Modeling extreme events:time-varying extreme tail shape

Bernd Schwaab, Xin Zhang (), Andre Lucas and Enzo D’Innocenzo ()
Additional contact information
Xin Zhang: Research Department, Central Bank of Sweden, Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
Enzo D’Innocenzo: University of Bologna, Postal: Bologna, Italy

No 399, Working Paper Series from Sveriges Riksbank (Central Bank of Sweden)

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 parameters. We establish parameter regions for stationarity and ergodicity and for the existence of (unconditional) moments and consider conditions for consistency and asymptotic normality of the maximum likelihood estimator for the deterministic parameters in the model. Two empirical datasets illustrate the usefulness of the approach: daily U.S. equity returns, and 15-minute euro area sovereign bond yield changes.

Keywords: dynamic tail risk; observation-driven models; extreme value theory; stock return tails; Securities Markets Programme (SMP). (search for similar items in EconPapers)
JEL-codes: C22 G11 (search for similar items in EconPapers)
Pages: 90 pages
Date: 2020-12-01, Revised 2023-06-01
New Economics Papers: this item is included in nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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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 (2021)
Working Paper: Modeling extreme events: time-varying extreme tail shape (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hhs:rbnkwp:0399

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