The Impact of Risk Management on Countries in the MENA Region

Abstract

This study explores how adjustments in risk management can influence the future financial performance of 20 countries in the MENA (Middle East and North Africa) region. While the existing literature has explored risk factors in emerging economies, this research provides novel empirical evidence on how risk management practices influence long-term financial stability and growth, a dimension underexplored in the MENA context. Using a Panel Vector Autoregression (PVAR) model, we analyze data from 2005 to 2021 to quantify the dynamic relationship between risk mitigation strategies and key financial outcomes, accounting for regional volatility and cross-country heterogeneity. This methodology allows for the examination of the impact of risk management on future financial outcomes, considering both current uncertainties and strategic approaches to mitigating risks. The results reveal that robust forward-looking risk management practices significantly impact the future financial performance and resilience of the countries in the MENA region. Our findings highlight that a well-designed risk management strategy is crucial for averting financial crises and supporting long-term economic growth and sustainability of nations. This study contributes to the understanding of how strategic risk management can drive future economic and financial stability in the MENA region, providing unique insights into the role of forward-thinking risk practices in shaping national success.

1. Introduction

Effective risk management enhances a country’s ability to withstand economic disruptions, ensuring financial stability and growth. For instance, managing market risks protects against volatility in commodity prices or exchange rates, while operational risk management prevents losses from supply chain disruptions. Integrating risk management into corporate governance fosters transparency and accountability, boosting investor confidence and improving access to capital. This, in turn, supports investment in growth opportunities, enhancing long-term earnings and economic resilience. Risk management is a key 20th-century innovation (Steinherr, 1998). It balances threats and opportunities, enabling businesses to navigate uncertainty and maintain quality services.

The Middle East and North Africa (MENA) region includes countries sharing cultural and historical ties. These countries face similar socioeconomic and institutional challenges that hinder regional stability and growth.

Our central objective is to demonstrate that effective risk governance frameworks play a pivotal role in mitigating systemic financial vulnerabilities while fostering sustainable economic development.

In the MENA region, lower risk correlates with higher bank returns, but Islamic banks face greater risks due to stricter regulations (Albaity et al., 2023). Proactive risk management ensures long-term success, especially for banks and corporations facing market volatility, credit risks, and regulatory changes. It safeguards assets and builds operational resilience.

Additionally, risk management reduces incidents and losses, particularly in finance and insurance (Sousa & Jordao, 2010). Balancing risk aversion and growth opportunities is crucial for sustainable success. In today’s volatile economy, robust risk management is essential for resilience and competitive advantage, especially in industries with tight margins.

While the existing literature extensively explores risk management’s impact on financial sector performance, this study makes several unique contributions to the field. First, it focuses on the MENA region, an area often underexplored in research. This study provides insights into how risk management practices influence future performance in a region that faces political instability, economic volatility, and shifting regulatory landscapes.

Effective risk management in crisis response requires the incorporation of policies that address the challenges posed by elevated sovereign debt levels, thereby safeguarding governments’ ability to provide sustained support throughout the recovery process (World Bank, 2022).

Unlike corporate risk management, this demand coordinates policy responses across fiscal, monetary, and regulatory domains particularly crucial for developing economies exposed to volatile capital flows and external shocks (IMF, 2023a).

The Middle East and North Africa (MENA) region presents a critical yet understudied context for examining these dynamics. In the 2000s, oil exports made up over 60 percent of the total goods and services exports in the GCC, with this proportion either increasing or remaining relatively stable since the 1980s (IMF, 2023b). This study addresses these gaps by answering the following questions: How do risk management practices influence long-term financial stability and economic growth in the MENA region? To what extent can forward-looking risk management strategies enhance financial resilience in the face of regional volatility and external shocks?

The analysis employs the PVAR model on a sample of 20 MENA countries over the period from 2005 to 2021, using relevant data from the World Bank Group. Using the PVAR model, it examines dynamic relationships between risk management and financial performance, offering deeper insights than traditional models.

The study is organized as follows. In Section 2, we present a thorough literature review on the concept of risk management, highlighting key theoretical frameworks. Section 3 outlines the data and describes the variables used in this study. Section 4 presents the methodology, while Section 5 discusses the results. Finally, in Section 6, the study concludes with final remarks and suggestions for policy implementation.

2. Literature Review

2.1. Macroeconomic Risk Management in Financial Crises

The financial crisis of 2008–09 and the COVID-19 pandemic underscored the importance of liquidity and credit risk management. In the MENA region, credit risk management shows a non-linear, convex relationship with market performance, while liquidity risk management alone has limited impact but becomes significant when combined with credit risk management (Harb et al., 2023).

2.2. MENA-Specific Financial Risk Challenges

Roberto (2009) emphasizes proactive risk strategies to prevent organizational decline. Khan et al. (2015) and Hanafi (2009) stress systematic and adaptive risk management. Boermans and Galema (2009) highlight the need for financial institutions to integrate climate-related risks into decision-making.

Despite global FDI growth, MENA faces declines due to political and economic challenges, underscoring the importance of country risk analysis for attracting investment (Bouyahiaoui & Hammache, 2017).

Jensen (2008) links risk management to corporate governance, emphasizing alignment with shareholder interests through agency theory. Freeman (1984) advocates balancing stakeholder interests for strategic decision-making and long-term success. Donaldson and Preston (1995) outline stakeholder theory’s three perspectives: descriptive, normative, and instrumental, emphasizing ethical obligations and performance benefits.

2.3. Governance and Strategic Risk Integration

Krolikowski (2016) finds that effective risk management reduces risky decision-making. Gordon et al. (2009) show that robust risk systems enhance firm performance and stability. Kose et al. (2009) reveal that strong investor protection encourages managerial risk-taking, while weak protection can lead to excessive risk for personal gain.

Altman and Saunders (1998) stress the importance of credit risk management, especially post-crises. Diamond and Rajan (2005) and Brunnermeier (2009) highlight liquidity risk as critical for bank stability. Basel II (2004) identifies operational risk as a distinct category, crucial for financial integrity (Chernobai et al., 2007). Jorion (2007) emphasizes VaR for market risk management, while Froot et al. (1993) focus on interest rate risk in lending institutions.

Ingram and Headey (2004) outline a seven-stage risk management process: setting context, risk identification, analysis, assessment, treatment, communication, and integration with business planning. Effective risk management enhances accountability, governance, and competitive advantage, fostering sustainable resource allocation and improved performance.

The financial sector faces diverse risks, each impacting stability and profitability, necessitating robust risk management practices.

While existing research provides robust insights into bank-level risk management, few studies address national-level risk governance systems, particularly in contexts like MENA where macroeconomic shocks require coordinated cross-sectoral responses. Current frameworks either focus narrowly on financial institutions or examine corporate governance without scaling to sovereign risk dynamics.

Additionally, while the reviewed literature provides foundational insights into bank-level and macroeconomic risk management, it requires critical updates to reflect post-COVID systemic risk paradigms and clearer linkages to our analytical framework. Recent studies demonstrate that pandemic-era shocks fundamentally altered risk transmission mechanisms, with non-linear effects particularly pronounced in commodity-dependent regions like MENA.

3. Data and Variables Description

We employ a panel data Vector Autoregression (PVAR) framework, where all variables are considered endogenous, with the panel data technique, which accommodates unobserved individual heterogeneity across MENA countries (Love & Zicchino, 2006).

Additionally, Panel Vector Autoregression (PVAR) models have gained popularity in applied research. Although time-series VAR models are typically supported by built-in tools in most statistical software, estimating and conducting inference for panel VAR models (Abrigo & Love, 2016).

We used the PVAR model on a sample of 20 MENA countries over the period from 2005 to 2021, using relevant data from the World Bank Group. The PVAR model examines dynamic relationships between risk management and financial performance, offering deeper insights than traditional models and highlighting the risk performance of 20 MENA1 countries from 2005–2021, using a Panel Vector Autoregression (PVAR) model.

Data are sourced from the World Bank’s World Development Indicators (WDI), ensuring reliability and cross-country consistency. Additionally, these databases provide standardized, cross-country comparable metrics, ensuring the reliability and consistency of the empirical analysis. Key variables include return on equity, the Herfindahl–Hirschman Index (HHI), GDP per capita (in constant USD), the bank deposit-to-GDP ratio, stock market performance (S&P), the real interest rate (RIR), Regulatory quality (RQ), and Domestic credit to the private sector (% of GDP).

The PVAR approach captures dynamic interactions between risk indicators and economic outcomes while accounting for country-specific characteristics. The model considers all variables as endogenous, allowing us to analyze how shocks to risk factors propagate through financial systems over time.

The dependent variable is Return on Equity (ROE), representing financial performance. To capture market structure and competition, we include the Herfindahl–Hirschman Index (HH) as a measure of banking sector concentration. Macroeconomic conditions are controlled for using GDP per capita, reflecting economic development, and the Bank Deposit-to-GDP ratio (BD/GDP), which indicates financial depth.

Monetary policy and credit conditions are accounted for through the real interest rate (RIR), while stock market performance (S&P) captures capital market dynamics. Institutional quality is proxied by Regulatory Quality (RQ), assessing the strength of financial oversight. Finally, Domestic Credit (DC) is included to evaluate the role of credit expansion in financial stability.

The PVAR framework treats these variables as endogenous, allowing us to examine their dynamic interdependencies. Country-fixed effects control for unobserved heterogeneity, and standard errors are clustered at the country level. Lag selection is based on information criteria, and post-estimation diagnostics include impulse response functions and variance decompositions to assess shock propagation and relative variable importance.

This methodology provides robust insights into how risk management practices influence financial stability and economic performance in the MENA region, while addressing the unique challenges of panel data analysis in emerging market contexts.

4. Methodology

PVAR models extend VAR models to panel data, allowing researchers to analyze the relationships between multiple variables across different units over time, while accounting for potential heterogeneity and interdependencies between the units. The panel vector autoregression (PVAR) model was proposed by Holtz-Eakin et al. (1988).

The general equation to be estimated is as follows:

ROEit = ß0 + ß1 HHit + ß2 GDP PER CAPITAit + ß3 BD TO GDPit + ß4 RIRit + ß5 S&Pit + ß6 RQit + ß7 DCit + eit

(1)

In this equation:

-

ROEit is the dependent variable (return on equity) for entity i at time t;

-

HHit is the Herfindahl–Hirschman Index;

-

The GDP PER CAPITAit is the GDP per capita;

-

BD TO GDPit is the Bank Deposit-to-GDP ratio;

-

RIRit is the real interest rate;

-

S&Pit is the stock market performance;

-

RQit is the regulatory quality;

-

DCit is the domestic credit;

-

eit is the error term or noise term.

This study examines the impact of risk management performance on the financial outcomes of 20 MENA (see Note 1) countries over the period 2005–2021, utilizing the Panel Vector Autoregression (PVAR) model. The PVAR model is particularly suited for this analysis as it allows for the exploration of dynamic relationships between multiple variables over time, considering both cross-sectional (between-country) and temporal (within-country) dependencies. Given the diverse economic, regulatory, and institutional contexts across MENA countries, PVAR is an ideal method as it does not impose strict assumptions about the direction of causality, thereby enabling a more flexible and comprehensive exploration of the relationships among risk management factors and company performance.

The selection of the data period from 2005 to 2021 and the inclusion of 20 MENA countries were driven by both data availability and the relevance of this timeframe for analyzing the dynamics of risk management in the region. The period from 2005 to 2021 captures a critical phase in the MENA region’s economic history, marked by significant events such as the Global Financial Crisis (2007–2008), the Arab Spring (2010–2011), and their aftermath, which have had profound impacts on the financial systems and risk management practices across the region. These events led to shifts in economic policies, regulatory changes, and heightened volatility, making this period particularly relevant for examining how risk management practices have evolved in response to external shocks and crises. Furthermore, the data available from the World Bank Group within this timeframe were sufficient to construct a robust panel dataset.

The choice of the 20 MENA countries was based on their geographical and economic significance within the MENA region, with each country offering a unique perspective on the risk–performance relationship due to its distinct regulatory, economic, and political context. However, it is acknowledged that there are data limitations that may influence the generalizability of the findings.

For example, some countries in the region faced challenges in data collection due to conflicts, which may have limited the robustness of certain country-specific data points. Additionally, the political and economic instability in some MENA countries could introduce biases or variations in risk management practices that may not be fully representative of the entire region. While the study strives to account for these factors, the findings may be more applicable to countries with stable data availability and may require caution when generalizing to all MENA countries.

Table 1. Description of variables.

Variables	Acronym	Definition
1. Gross Domestic Product Per Capita	(GDP Per Capita)	GDP per capita reflects the average economic production per individual within a country. It is obtained by dividing the country’s Gross Domestic Product (GDP) by its total population. It provides an average income or standard of living indicator.
2. Herfindahl–Hirschman Index	(HH Index)	The Herfindahl–Hirschman Index (HHI) is a tool used to evaluate the concentration of a market, which in turn helps to gauge the degree of competition within a particular industry. The index is determined by adding together the squares of the market shares operating in the market.
3. Bank Deposits to Gross Domestic Product	(BD to GDP)	This ratio measures the size of a country’s bank deposits relative to its GDP. It is calculated by dividing total bank deposits by GDP. This indicator provides insights into the level of banking sector development and financial intermediation in an economy.
4. Return on Equity	(ROE)	Return on Equity (ROE) gauges a company’s profitability in relation to its shareholders’ equity. It is determined by dividing net income by shareholders’ equity. ROE reflects the efficiency with which a company utilizes its investors’ capital to generate profit.
5. Real Interest Rate	(RIR)	The Real Interest Rate represents the interest rate after adjusting for inflation, offering a clearer view of the actual cost of borrowing and the real return on savings. It provides a more accurate assessment of the purchasing power of interest payments.
6. Stock Market Performance	(S&P)	The S&P (the Standard & Poor’s 500 index) is considered as a benchmark for assessing global financial markets, particularly in understanding trends in developed economies. The performance of the S&P 500 is seen as a reflection of investor confidence and economic conditions, which can influence global investment flows, economic growth, and financial stability in both developed and developing countries.
7. Regulatory Quality	(RQ)	Regulatory Quality refers to the effectiveness of government policies and regulations in promoting private sector development.
8. Domestic Credit	(DC)	Domestic Credit refers to the total amount of credit provided by the financial sector to various sectors of the economy, including households, businesses, and the government. This credit includes loans, advances, and other forms of credit extended by commercial banks, savings institutions, and other financial intermediaries within a country.
Source: World Bank Data Base

provides a summary of the variables from the World Bank Database which are used in the analysis.

5. Discussion and Results

Table 2. The Fixed Effects Model.

	COEF	STD ERR	Z	p > Z	95%CONF	INTERVAL
GDP Per Capita	0.0000309	0.0000437	0.71	0.482	−0.000561	0.0001178
HH index	−0.5577142	14.45497	−0.04	0.969	−29.3296	28.21417
Bank Deposits to GDP	−0.0601301	0.0740856	−0.81	0.419	−0.2075938	0.0873336
Real interest Rate	−0.04112	0.03934	−1.05	0.299	−0.1194243	0.0371844
Domestic credit	−0.0551992	0.0517431	−1.07	0.289	−0.1581911	0.0477928
S&P	0.0104284	0.0119851	0.87	0.387	−0.0134274	0.0342842
Regulatory Quality	−0.0458871	0.0718119	−0.64	0.525	−0.1888251	0.0970508
CONS	22.53998	7.511908	3	0.004	7.587899	37.49206
SIGMA_U	3.8931036
SIGMA_E	3.5642576
rho	0.54401125					sd
Note: Dependent Variable ROE

,

Table 3. Random Effects Model.

	COEF	STD ERR	Z	p > Z	95%CONF	INTERVAL
GDP Per Capita	0.0000675	0.000012	5.64	0	0.0000441	0.000091
HH index	−2.306493	7.793389	−0.3	0.767	−17.58125	12.96827
Bank Deposits to GDP	0.007856	0.0094903	0.83	0.408	−0.0107447	0.0264567
Real INTEREST RATE	−0.0432309	0.0372688	−1.16	0.246	−0.1162763	0.0298146
Domestic CREDIT	−0.0961171	0.0218274	−4.4	0	−0.138898	−0.0533362
S&P	0.0118443	0.011721	1.01	0.312	−0.0111284	0.0348169
Regulatory Quality	−0.0817273	0.0348799	−2.34	0.019	−0.1500907	−0.0133639
CONS	18.92501	1.52803	12.39	0	15.93013	21.9199
SIGMA_U	0
SIGMA_E	3.5642576
rho	0
Note: Dependent Variable ROE

and

Table 4. Hausman test.

	(b) FE	(B) RE	(b-B) Difference	Sqrt S.E
GDP Per Capita	0.0000309	0.0000675	−0.0000367	0.0000415
HH index	−0.5577142	−2.306493	1.748778	11.97957
Bank Deposits to GDP	−0.0601301	0.007856	−0.067986	0.0726304
Real interest rate	−0.04112	−0.0432309	0.0021109	0.0111297
Domestic credit	−0.0551992	−0.0961171	0.0409179	0.0462677
S&P	0.0104284	0.0118443	−0.0014159	0.0017413
Regulatory quality	−0.0458871	−0.0817273	0.0358401	0.0618414
Note: Dependent Variable ROE

show that since the p-value for the comparison between the fixed effects (FE) and random effects (RE) models is 0.5235, which is greater than the conventional significance threshold of 0.05, we do not reject the null hypothesis. This indicates that the difference in coefficients between the two models is not statistically significant. Consequently, we conclude that the random effects model is the more appropriate choice for our analysis, as it is consistent and efficient. The random effects model accounts for both within-group and between-group variability, offering a more comprehensive understanding of the data. This decision highlights the robustness of the random effects model in capturing the nuances of the variables under consideration.

Table 3 presents the results of the Random Effects Model with Return on Equity (ROE) as the dependent variable, such as the following:

• GDP per capita (Coefficient: 0.0000675, p = 0.000)

Positive and significant: A significant positive relationship exists between GDP per capita and ROE. Specifically, for every unit increase in GDP per capita, ROE increases slightly. This suggests that economic growth, as reflected by rising GDP per capita, positively impacts corporate profitability.

• Herfindahl–Hirschman Index (HH Index) (Coefficient: −2.306493, p = 0.767)

Negative but not significant: The HH Index, a measure of market concentration, has a negative effect on ROE, but this effect is not statistically significant. This implies that market concentration does not appear to have a meaningful impact on corporate profitability in this model.

• Bank Deposit to GDP Ratio (Coefficient: 0.007856, p = 0.408)

Positive but not significant: The ratio of bank deposits to GDP is positively correlated with ROE, though the effect is not statistically significant. This suggests that while higher bank deposits may be associated with higher ROE, the relationship is not strong enough to be considered conclusive.

• Real Interest Rate (Coefficient: −0.0432309, p = 0.246)

Negative but not significant: The real interest rate has a negative effect on ROE, but this effect is not statistically significant. This indicates that changes in real interest rates do not substantially influence corporate profitability in this context.

• Domestic Credit (Coefficient: −0.0961171, p = 0.000)

Negative and significant: Domestic credit has a significant negative effect on ROE. Specifically, higher levels of domestic credit are associated with lower ROE, suggesting that excessive credit growth may undermine corporate profitability by leading to financial inefficiencies or instability.

• Stock Market Performance (S&P) (Coefficient: 0.0118443, p = 0.312)

Positive but not significant: Stock market performance shows a positive but statistically insignificant effect on ROE. This suggests that, in this model, stock market movements do not have a strong influence on corporate profitability.

• Regulatory Quality (Coefficient: −0.0817273, p = 0.019)

Negative and significant: Regulatory quality has a significant negative effect on ROE. This finding suggests that stricter regulatory frameworks might constrain corporate profitability, possibly by increasing operational costs or limiting firms’ flexibility.

The results shown in Table 2, Table 3 and Table 4 highlight several important relationships between the variables and ROE, with some findings being significant and others not.

The following is an expanded discussion of these results, particularly for variables like market concentration (HH Index) and bank deposits to GDP, where significance was lacking:

• GDP per capita

A significant positive relationship exists between GDP per capita and ROE, supporting the notion that economic prosperity leads to improved financial sector performance. This result aligns with the broader economic context of the MENA region, where higher GDP per capita is often associated with increased demand for financial services and greater corporate profitability. Policymakers should focus on policies that promote sustained economic growth, such as investments in infrastructure and human capital development.

• Domestic Credit

Domestic credit has a significant negative impact on ROE, suggesting that excessive credit growth can harm profitability, possibly due to increased financial risks or inefficiencies in credit allocation. This finding is particularly relevant to the MENA region, where rapid credit expansion without adequate regulation has led to financial instability in some countries. Financial regulators in the region should focus on implementing sound credit policies to ensure sustainable growth and reduce the risks of excessive credit.

• Regulatory Quality

Regulatory quality is significantly negatively associated with ROE. This result suggests that overly stringent or inefficient regulations can reduce business profitability. In the MENA context, where regulatory environments vary widely, this finding highlights the importance of regulatory reforms that balance the need for stability with the flexibility required for business growth. Policymakers should aim to streamline regulations to foster a more competitive and efficient business environment.

• Market Concentration(HH Index)

The lack of statistical significance for the Herfindahl–Hirschman Index (HHI) in this study suggests that, in the MENA region, market concentration may not have a clear or consistent effect on ROE across different countries or industries. It is possible that in some sectors, market concentration may indeed provide pricing power and higher profits, but in others, it may lead to reduced competition and innovation, thereby limiting profitability. This result underscores the need for more context-specific analyses that consider industry-specific dynamics and regional variations.

• Bank Deposits

The variable of bank deposits to GDP did not show a statistically significant effect on ROE. This suggests that, in the MENA context, the relationship between bank deposits and firm profitability may be more nuanced. While higher deposits can provide additional funding for lending, this does not always translate into higher profitability, particularly if banks face challenges in finding profitable lending opportunities. This finding may point to the need for further research into how different types of deposits and lending practices affect profitability in MENA economies.

• Real Interest Rate

The real interest rate showed a negative but not significant relationship with ROE. This indicates that while changes in interest rates may influence profitability, the effect is not strong enough to be considered statistically significant in the MENA region. The impact of interest rates on ROE may vary depending on the broader macroeconomic environment, including inflation, economic stability, and fiscal policies.

• Stock Market Performance

Stock market performance also did not show a statistically significant effect on ROE in this study. While volatility in stock markets can influence firm valuations and access to capital, the lack of significance suggests that other factors, such as market volatility or investor sentiment, may play a more prominent role in shaping profitability in the MENA region. The relationship between stock market performance and ROE may therefore be more context-dependent, requiring a more detailed exploration of specific market conditions.

6. Conclusions

This study highlights the critical role which effective risk management plays in shaping the long-term financial performance of countries. In today’s volatile global economy, anticipating, assessing, and mitigating risks is a cornerstone of strategic decision-making, influencing both short-term stability and long-term sustainability.

Risk analysis, a precursor to risk management, involves identifying potential risks, assessing their likelihood and impact, and prioritizing them based on their significance to a country’s economic future. This forward-looking approach enables efficient resource allocation and the mitigation of critical risks. Once risks are identified, tailored strategies such as avoidance, reduction, sharing, and retention are implemented to address current challenges and future uncertainties, considering factors like market conditions and economic health.

For financial institutions, particularly in the MENA region, managing risks related to credit, liquidity, and operational disruptions is essential. Policymakers must adopt forward-thinking regulatory frameworks to promote stability and resilience. This research offers actionable insights for refining risk management strategies, such as improving regulatory frameworks to enhance firm profitability and adopting balanced domestic credit management to prevent over-leveraging and ensure sustainable growth.

This study provides empirical evidence that forward-looking and foresight-driven risk management practices significantly influence the financial performance and long-term stability of economies in the MENA region. The findings suggest that well-structured risk management frameworks enhance resilience, mitigate financial vulnerabilities, and support sustainable economic growth.

These results align with existing research on MENA financial markets, which has documented volatility clustering and varying degrees of predictability in the region’s emerging stock markets. While prior studies indicate that most MENA markets exhibit stable returns and non-explosive volatility shocks, the present analysis reinforces the importance of proactive risk mitigation in maintaining financial stability. The low correlation among regional markets highlighted in the earlier literature further underscores the potential for diversification, but also the need for robust risk management to capitalize on these opportunities while minimizing systemic risks.

Given the dynamic and evolving nature of financial markets in the MENA region, policymakers and financial institutions must prioritize adaptive risk management strategies. Strengthening regulatory frameworks, enhancing transparency, and implementing predictive risk assessment tools can help mitigate volatility, attract investment, and foster long-term economic resilience.

Ultimately, effective risk management is not merely advantageous; it is a critical necessity for ensuring financial stability, sustaining growth, and securing the region’s economic future. By adopting forward-thinking and foresight-driven risk practices, MENA countries can better navigate uncertainties, enhance market confidence, and achieve enduring prosperity.

In conclusion, proactive risk management is a vital element of good governance and strategic foresight. Risks can have major and enduring repercussions on a country’s economic stability, yet in many countries policymakers are often caught by surprise, forcing them to approve unplanned measures, including cuts in many sectors of the economy. It is therefore important for countries to accept adequate procedures and institutional setups to manage exposure to these risks and help ensure sound economic growth to maintain stability. Policymakers should develop policies that would allow them to be better aware of risks. By systematically identifying and addressing risks, countries can safeguard their financial health, achieve sustainable profitability, and position themselves for long-term economic success.