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Estimation of Contagion: Bayesian Model Averaging on Tail Dependence of Mixture Copula

Author

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  • Sundusit Saekow

    (Applied Statistics Program, Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Phisanu Chiawkhun

    (Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Woraphon Yamaka

    (Faculty of Economic, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Nawapon Nakharutai

    (Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Parkpoom Phetpradap

    (Department of Mathematics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

This study introduces a novel approach to estimate tail dependence in financial contagion using mixture copulas. Addressing the challenges of weight parameter estimation in conventional models, we propose a Bayesian model averaging method to determine optimal copula weights. Through both simulations and empirical studies, the proposed method demonstrates improved robustness and accuracy, particularly when handling extreme weight scenarios. These advancements offer more reliable measurements of financial contagion, contributing to enhanced risk management and policy-making in interconnected financial markets.

Suggested Citation

  • Sundusit Saekow & Phisanu Chiawkhun & Woraphon Yamaka & Nawapon Nakharutai & Parkpoom Phetpradap, 2024. "Estimation of Contagion: Bayesian Model Averaging on Tail Dependence of Mixture Copula," Mathematics, MDPI, vol. 12(21), pages 1-23, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:21:p:3350-:d:1506758
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