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.

Enzo D’Innocenzo, A.L. (2024). Modeling extreme events: time-varying extreme tail shape*. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 42(3), 903-917 [10.1080/07350015.2023.2260439].

Modeling extreme events: time-varying extreme tail shape*

Enzo D’Innocenzo;
2024

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.
2024
Enzo D’Innocenzo, A.L. (2024). Modeling extreme events: time-varying extreme tail shape*. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 42(3), 903-917 [10.1080/07350015.2023.2260439].
Enzo D’Innocenzo, André Lucas, Bernd Schwaab and Xin Zhang
File in questo prodotto:
File Dimensione Formato  
Modeling Extreme Events Time-Varying Extreme Tail Shape.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 5.47 MB
Formato Adobe PDF
5.47 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/942513
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 1
social impact