks defaulting. Furthermore, Ghosh (2016), who advocates political instability, shrinks the credit offerings of MENA banks. Similarly, Compaoré et al. (2020) highlighted political turbulence, intensifying the financial fragility of banks through declining credit growth. The findings of this study align with these studies and extend them through quantifying the nonlinear turning point. Lower borrowing costs and stronger capital adequacy are associated with low political risk. On the contrary, high political variabilities erode asset quality and limit credit offerings. Simultaneously, the results argued with Athari (2022), who demonstrated banks effectively adapt to long-lasting political stability. However, the present empirical evidence addressed that modest political stability fosters robustness, while excessive political variability can be destructive. Therefore, the observed nonlinear impact implies the partial adaptation of banks to political risk beyond a critical threshold because even well-capitalized banks inevitably confront systematic pressure, and financial fragility can emerge regardless of adaptation plans.

As for economic risk, the evidence shows a steady negative linear impact of economic risk on the financial stability of MENA banks. This result complies with Sufian (2012), who discovered economic disruptions destabilize the solidity of South Asian banks, and it agrees with Wu et al. (2020), who addressed how major economic downturns can expand deleveraging activities, which minimize loan offerings. Saliba et al. (2023) highlighted that macroeconomic disturbances expand the financial fragility of the banking sector in BRICS countries. This trend is due to factors such as fiscal imbalances, poor GDP growth, hyperinflation, and an increase in the non-performing loan (NPL) ratio. However, the results disagree with Podstawski and Velinov (2018), who argued that the economic risk impact may be in the short run and usually offset by counter-cyclical responses. The sustained negative influence of economic risk on MENA banks implies weaker turmoil absorption compared to highly diversified economies because MENA states have narrow fiscal techniques and limited monetary tools.

Furthermore, the findings signify financial risk has an adverse linear effect on the financial soundness of banks. These results aligned with Albertazzi et al. (2014) and Altavilla et al. (2017), who displayed that financial risks of a country diminish banking stability through depressing asset values and increasing cost of funding. Typically, Andreeva and Vlassopoulos (2019) underscored that the downgraded financial stability of a country straightforwardly translated into banking fragility. That is why financial risk has an inverse linear impact on exchange rate volatility, balance of payments deficits, and external debt burden, all of which undermine bank stability. Banks heavily rely on government securities, so any potential sovereign distress can affect their solvency. This consistency magnifies the banking vulnerability with respect to financial risk; therefore, financial risk is not only a dummy variable but also has a direct destabilizing impact on banking robustness. Nevertheless, the current study findings diverge from Engler and Große-Steffen (2016), who realized that some banks may take advantage during financial supersession in a high-risk climate. Exchange rate volatility and elevated foreign debts significantly expose MENA countries to financial risks, rendering banks less stable and highlighting their continued detrimental nature.

Finally, this study discloses prominent heterogeneity across MENA states regarding bank responses to country risk. The Gulf countries demonstrate robust banking stability because of solid regulatory oversight and sufficient liquidity, whereas countries with frequent conflicts, such as Palestine and Iraq, are still financially fragile. This heterogeneity between MENA banking stability can be attributed to high oil revenues, sovereign wealth funds, expanded foreign reserves, a stable exchange rate system, and diversified strategies of investments. These factors enable Gulf banking institutions to effectively manage the increasing risks associated with their respective countries. The results support Ben Naceur and Omran (2023), who addressed the unequal impacts of country risk across MENA countries. Furthermore, the findings support the Triki et al. (2017) argument that fragile countries with poor banking resilience are disproportionately impacted by country risk via political, economic, and financial channels.

5. Conclusions and Recommendations

This paper has tested the impact of country risk on banking stability in the MENA countries, focusing on political, economic, and financial dimensions. The outcomes present solid evidence that country risk is not a peripheral matter but a key determinant of financial rigidity. The results displayed that modest improvements in country risk conditions can improve bank stability, while excessive country risk depreciates confidence, solvency, and asset quality. The analysis emphasizes that political risk has the dominant and most complicated effect on banking stability via nonlinear technique. Political stability initially solidifies banking stability, but severe deterioration occurs when political volatilities escalate beyond a controllable threshold. Conversely, both economic and financial risk exhibited a consistent negative impact on bank stability, representing the structural vulnerabilities of the banking sector to economic downturns, sovereign debt stress, and fiscal imbalances. Moreover, the findings indicate significant variations in bank stability among MENA countries, with oil-dependent countries showing much stronger stability. While countries with fiscal problems However, countries grappling with fiscal issues and regional conflicts continue to exhibit persistent fragility. A customized and sensitive strategy for risk management is necessary, rather than relying on generic mechanisms. The analysis theoretically strengthens the grasp of sovereign risk theory by tackling the complex and nonlinear pathways that translate domestic vulnerabilities into systematic banking fragility. In practical terms, the evidence highlights the necessity for regulators, policymakers, and banking executives to integrate the assessment of country risk into their risk mitigation strategies. Proactive measures of fiscal burdens, political governance, and banking oversights are necessary to ensure long-run banking resilience and mitigate systematic risks. In due course, the finding implies that maintaining banking stability within a highly volatile environment moves beyond just liquidity buffers and capital sufficiency; it requires formulating comprehensive policies that confront the root causes of country turbulences. For practical implications, these findings underline that effective practices of risk management of MENA banks are not only a one-size-fits-all dynamic, but also banking executives are recommended to incorporate country risk in the banking risk assessment framework. As a result, banking executives must enhance the quality of assets, solvency, loan offering strategies, diversification approach, and capital buffers in response to elevated country risk that exceeds the established threshold. Furthermore, regulators have to pay considerable attention to the periodic reports that address the level of political risk in each country and take the necessary proactive actions to mitigate banking exposure and minimize the probability of financial fragility. Similarly, policymakers must develop early-warning systems and formulate adequate interventions to effectively manage different country risks because their influences are nonlinear on banking stability. By integrating those policies with regulatory and institutional guidelines, MENA states can have a more resilient banking sector, which withstands political, economic, and financial risks.

Therefore, the paper offers numerous recommendations for future research to strengthen the generalizability and applicability of findings. First, it is recommended to test the impact of country risk on state-owned banks, not only publicly listed banks, to better represent the structural diversity of banks within MENA countries, particularly since non-listed banks have the majority of market share. Additionally, future studies must explicitly differentiate between conventional and Islamic banks in terms of their responses to elevated country risk. The latter factor is due to the unique governance structures, risk-sharing strategies, and regulatory frameworks of Islamic banks, which can lead to varying levels of sensitivity to country risk. Additionally, it is recommended to replicate this study in other emerging regions, territories, and countries to determine if the results of the current analysis comply with or argue against those of the proposed study. At last, it is highly recommended to incorporate additional tools for measuring country risk, such as sovereign credit ratings, to test their impact on banking resilience as well. This paper aims to provide an in-depth understanding of country risks in MENA countries and their impact on banking sector stability. This paper also sets the stage for various future research endeavors, with the goal of gradually enhancing the body of academic and practical knowledge.