Predicting Extreme Losses in Periods of Financial Stress Using the Asymptotic Marginal Expected Shortfall Decision Criterion
Keywords:
marginal expected shortfall, asymptotic marginal expected shortfall, systemic riskAbstract
This study aims to examine and predict extreme losses during periods of financial stress by applying the asymptotic marginal expected shortfall (AMES) criterion and to compare its predictive capacity with traditional systemic risk measures. The AMES measure was theoretically defined and formulated based on multivariate extreme value theory to model tail dependencies between banks. Daily price data of twelve active banks listed on the Tehran Stock Exchange from 2017 to 2022 were analyzed. Each bank’s contribution to systemic risk was calculated, and the predictive performance of AMES was tested against the conventional marginal expected shortfall. The results revealed that AMES significantly outperforms the traditional marginal expected shortfall in forecasting severe losses during extreme systemic events, offering distinct and richer information about systemic vulnerability and bank-level risk contributions. The additive property of AMES makes it a robust and practical tool for fair systemic risk allocation among banks. Relying solely on simple characteristics such as size or individual risk is insufficient; AMES provides a more accurate basis for supervisory decisions and capital requirement design in times of financial crisis.
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Copyright (c) 2025 Mojtaba Khoddam (Author); Azar Moslemi (Corresponding author); Mohsen Rashidi (Author)
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