Modeling extreme events: time-varying extreme tail shape

Bernd Schwaab, Xin Zhang and Andre Lucas
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Xin Zhang: Sveriges Riksbank

No 20-076/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: A dynamic semi-parametric framework is proposed to study time variation in tail fatness of sovereign bond yield changes during the 2010--2012 euro area sovereign debt crisis measured at a high (15-minute) frequency. 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-sepcified, as will be the case in any finite sample. This is confirmed in simulations. Using the model, we find the ECB program had a beneficial impact on extreme upper tail quantiles, leaning against the risk of extremely adverse market outcomes while active.

Keywords: dynamic tail risk; observation-driven models; extreme value theory; European Central Bank (ECB); Securities Markets Programme (SMP) (search for similar items in EconPapers)
JEL-codes: C22 G11 (search for similar items in EconPapers)
Date: 2020-11-10
New Economics Papers: this item is included in nep-eec, nep-ore and nep-rmg
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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 (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20200076

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