Systemic risk and economic sectoral interdependencies in the Tunisian stock market: a Random Matrix Theory approach

Sirine Ben Yaala and Jamel Eddine Henchiri

Journal of Economic Studies, 2025, vol. 53, issue 7, 1410-1430

Abstract: PurposeThis study investigates the correlation structure, systemic risk, and sectoral interdependencies in the Tunisian stock market during the pre-COVID-19, COVID-19, and post-COVID-19 periods using Random Matrix Theory (RMT) and spectral analysis.Design/methodology/approachRMT and spectral analysis were applied to correlation matrices across different periods to examine market dependencies, eigenvalue distributions, and systemic risk. Kolmogorov–Smirnov tests further validated the statistical significance of the observed spectral deviations, while eigenportfolio returns were analyzed to capture patterns of market synchronization and financial contagion.FindingsFindings reveal that during the COVID-19 crisis, stock dependencies intensified, particularly within banking and financial sectors, reducing diversification opportunities. Post-crisis, financial services, industrials, and consumer goods remained highly interconnected, reflecting persistent structural shifts. Systemic risk peaked during the crisis and stayed elevated afterward, with Kolmogorov–Smirnov tests confirming deviations from the Marchenko–Pastur law. Eigenvector analysis further showed banking and insurance as the main risk drivers during the crisis, while industrial and consumer goods gained prominence in the post-pandemic period.Research limitations/implicationsThe findings contribute to the literature on systemic risk propagation, showing how eigenvalue and eigenvector analyses can detect shifts in market-wide and sectoral risk.Practical implicationsRegulators should reinforce macroprudential measures and monitor sectoral interdependencies, particularly in the banking and insurance sectors, to contain contagion risks. Investors should adapt diversification strategies in response to elevated correlations and systemic risk concentrations. Spectral analysis can serve as an early-warning tool for financial instability.Social implicationsReducing systemic risk transmission helps protect small investors and other vulnerable market participants, strengthening market resilience without making unsupported claims about foreign investment or generalized investor confidence.Originality/valueThis study extends the application of RMT to an emerging market context, offering a novel and statistically robust approach to assessing systemic risk. It challenges the assumption that markets naturally revert to normal post-crisis by demonstrating persistent structural dependencies.

Keywords: Systemic risk; Sectoral interdependencies; Random Matrix Theory; Spectral analysis; Eigenvalue decomposition (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eme:jespps:jes-04-2025-0246

DOI: 10.1108/JES-04-2025-0246

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