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Dynamic Bivariate Peak Over Threshold Model for Joint Tail Risk Dynamics of Financial Markets

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  • Zifeng Zhao

Abstract

We propose a novel dynamic bivariate peak over threshold (PoT) model to study the time-varying behavior of joint tail risk in financial markets. The proposed framework provides simultaneous modeling for dynamics of marginal and joint tail risk, and generalizes the existing tail risk literature from univariate dimension to multivariate dimension. We introduce a natural and interpretable tail connectedness measure and examine the dynamics of joint tail behavior of global stock markets: empirical evidence suggests markets from the same continent have time-varying and high-level joint tail risk, and tail connectedness increases during periods of crisis. We further enrich the tail risk literature by developing a novel portfolio optimization procedure based on bivariate joint tail risk minimization, which gives promising risk-rewarding performance in backtesting.

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  • Zifeng Zhao, 2021. "Dynamic Bivariate Peak Over Threshold Model for Joint Tail Risk Dynamics of Financial Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 892-906, October.
    • Handle: RePEc:taf:jnlbes:v:39:y:2021:i:4:p:892-906
    DOI: 10.1080/07350015.2020.1737083
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    Cited by:

    1. Ke, Rui & Yang, Luyao & Tan, Changchun, 2022. "Forecasting tail risk for Bitcoin: A dynamic peak over threshold approach," Finance Research Letters, Elsevier, vol. 49(C).
    2. Feng, Yun & Hou, Weijie & Song, Yuping, 2023. "Tail risk in the Chinese stock market: An AEV model on the maximal drawdowns," Finance Research Letters, Elsevier, vol. 58(PA).
    3. Lin Deng & Michael Stanley Smith & Worapree Maneesoonthorn, 2023. "Large Skew-t Copula Models and Asymmetric Dependence in Intraday Equity Returns," Papers 2308.05564, arXiv.org, revised Jul 2024.

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