Modeling extreme events: time-varying extreme tail shape

Bernd Schwaab, Xin Zhang and Andre Lucas

No 2524, Working Paper Series from European Central Bank

Abstract: We propose a dynamic semi-parametric framework to study time variation in tail parameters. The framework builds on the Generalized Pareto Distribution (GPD) for modeling peaks over thresholds as in Extreme Value Theory, but casts the model in a conditional framework to allow for time-variation in the tail shape parameters. The score-driven updates used improve the expected Kullback-Leibler divergence between the model and the true data generating process on every step even if the GPD only fits approximately and the model is mis-specified, as will be the case in any finite sample. This is confirmed in simulations. Using the model, we find that Eurosystem sovereign bond purchases during the euro area sovereign debt crisis had a beneficial impact on extreme upper tail quantiles, leaning against the risk of extremely adverse market outcomes while active. JEL Classification: C22, G11

Keywords: dynamic tail risk; European Central Bank (ECB); extreme value theory; observation-driven models; Securities Markets Programme (SMP) (search for similar items in EconPapers)
Date: 2021-02
New Economics Papers: this item is included in nep-ecm, nep-ore and nep-rmg
Note: 955417
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: Modeling Extreme Events: Time-Varying Extreme Tail Shape (2024)
Working Paper: Modeling extreme events:time-varying extreme tail shape (2023)
Working Paper: Modeling extreme events: time-varying extreme tail shape (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212524

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