Soufiane Benbachir

In today’s volatile financial environment, banks encounter various risks, including political instability, regulatory changes, and global market fluctuations, which can undermine efficiency and threaten systemic stability. This study focuses on banking efficiency in the MENA region, highlighting its crucial role in economic growth and financial stability. This paper addresses the gap in banking efficiency research in the MENA region by evaluating the technical and pure technical efficiency of 59 conventional banks from 11 MENA countries between 2019 and 2023 and identifying the internal and external factors affecting their efficiency. Using a Data Envelopment Analysis, the study evaluates efficiency based on three inputs and two outputs. A panel Tobit regression model is then applied to analyze the impact of eight internal factors and four external factors on efficiency. The findings indicate that just 16% of the MENA banks were technically efficient, with Qatari banks outperforming and banks in Morocco and Jordan underperforming. The Tobit regression model results indicate that both return on assets and capital adequacy positively influence technical efficiency (TE) and pure technical efficiency (PTE). In contrast, Liquidity and operational costs negatively affect PTE and TE. Non-performing loans negatively impact TE but not PTE, and macroeconomic factors positively influence both TE and PTE. In conclusion, banks in the MENA region must prioritize improving their efficiency to stay competitive. The findings offer valuable insights into operational best practices and provide practical guidance for policymakers, regulators, and banking institutions to enhance the performance of the region’s financial systems.

This paper investigates the multifractal behavior of the six largest African stock markets, including the Johannesburg, Casablanca, Botswana, Nigerian, Egyptian, and Regional Stock Exchanges. Despite the growing significance of these markets in the global economy, there is limited understanding of their underlying dynamics, particularly regarding their multifractal properties. This lack of knowledge raises concerns about the informational efficiency of these markets, as traditional models may not adequately capture the complexities of price movements. To achieve the goals of the study, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method is applied to capture the multifractal dynamics, and shuffling and phase randomization techniques are performed to identify the sources of the multifractality of the six African stock markets. The empirical results, derived from the generalized Hurst exponents, Rényi exponents, and Singularity spectrum, show that all six stock markets display multifractal behavior, characterized by irregular and complex price movements that vary across different scales and timeframes. Additionally, the study finds that both long-term correlations and heavy-tailed distributions contribute to the observed multifractality. Long-term correlations lead to persistent price trends, challenging the Efficient Market Hypothesis (EMH), while heavy tails increase market unpredictability by raising the likelihood of extreme events like crashes or booms. The findings have significant practical implications for stakeholders in African stock markets, enabling investors and portfolio managers to enhance risk assessment and develop effective trading strategies while helping market regulators improve efficiency and stability through appropriate policies. Financial institutions can also refine risk management frameworks to better account for extreme events.

Informational efficiency is a fundamental pillar of well-functioning financial markets, as it underlies informed investment decisions, effective risk management, and broader economic stability, particularly in emerging African markets, where inefficiencies are more likely to persist. This study assesses the weak-form informational efficiency of six major African stock markets – Johannesburg, Casablanca, Botswana, Nigeria, Egypt, and the Regional Stock Exchange – through the lens of long-memory behavior in returns and volatility. This is achieved by employing four advanced models: ARFIMA-FIGARCH, ARFIMA-FIEGARCH, ARFIMA-FIAPARCH, and ARFIMA-HYGARCH. Each of these models is specifically designed to capture long memory in both the conditional mean and variance. The empirical results demonstrate that the ARFIMA-FIGARCH framework, across all four model variants, consistently outperformed alternative specifications in fitting the return and volatility dynamics of all six African stock market indices. The estimated fractional differencing parameters in both the mean (dARFIMA) and variance (dFIGARCH) equations were highly statistically significant at the 1% level for each market, confirming the presence of persistent long-memory behavior. This strong evidence of long-range dependence implies that past return information is not fully reflected in current prices, thereby violating the assumptions of weak-form market efficiency. Consequently, these findings provide compelling and systematic evidence against the weak-form Efficient Market Hypothesis (EMH) for the markets studied, highlighting a common structural inefficiency across the African financial landscape.

Type of the article: Research Article

Abstract
Systemic risk in the banking sector poses a major threat to financial stability, particularly in concentrated markets such as Morocco, where the failure of one institution may trigger widespread disruptions. Understanding the extent to which individual banks contribute to systemic risk and generate spillover effects is essential for sustaining financial system resilience. This study aims to assess the systemic risk contributions and spillover potential of six listed Moroccan banks, Attijariwafa Bank (ATW), Bank of Africa (BOA), Banque Marocaine pour le Commerce et l’Industrie (BCI), Banque Centrale Populaire (BCP), Crédit Immobilier et Hôtelier (CIH), and Crédit du Maroc (CDM), by applying the Conditional Value-at-Risk (CoVaR) methodology. The analysis uses daily return data covering the period from January 4, 2010 to January 10, 2025. Value-at-Risk (VaR) estimates at 99% and 95% confidence levels show that the three largest banks, ATW, BCP, and BOA, are the least individually risky banks under normal market conditions, suggesting greater stability. In contrast, the smallest banks, CDM, BCI, and CIH, exhibit higher individual risk exposure. CoVaR and ΔCoVaR (marginal CoVaR) results indicate that ATW, BCP, and BOA are the primary contributors to systemic risk, with a higher potential for spillover during times of distress, while the remaining banks are less systemically significant. These findings highlight the need for enhanced macroprudential oversight and regular stress testing for larger institutions, alongside improved internal risk controls for smaller banks. The study emphasizes the importance of data-driven regulatory strategies in mitigating systemic vulnerabilities and strengthening the long-term stability of Morocco’s banking sector.