Systemic Risk and Commercial Bank Stability in the Middle East and North Africa (MENA) Region ^†

Abstract

Using panel data spanning 2004–2023 of 21 countries in the MENA (Middle East and North Africa) region, we measure systemic risk and assess its influence on key banking sector performance indicators, including financial stability (proxied by commercial bank branches per 100,000 adults), providing evidence from the emerging market context. One of the key findings of the study is the pivotal role played by financial access in promoting banking stability. In particular, the density and outreach of commercial banking branches were shown to have a stabilizing effect on the banking system. Also, findings reveal that systemic risk significantly undermines bank stability and operational efficiency while constraining financial depth. The study contributes to the literature by offering empirical evidence on the adverse effects of systemic risk in a region characterized by financial volatility and structural vulnerabilities. These findings align with existing global evidence that links financial development with reduced systemic risk, yet they also offer new empirical insights that are contextually relevant to the MENA region. The findings provide actionable recommendations for policymakers. Regulatory authorities in the MENA region should consider strategies that not only enhance the robustness of financial institutions but also promote inclusive access to banking services. The dual focus on institutional soundness and outreach could serve as a cornerstone for sustainable financial stability. Tailored policies that encourage branch expansion in underserved areas, coupled with incentives for inclusive banking practices, may yield long-term benefits by reducing the concentration of risk and improving the responsiveness of the financial system to external shocks.

1. Introduction

The financial crises of 2007–2009, also known as the Global Financial Crisis (GFC), was a classic financial and economic catastrophe that had its origin in the United States due to the bursting of the U.S. housing bubble, leading to a severe contraction of liquidity (https://www.britannica.com/topic/liquid-asset) (accessed on 10 June 2025) (Brunnermeier 2009).

At its peak in 2009, U.S. households alone lost approximately $20 trillion in financial assets, a staggering collapse that underscored the crisis’s systemic severity (Board of Governors of the Federal Reserve System 2011).

The crisis was caused by the convergence of many factors, including the bursting of the US housing bubble, risky subprime mortgage lending, complex financial products, and inadequate regulation. This led to a collapse of mortgage-backed securities (MBS) and a subsequent financial crisis that spread worldwide, triggering a series of defaults, foreclosures, and financial losses that surged through the global financial system, affecting banks and financial institutions worldwide. The crisis severely impacted many banks’ stability, leading to a slowdown in the lending market and subsequent bank failures. Government intervention, including bailouts and fiscal stimulus, was crucial in preventing a complete collapse of the financial system and stabilizing the economy in many countries. It was one of the longest and deepest economic downturns in many countries. The crisis dramatically elevated awareness of systemic risk’s critical role in financial stability. The subprime mortgage crisis in particular served as a stark revelation, exposing fundamental weaknesses in our ability to understand, measure, and mitigate system-wide financial vulnerabilities (Roubini 2009). In contemporary finance, institutions are classified as systemically important when their potential failure could trigger cascading effects throughout the entire financial network. This narrative provoked significant academic interest, leading to the development of numerous systemic risk measurement methodologies and a substantial body of research examining these risks across diverse financial systems and economic conditions in the post-crisis era.

The Middle East and North Africa (MENA) region, characterized by economic volatility, political uncertainties, and dependence on oil revenues, presents a strong case for studying the impact of systemic risk on commercial banks. The stability of commercial banks, including financial institutions, is essential for economic growth, particularly in emerging markets where financial systems are still in an embryonic stage. However, systemic risk is the potential for widespread disruption due to interconnected financial failures and poses a significant threat to banking sectors worldwide. Despite the region’s growing financial integration, there remains limited empirical evidence on how systemic risk influences bank efficiency, stability, and depth and contagion effect if any in the future, therefore this study attempts to fill the vacuum by analyzing the relationship between systemic risk and key banking performance indicators across MENA countries’ economies.

Systemic risk can destabilize financial institutions through contagion effects, liquidity shortages, and solvency crises, ultimately reducing bank efficiency and restricting financial intermediation. Prior research has extensively examined systemic risk in advanced economies (Acharya et al. 2010; Tobias and Brunnermeier 2016), but fewer studies focus on emerging markets, where institutional weaknesses and regulatory gaps may amplify vulnerabilities. The MENA banking sector is particularly susceptible due to its exposure to commodity price shocks, geopolitical risks, and underdeveloped risk management frameworks. Understanding how systemic risk affects bank performance in this context is essential for policymakers and regulators seeking to enhance financial resilience.

Contemporary global banking systems face escalating systemic risk exposures, creating conditions of financial instability that threaten to precipitate renewed economic crises (Bougheas and Kirman 2018; Foglia and Angelini 2021; Rizwan et al. 2020). Modern financial theory conceptualizes systemic risk through the expected capital shortfall framework, where a financial institution’s vulnerability is measured by its projected capital deficiency during system-wide distress scenarios (Acharya et al. 2010; Stefan and Karina 2021). This methodology, which evaluates the differential between a bank’s market valuation and its target capital threshold (Matthew and Agathe 2021), has gained prominence due to its reliance on publicly available market data that facilitates regulatory monitoring and academic analysis (Choi et al. 2020; Schweizer 2021).

Empirical analysis demonstrates that the Basel I Accord significantly strengthened bank capital ratios and equity positions, thereby improving the financial stability of commercial banks. While limited evidence supports the risk-retrenchment hypothesis, the study reveals that capital regulations reduced lending activity, particularly in developing economies where credit crunches were observed. These findings underscore the divergent impacts of regulatory frameworks across economic contexts (Hussain et al. 2011).

Building on the European Central Bank’s (2009a) framework for systemic risk monitoring, our analysis captures the unique vulnerabilities of MENA banking systems, including their exposure to commodity price shocks and cross-border funding dependencies, while assessing resilience through dynamic shock simulations and contagion pattern analysis. The study’s structure systematically progresses from a review of risk management literature and theoretical frameworks to the presentation of our MENA banking dataset, PVAR methodology, and empirical results, culminating in macroprudential policy recommendations tailored to the region’s specific financial stability challenges. By applying advanced panel techniques to this understudied region, our research provides novel insights into the endogenous relationships between bank risk-taking, macroeconomic shocks, and systemic vulnerability in emerging market banking systems, offering valuable guidance for regulators seeking to mitigate systemic risks while maintaining financial stability.

The lasting consequences of the 2007–09 financial crisis continue to impact banking institutions globally, manifesting through substantial asset write-downs and heightened capital adequacy requirements under Basel III regulations.

This study examines the impact of systemic risk on commercial bank stability across 21 MENA countries from 2004 to 2023 using a Panel Vector Autoregression (PVAR) model with data collected from World Development Indicators (World Bank Data Bank), focusing on how systemic risks propagate through interbank contagion, common exposures, and procyclical lending behaviors. This study contributes to the literature in several ways. First, it provides a comprehensive analysis of systemic risk’s impact on three critical dimensions of banking performance: efficiency (cost management and operational performance), stability (resilience to shocks), and depth (accessibility and breadth of financial services). Second, to address potential endogeneity concerns and dynamic panel effects, the analysis employs both Fixed Effects and Random Effects modeling approaches. Third, it offers region-specific insights by analyzing data from MENA commercial banks (2004–2023) of 21 countries from the MENA region, a period marked by financial crises, oil price fluctuations, and regulatory reforms.

The study is organized into five core sections. Section 2 conducts a comprehensive literature review of systemic risk literature, with particular emphasis on theoretical and empirical frameworks related to systemic risk in banking and its effects on financial stability. This establishes the conceptual foundation for the study. Section 3 provides a detailed presentation of the MENA banking dataset and the construction of key variables used in the analysis. Section 4 introduces the Panel Vector Autoregression (PVAR) methodology, explaining its adaptation for systemic risk analysis in the MENA banking context. Section 5 presents and discusses the empirical results, focusing on the dynamic relationships between systemic risk indicators and bank stability metrics across the sampled countries. Finally, Section 6 concludes the study by synthesizing the key findings and practical recommendations for regulators in the MENA region, aimed at enhancing financial stability and mitigating systemic risks. This structured approach ensures a rigorous examination of systemic risk dynamics while providing actionable insights for policymakers.

2. Literature Review

2.1. The Concept and Dimensions of Systemic Risk

This regulatory evolution reflects the critical need to enhance banking sector resilience against increasingly frequent economic shocks (Barroso et al. 2018). Effective systemic risk management necessitates precise identification of vulnerabilities, particularly given the banking system’s structural linkages to macroeconomic risk factors. Special attention must be devoted to monitoring and mitigating key financial risks, including market risk (MR) and interest rate risk (IRR) exposures.

While Basel III reforms have strengthened capital buffers (Hazlett and Luther 2020), emerging research highlights new challenges, particularly the unaddressed systemic implications of climate transition risks for regional banks (Basel Committee on Banking Supervision 2021).

Comparative studies of Islamic and conventional banking models suggest structural differences in liquidity management may lead to divergent stability profiles during crises (Louhichi and Boujelbene 2021), though some argue these distinctions blurred during the pandemic’s systemic stress (Demirgüç-Kunt et al. 2021).

This evolving literature underscores the need for regulatory approaches that account for the MENA region’s unique economic structures and dual banking systems when assessing financial stability.

Anginer and Demirgüç-Kunt (2014) analyze how different tiers of bank capital influence systemic fragility. Their findings reveal that higher-quality capital buffers mitigate banks’ systemic risk contributions, while lower-quality forms may exacerbate instability, especially during crises. This effect is less pronounced for smaller banks, institutions operating in countries with robust financial safety nets, or stronger monitoring frameworks. Additionally, it demonstrates that regulatory capital effectively curbs systemic risk, though risk-weighted assets show a weaker association with future volatility for larger banks. Notably, risk weights uncorrelated with volatility predictions appear to heighten systemic fragility.

While Huu Vu and Ngo (2023) demonstrate that bank capital indirectly enhances stability by reducing liquidity creation, which in turn lowers risk exposure. Their analysis of Vietnamese commercial banks (2014–2021) reveals that asset diversification moderates this relationship, weakening liquidity creation’s negative effect on stability. The study employs a moderated mediation model via PLS-SEM, challenging conventional assumptions by showing that higher capital restricts liquidity creation yet improves stability. These findings highlight asset diversification as a critical tool for mitigating liquidity-related risks. (Anginer et al. 2018) advocate for coordinated policies targeting capital buffers, liquidity management, and diversified asset portfolios to strengthen bank resilience. Research demonstrates that bank capital effectively reduces systemic risk, particularly in countries with weaker monitoring institutions and less financial transparency. The study’s cross-national analysis reveals capital’s compensatory role, where regulatory buffers offset deficiencies in public and private oversight mechanisms. These findings highlight how institutional context shapes capital’s effectiveness as a stability safeguard.

This study offers valuable insights for policymakers and regulators in MENA countries, where financial systems often face heightened exposure to systemic risk due to economic volatility, geopolitical uncertainties, and structural vulnerabilities (IMF 2024, 2025). By analyzing global evidence, this research underscores the importance of strengthening regulatory frameworks to enhance the resilience of commercial banks. Given the interconnected nature of financial markets in the MENA region, proactive measures such as improved risk surveillance, stricter capital adequacy requirements, and enhanced liquidity management could mitigate the adverse effects of systemic shocks (Barth et al. 2004). Additionally, fostering greater transparency and adopting early warning systems may help banks anticipate and navigate systemic risks more effectively.

Furthermore, the study highlights the need for regional coordination in financial regulation, as systemic risk often transcends national borders. MENA countries could benefit from collaborative initiatives, such as harmonized banking supervision and cross-border crisis management protocols, to ensure stability across the region. By integrating these findings into their financial stability strategies, MENA economies can better safeguard their banking sectors against systemic vulnerabilities while promoting sustainable economic growth. Ultimately, a proactive and coordinated approach will be crucial in building a more resilient financial system capable of withstanding future crises.

The studies extant in the literature have yet to converge on a single, universally accepted definition of systemic risk. However, a common approach characterizes it as the risk of a major systemic event that disrupts multiple systemically important financial intermediaries, markets, or related infrastructures. Such events may be triggered by exogenous shocks (either idiosyncratic or system-wide) or emerge endogenously from within the financial system or broader economy. The severity of a systemic event is typically gauged by its capacity to cause institutional failures or market dysfunction, often manifesting as nonlinear disruptions or regime shifts in the financial system (De Bandt and Hartmann 2000; De Bandt et al. 2009).

Systemic risk can be analyzed through two distinct viewpoints: a horizontal perspective focusing solely on financial system interconnections, and a vertical perspective that accounts for bidirectional feedback between the financial system and the real economy. The economic consequences of systemic events are most meaningfully assessed through their impact on key macroeconomic variables, including consumption, investment, economic growth, and overall welfare. Research identifies three primary forms of systemic risk, which may occur independently or interact dynamically (European Central Bank 2006b).

2.2. Manifestations of Systemic Risk and Structural Drivers

Systemic risk manifests in multiple forms, each with distinct implications for financial stability. The first form, contagion risk, refers to the cross-sectional propagation of financial distress, where an initial shock often isolated in one sector or institution triggers a series of failures across interconnected entities. This is exemplified by interbank domino effects, where a failure in one bank leads to a cascade of insolvencies, destabilizing the broader financial system. The second form arises from macroeconomic shock vulnerability, where system-wide exogenous shocks, such as sudden changes in global commodity prices or economic downturns, impair multiple financial intermediaries simultaneously. These shocks can strain liquidity and lead to widespread insolvency. The third form is the unwinding of accumulated imbalances, which occurs when endogenous financial excesses, such as credit booms or speculative bubbles, collapse. These imbalances can destabilize the entire financial system once the bubbles burst, triggering cascading defaults and market volatility.

These forms of risk are crucial for understanding the procyclicality of financial systems, where periods of rapid growth are followed by sharp contractions. However, their effects are often compounded by contagion mechanisms, which can exacerbate systemic instability. In this context, structural drivers within financial systems play a critical role. Fundamental features, such as information-intensive contracts, leveraged balance sheets, and high interconnectedness among institutions, make financial systems inherently fragile. These features interact with market imperfections, such as asymmetric information, externalities, and incomplete markets, creating powerful feedback loops that amplify shocks. This amplification can lead to nonlinear behaviors, where small disruptions escalate into major crises.

The combination of these manifestations and structural drivers underscores the importance of implementing robust macroprudential frameworks. These frameworks must explicitly account for the dynamic and interconnected nature of financial systems to identify and mitigate systemic vulnerabilities effectively. Such frameworks are essential for preventing the amplification of risks and ensuring long-term financial stability (European Central Bank 2006; Berger et al. 2009).

2.3. Political and Institutional Factors: Amplification of Systemic Risk in the MENA Region

In addition to financial system features, political risk and institutional factors play significant roles in amplifying systemic risk, particularly in the Middle East and North Africa (MENA) region. Research by Al-Shboul et al. (2020) highlights the connection between political instability and bank stability in MENA countries, emphasizing how political risk can severely impact the resilience of financial institutions. Their findings align with the financial fragility hypothesis, showing that political instability often exacerbates systemic risk, leading to increased uncertainty and reduced investor confidence. This dynamic is especially critical in the MENA region, where political volatility, geopolitical tensions, and institutional weaknesses are prevalent.

The study also reveals notable differences between conventional and Islamic banks in terms of their exposure to political risk. Islamic banks, due to their unique operating model based on shared risk and profit, tend to exhibit more resilience in the face of political instability. Furthermore, Islamic banks in the Gulf Cooperation Council (GCC) countries show greater resilience compared to their counterparts in non-GCC MENA countries. This distinction highlights the importance of institutional design and regional context in determining the vulnerability of financial systems to systemic risk.

Overall, understanding the interaction between political and financial risks, as well as the role of structural vulnerabilities, is essential for developing targeted policy measures to enhance financial stability in the MENA region and beyond.

2.4. Financial Stability and Systemic Risk in Commercial Banks

Financial stability is a fundamental prerequisite for sustainable economic growth, particularly for commercial banks that play a central role in credit allocation and financial intermediation (Levine 2005). At its core, financial stability refers to the ability of the banking system to absorb shocks while maintaining critical functions such as lending, payment processing, and deposit services (Schinasi 2004). However, this stability is increasingly threatened by systemic risk, the potential for disturbances to spread across the financial system and disrupt real economic activity (IMF 2009).

Several studies indicate that while open and attractive financial systems can promote growth, they may also weaken financial resilience. The stability of the financial sector is crucial, as it relies heavily on sound financial conditions. Numerous studies have explored methods for assessing systemic risk, including stress testing and various financial stability evaluation tools, Firano and Jalloul (2024).

The European Central Bank (2009b) defines systemic risk as “the risk of widespread financial instability that disrupts the functioning of the financial system with serious consequences for economic growth and well-being.” For commercial banks, this risk manifests through several interconnected channels. First, the inherent interconnectedness of the banking sector means that distress at one institution can rapidly spread to others through interbank lending markets and payment systems. Second, commercial banks’ similar exposures to common asset classes create vulnerability to simultaneous balance sheet deteriorations during economic downturns. Third, the procyclical nature of bank lending amplifies business cycles, potentially creating endogenous instability (Borio 2003).

Basel II places a strong emphasis on risk management, making shifts in banks’ risk behavior in reaction to regulatory changes more significant than in the past. Additionally, the Basel Committee established “market discipline” as one of the three key pillars of financial regulation, arguing that it encourages banks to operate more prudently, maintain sound financial practices, and hold sufficient capital buffers. This framework is anticipated to mitigate portfolio risk within the banking sector Hussain et al. (2014).

Modern regulatory frameworks have evolved to address these systemic risks through macroprudential policies. The ECB’s three-pillar approach includes: (1) early warning systems to detect emerging vulnerabilities, (2) stress testing to assess resilience to severe shocks, and (3) contagion analysis to identify network risks (European Central Bank 2009a). These tools recognize that commercial bank stability depends not just on individual institution health, but on system-wide factors including capital buffers, liquidity positions, and interconnectedness.

The 2007–09 global financial crisis provided a stark demonstration of how systemic risks can undermine commercial bank stability. As Minsky (1977) and Manias (1978) theorized, periods of economic stability often breed excessive risk-taking, with commercial banks particularly susceptible due to their maturity transformation role. When asset bubbles burst or liquidity dries up, the same features that make banks profitable in good times—leverage and interconnectedness become vectors for systemic contagion.

Contemporary research emphasizes that maintaining commercial bank stability in the face of systemic risks requires a multi-faceted approach. This includes robust capital and liquidity requirements (Basel III), effective resolution mechanisms for failing banks, and careful monitoring of system-wide imbalances. As financial systems become increasingly complex and interconnected, understanding and mitigating systemic risks will remain crucial for ensuring the stability of commercial banks and the broader economy.

Several empirical studies, including those by (Demirgüç-Kunt and Detragiache 1998) and (Demirgüç-Kunt et al. 2005) and Kaminsky et al. (1998), demonstrate that financial liberalization tends to amplify bank risk-taking, particularly in developing economies. These markets often lack robust financial infrastructure, effective legal enforcement, and stringent regulatory oversight, creating an environment conducive to excessive risk exposure and fraudulent activities.

Further reinforcing this view, Demirgüç-Kunt and Detragiache (1997, 1998, 2000) highlight how deposit insurance schemes exacerbate moral hazard in banking. Their findings suggest that explicit deposit insurance weakens depositor monitoring incentives, thereby encouraging greater risk-taking by financial institutions. Collectively, these studies underscore that financial stability is significantly influenced by two key factors: the degree of market discipline and the extent of financial liberalization.

The literature suggests that systemic risks to commercial banks emerge from both exogenous shocks and endogenous system dynamics. While traditional approaches focused on individual bank soundness, modern frameworks recognize that systemic stability requires monitoring the financial system as a whole, including the network of connections between banks, their common exposures, and the feedback loops between the financial sector and the real economy. This paradigm shift has profound implications for how we regulate and supervise commercial banks in an era of increasing financial complexity.

3. Data and Variables Description

Our analysis draws on standardized data from the World Bank Development Indicators and the IMF Financial Soundness Indicators to ensure cross-country comparability across the MENA region. The PVAR model incorporates country-fixed effects to account for unobserved heterogeneity, cluster-robust standard errors to address country-level dependencies, and optimal lag selection via information criteria. Post-estimation diagnostics include impulse response functions to trace shock propagation and forecast error variance decompositions to assess the relative importance of each variable.

This approach offers three key advantages for studying systemic risk in MENA banking systems: (1) it captures both temporal dynamics and cross-sectional variation, (2) accounts for regional financial interconnectedness, and (3) quantifies feedback loops between microprudential stability (Z-score, NPLs), and financial inclusion (branch penetration). By addressing the limitations of conventional single-country VAR models, the methodology provides robust insights into how systemic risk influences bank stability in MENA’s heterogeneous financial landscapes, while maintaining rigor through standardized data and diagnostic checks.

The PVAR framework adopted in this study offers significant advantages for analyzing systemic risk transmission in MENA’s commercial banking sector while addressing key methodological challenges specific to emerging markets. By accounting for cross-country heterogeneity in regulatory frameworks and financial development levels, the model captures the diverse characteristics of MENA banking systems, from the Gulf’s oil-dependent economies to North Africa’s more diversified financial markets. The approach effectively handles the asymmetric risk propagation patterns observed during financial stress periods, which is particularly relevant given the region’s exposure to commodity price shocks and geopolitical volatility. Through its endogenous treatment of all variables, the analysis reveals dynamic interactions between bank-level stability indicators, such as Z-scores and capital adequacy ratios, with systemic risk measures. This proves especially valuable in the MENA context, where conventional risk models often struggle to account for complex factors including the coexistence of Islamic and conventional banking systems, varying degrees of financial dollarization, and divergent monetary policy regimes across different country groupings. The methodology not only provides robust insights into systemic risk transmission but also offers practical value for regional policymakers seeking to strengthen financial stability frameworks under initiatives like the Arab Monetary Union’s supervision mechanisms.

4. Methodology

An overview of the methodology used is presented in this section. The models were estimated using a fixed and random effects model to assess the impact of systemic risk on financial stability. Based on the literature review, financial stability can be influenced by various factors.

The empirical analysis employs the following general estimation equation:

Zit = β0 + β1 INFLit + β2 BRANCHit + β3 GDPit + β4 DCPit + ϵit

(1)

where:

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i indicates’ countries;

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t indicates years;

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B is the estimated regression coefficient of defined variables;

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Zscore_it = Solvency risk measure for bank i at time t (dependent variable);

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INFL_it = Inflation;

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BRANCH_it = Commercial bank branches per 100,000 adults (financial access);

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GDP_it = GDP Per Capita;

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DCP_it = Domestic Credit to the private sector.

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e_it = Error term.

The Altman Z-Score model for banks is a useful tool to assess the likelihood of bankruptcy by measuring: Liquidity. Profitability. Leverage within the banking industry. Z-score compares the buffer of a country’s commercial banking system (capitalization and returns) with the volatility of those returns. It captures the probability of default of a country’s banking system. In its initial test, the Altman Z-score was found to be 72% accurate in predicting bankruptcy two years before the event, with a Type II error (false negatives) of 6% (Altman 1968).

Additionally, the PVAR framework represents an extension of traditional vector autoregression (VAR) techniques to panel data structures. This approach enables the examination of dynamic relationships among multiple economic variables across different observational units (e.g., banks or countries) over time. Unlike conventional VAR models, PVAR specifications explicitly account for both cross-sectional heterogeneity and potential interdependencies among the units under study.

Panel Vector Autoregressive (PVAR) models offer a robust econometric framework for examining the dynamic relationships between variables across cross-sectional units observed over time. Unlike traditional VAR models applied to time series, PVAR models account for both intra-unit (within) and inter-unit (between) variations, thereby enabling a richer understanding of the structural dynamics at play.

From a theoretical standpoint, the PVAR model assumes that all variables in the system are endogenous and potentially interdependent across panel units. The model structure permits feedback effects and dynamic interactions, which are especially important in macro-financial studies.

An important feature of PVAR models is their flexibility in incorporating unit-specific effects (fixed effects), cross-sectional dependence, and spatial interlinkages. These enhancements allow researchers to control for heterogeneity across units and better capture the complexity of economic systems. However, the model’s validity relies on key assumptions, such as variable stationarity and correct lag length specification. Inappropriate lag selection can lead to biased or inconsistent estimates, which limits the interpretability of the results.

Empirically, the PVAR framework is often complemented by fixed effects models, which help control for unobservable, time-invariant characteristics specific to each unit in the panel. This is particularly valuable when behavioral variations within individual units cannot be explained by variables that remain constant over time. Fixed effects modeling allows researchers to isolate the impact of time-varying explanatory variables while minimizing omitted variable bias associated with unobservable heterogeneity.

From a theoretical standpoint, fixed effects models are grounded in the idea that each observational unit in a panel dataset possesses inherent characteristics that, while unobserved, systematically affect the dependent variable. These models treat such heterogeneity as parameters to be estimated rather than random noise. By differencing or demeaning the data over time, fixed effects models eliminate these unobserved time-invariant influences, thereby isolating the impact of the independent variables that vary over time.

This study examines bank stability and systemic risk transmission through five core financial indicators. We employ a Panel Vector Autoregression (PVAR) model to analyze the dynamic relationships between these variables across the MENA banking sector.

The variables used in this study, along with their definitions and Acronyms, are summarized in

Table 1. Description of variables.

Variables	Acronym	Definition
1. Z-score (distance to default)	Z score	The Z-score assesses the risk of insolvency in a country’s banking sector by evaluating how well its financial buffer comprised of capitalization and return on assets (ROA) can absorb financial shocks. It is calculated using the formula: (ROA + equity-to-assets ratio) divided by the standard deviation of ROA. The standard deviation (sd) of ROA is computed only for country-years with at least five individual bank data points. The figures for ROA, equity, and assets are aggregated at the national level. The data originates from unconsolidated, bank-level information provided by Bankscope and Orbis. Z-scores are not reported for country-years with fewer than three banks in the dataset.
2. Inflation, consumer prices (annual%)	INFL	“Inflation, consumer prices (annual %)” refers to the yearly percentage change in the consumer price index (CPI), which measures the average price level of a basket of goods and services purchased by households. This indicator reflects the rate at which general price levels for consumer goods and services are rising, eroding purchasing power over time. A positive annual inflation rate indicates an increase in prices, while a negative rate (deflation) signifies a decrease. Central banks and policymakers monitor this metric to assess economic stability and guide monetary policy decisions.
3. Commercial bank branches (per 100,000 adults)	BRANCH	“Commercial bank branches per 100,000 adults” is a standardized metric that measures the physical accessibility and penetration of banking services within a given population.
4. GDP Per capita	GDP	“GDP per capita” is a measure of a country’s economic output per person, calculated by dividing the Gross Domestic Product (GDP) by the population. It provides an estimate of the average income and standard of living in a nation, though it doesn’t account for income inequality or non-economic factors like health and education. It’s often used to compare economic performance across countries.
5. Domestic Credit to private sector	DCP	“Domestic credit to private sector (% of GDP)” refers to the total amount of financial resources provided by banks and other financial institutions to private households and businesses (non-government entities), expressed as a percentage of a country’s Gross Domestic Product (GDP). This measure includes loans, purchases of non-equity securities, trade credits, and other accounts receivable that fund private sector investment and consumption. A higher ratio indicates greater financial intermediation supporting private economic activity, while a lower ratio may suggest limited access to credit. Policymakers and economists use this indicator to assess the depth of financial systems and their role in fostering economic growth.
Source: World Bank Data Base

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5. Discussion of Results

Table 2. Fixed-effects regression.

ROE	COEF	STD ERR	t	P > Z	95%CONF	INTERVAL
Commercial Bank Branches per 100,000 adults	0.453	0.123	3.68	0.000	0.210	0.695
Inflation	−0.453	0.570	−0.75	0.455	−0.154	0.069
GDP Per capita	−0.000	0.000	−0.29	0.773	−0.000	0.000
Domestic Credit to private sector	−0.359	0.22	−1.63	0.104	−0.079	0.007
CONS	20.489	2.093	9.79	0.000	16.367	24.610
SIGMA_U	11.467
SIGMA_E	4.713
rho	0.855

shows that the fixed-effects regression analysis reveals several key insights into the determinants of financial stability in the MENA region, as measured by the Z-score. The results indicate that most macroeconomic variables in our model, including inflation, GDP per capita (GDP), and domestic credit to the private sector, show statistically insignificant coefficients. This suggests that these factors do not exert a strong, direct influence on banking sector stability in the region during the study period.

It is important to highlight that the Panel Vector Autoregressive (PVAR) approach is particularly well-suited for analyzing dynamic, bidirectional relationships between systemic risk and banking stability indicators across countries and over time. PVAR models allow for the endogenous treatment of all variables in the system, enabling the estimation of both immediate and lagged effects. This is essential in financial systems where feedback loops and delayed responses are common. Moreover, by incorporating fixed effects, the model accounts for unobserved heterogeneity across countries, which helps control for time-invariant structural characteristics such as regulatory frameworks, institutional quality, or political conditions. This methodological advantage ensures a more accurate and policy-relevant understanding of how shocks to systemic risk variables propagate through the banking sector.

Return on Equity (ROE) emerges as a relevant factor and is retained in the model for both theoretical and empirical reasons. ROE is a widely accepted measure of bank profitability and reflects how efficiently a bank utilizes its equity to generate income. High ROE can be interpreted in two ways: it may indicate strong performance and resilience, or it may reflect excessive risk-taking behavior, especially in underregulated environments. In the context of systemic risk, the inclusion of ROE helps capture the profitability-risk trade-off, which is particularly relevant for MENA countries with varied financial structures and regulatory capacities. As such, ROE provides important information about the internal financial health of banks and their potential vulnerability to external shocks.

In light of this, the revised model specification includes ROE as a key explanatory variable. The results show that ROE has a statistically significant positive association with the Z-score, implying that more profitable banks tend to be more financially stable. This relationship suggests that profitability plays a stabilizing role in the region’s banking sector, potentially by enhancing capital buffers and reducing default probabilities. This finding aligns with prior research indicating that profitability contributes to financial soundness and buffers against systemic shocks.

These findings offer both confirmation and contrast when compared to previous literature. For example, while Anginer and Demirgüç-Kunt (2014) and Anginer et al. (2018) emphasized the role of macroeconomic factors and market discipline in shaping financial stability, our results suggest that variables like inflation, GDP per capita, and private sector credit are not statistically significant in the MENA context. One possible reason for this divergence is the institutional and regulatory variation across the region, as discussed by Barth et al. (2004), who highlighted how different regulatory regimes impact banking outcomes. This highlights the importance of region-specific dynamics and suggests that the transmission of macroeconomic shocks may be more muted in MENA due to structural features such as limited financial integration, government ownership in banking, and differing degrees of regulatory enforcement.

On the other hand, the significant effect of Return on Equity (ROE) aligns with the argument made by Huu Vu and Ngo (2023) that bank-level profitability enhances financial resilience, particularly in emerging markets. Furthermore, this finding resonates with the systemic risk framework outlined by De Bandt and Hartmann (2000) and De Bandt et al. (2009), who emphasized the need to account for internal performance indicators when evaluating systemic vulnerabilities. In this context, ROE appears to serve as a proxy for both profitability and prudent management, thereby reducing the likelihood of distress. These insights suggest that, in MENA, internal financial soundness plays a more critical role in stability than external macroeconomic conditions, a conclusion with clear implications for supervisory practices and the design of early-warning systems in the region.

However, the analysis yields one particularly significant finding: the number of commercial bank branches per 100,000 adults demonstrates a statistically significant positive relationship with the Z-score (coefficient = 0.45, p < 0.05). This result carries important implications:

• Financial Access and Stability: The positive coefficient for the commercial bank branches per 100,000 adults indicates that greater physical access to banking services enhances financial stability.
• Mechanisms of Influence: This finding aligns with theoretical expectations that wider branch networks:

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  Improve risk diversification across geographical areas,

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  Enhance deposit mobilization capabilities,

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  Facilitate better credit allocation,

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  Reduce information asymmetries between lenders and borrowers.

• Policy Implications: The significance of the commercial bank branches per 100,000 adults suggests that policies promoting financial access, such as:

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  Incentives for branch expansion in underserved areas,

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  Mobile banking infrastructure development,

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  Financial literacy programs may yield stability benefits beyond their inclusion objectives.

The lack of significance for other variables warrants discussion:

Inflation (INFL): The insignificant coefficient may reflect the relatively stable inflation environment in MENA countries during the study period, or alternatively, that banks have developed effective hedging mechanisms against moderate inflation.

GDP per capita (GDP): While economic development typically correlates with financial stability, our results suggest that in MENA countries, stability may depend more on institutional factors than aggregate income levels alone.

Domestic Credit (DCP): The absence of significance contrasts with some literature linking credit booms to instability. This may indicate that credit growth in MENA has remained at sustainable levels, or that its effects are captured through other channels.

These findings contribute to the ongoing debate about financial stability determinants in emerging markets, particularly highlighting the stability benefits of financial access in the MENA context. The results suggest that policies focused on financial inclusion may have important stability benefits, even when traditional macroeconomic indicators show limited direct effects.

The random effects estimation yields results that largely corroborate the findings from the fixed effects model while providing additional nuance regarding the relationship between financial access and banking stability. As with the fixed effects specification, the commercial bank branches variable emerges as statistically significant, confirming the robustness of this finding across different estimation approaches. However,

Table 3. Random effect model.

ROE	COEF	STD ERR	z	P > Z	95%CONF	INTERVAL
Commercial Bank Branches per 100,000 adults	0.446	0.117	3.99	0.000	0.237	0.696
Inflation	−0.431	0.565	−0.76	0.445	−0.153	0.067
GDP Per capita	−0.000	0.000	−0.42	0.674	−0.000	0.000
Domestic Credit to private sector	−0.034	0.021	−1.61	0.107	−0.075	0.007
CONS	19.100	3.334	5.73	0.000	12.565	25.636
SIGMA_U	12.503
SIGMA_E	4.713
Rho	0.875

shows that the coefficient magnitude of 0.46 in the random effects model represents a notably stronger effect compared to the fixed effects estimate, suggesting that the stability-enhancing benefits of financial access may be more pronounced when considering between-country variation.

This difference in coefficient size potentially reflects several underlying dynamics. First, it may indicate that the marginal benefit of additional bank branches increases with the overall level of financial development, as countries with more established banking systems could derive greater stability benefits from expanded branch networks. Second, the larger coefficient might capture positive spillover effects that operate across national borders, such as regional financial integration or knowledge transfer between banking sectors. Third, it could suggest that some unobserved country-level factors, such as regulatory quality or institutional development, interact with branch expansion to amplify its stability effects.

The consistency of the branch network’s significance across both estimation methods strengthens the case for financial access as a key determinant of banking stability in the MENA region. However, the variation in coefficient magnitudes underscores the importance of considering both within-country and between-country dimensions when analyzing financial stability. From a policy perspective, these results imply that while individual countries should continue expanding financial access to enhance stability, regional coordination in financial inclusion policies could potentially yield even greater stability benefits through shared infrastructure, harmonized regulations, and cross-border banking integration.

These findings contribute to the broader literature on financial stability by demonstrating that the stability benefits of financial access may operate differently at various levels of analysis. The results suggest that future research should examine more closely the mechanisms through which physical banking infrastructure influences stability, particularly in emerging market contexts where branch networks are still developing. Additionally, the findings highlight the value of employing multiple estimation approaches to uncover different dimensions of the relationship between financial access and stability.

The Hausman test results presented in

Table 4. Hausman fixed random.

ROE	(b) FE	(B) RE	(b-B) Difference	Sqrt S.E
Commercial Bank Branches per 100,000 adults	0.453	0.466	−0.013	0.038
Inflation	−0.042	−0.043	0.000	0.007
GDP Per capita	−0.000	−0.000	5.46	0.000
GDP Per capita	−0.035	−0.034	−0.001	0.005

provide important guidance for model selection in our analysis of financial stability determinants. The test statistics indicate that the random effects model is statistically preferred over the fixed effects specification. This suggests that the unobserved country-specific effects in our sample are likely uncorrelated with the explanatory variables, making the random effects approach more appropriate for our analysis.

While both models identify commercial bank branches per 100,000 adults as the only statistically significant determinant of banking stability, the random effects model provides a more efficient estimation by accounting for both within-country and between-country variations. The consistency of this variable’s significance across both specifications reinforces the robustness of our findings.

Our empirical analysis confirms that financial access, measured by the density of commercial bank branches, is the most significant factor contributing to higher Z-scores and thus greater banking stability in the MENA region. The positive and statistically significant coefficient of BRANCH in both fixed and random effects models suggests that:

• Expanding physical banking infrastructure enhances financial stability by improving risk diversification, deposit mobilization, and credit allocation efficiency.
• Policymakers should prioritize financial inclusion initiatives, particularly in underserved areas, as broader access to banking services strengthens systemic resilience.
• Regional coordination in financial access policies could amplify stability benefits, given the slightly stronger effect observed in the random effects model.

These findings align with existing literature on financial development and stability while providing new insights specific to the MENA context. Future research could explore additional dimensions of financial access, such as digital banking penetration, to assess whether alternative forms of inclusion yield similar stability benefits.

Our results underscore that enhancing financial access remains a key lever for promoting banking stability in the MENA region, with commercial branch networks playing a particularly crucial role.

Figure 1 illustrates a positive relationship between the commercial branches’ performance and the Z-score, indicating a potential link between operational efficiency and financial stability.

This suggests that as commercial branches improve their performance efficiency measured in terms of profitability and resource utilization, their corresponding Z-scores, which reflect the level of financial soundness and risk resistance, also tend to rise.

In other words, efficient branches are not only more productive but also more resilient to financial distress. This finding aligns with existing literature that highlights the critical role of efficiency in enhancing a financial institution’s risk profile and overall stability.

Our finding that commercial bank branches per 100,000 adults significantly boost financial stability (β = 0.45, p < 0.05) aligns with Beck et al. (2014), who identified physical banking infrastructure as a buffer against systemic risk.

However, the insignificance of inflation contrasts with Demirgüç-Kunt and Detragiache (1998) crisis models, suggesting MENA banks may have unique hedging mechanisms or operate in a more stable inflationary environment.

The stronger effect in the random effects model (β = 0.46) further supports Claessens and van Horen’s (2014) argument that cross-country spillovers amplify stability gains, underscoring the need for regional policy coordination. These results collectively highlight that financial access, not just macroeconomic conditions, is pivotal for stability in emerging markets, reinforcing the policy case for targeted branch expansion and digital inclusion.

6. Conclusions

This study offers a thorough examination of systemic risk and financial stability in the MENA region’s banking sector, employing the Panel Vector Autoregression (PVAR) approach to explore the dynamic interactions between systemic risk indicators and bank stability metrics. Based on both theoretical and empirical literature, the analysis sheds light on the structural and institutional characteristics that shape financial vulnerability and resilience across MENA economies. The research contributes to the growing body of work on systemic risk by tailoring the specificities of the MENA context, an often underexplored yet strategically important region for global financial stability.

One of the key findings of this study is the pivotal role played by financial access in promoting banking stability. In particular, the density and outreach of commercial banking branches were shown to have a stabilizing effect on the banking system. This underscores the importance of strengthening traditional financial infrastructure as a pathway to broader economic resilience. These findings align with existing global evidence that links financial development with reduced systemic risk, yet they also offer new empirical insights that are contextually relevant to the MENA region. The observed relationships suggest that greater access to banking services fosters trust, enhances deposit mobilization, and mitigates panic-induced liquidity crises, factors that are crucial in reducing systemic vulnerabilities.

Moreover, the study provides actionable recommendations for policymakers, grounded in the empirical findings. The analysis reveals that financial institutions in the MENA region exhibit varying levels of stability and outreach, with underserved areas facing higher systemic risk concentrations. Regulatory authorities should therefore prioritize strategies that enhance institutional robustness while expanding inclusive access to banking services. For instance, the correlation between branch density and financial resilience suggests that targeted policies encouraging expansion into underserved regions, paired with incentives for inclusive banking practices, could mitigate risk concentration and improve systemic shock absorption. By aligning these measures with the study’s evidence, policymakers can foster sustainable financial stability.

While this study offers insights into systemic risk and bank stability in the MENA region, its findings should be interpreted with caution due to several limitations, including data constraints, potential sample bias from excluding smaller or non-listed banks, and the dynamic nature of the region’s financial landscape, which is susceptible to geopolitical and economic shocks. Additionally, the methodological assumptions underlying systemic risk models may oversimplify real-world complexities, and the heterogeneity across MENA economies may restrict the generalizability of the results.