High-frequency financial data modeling using Hawkes processes

Détails

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Etat: Supprimée
Version: Final published version
ID Serval
serval:BIB_1BC9BF93E651
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
High-frequency financial data modeling using Hawkes processes
Périodique
Journal of Banking and Finance
Auteur⸱e⸱s
Chavez-Demoulin V., McGill J. A.
ISSN
0378-4266
Statut éditorial
Publié
Date de publication
12/2012
Peer-reviewed
Oui
Volume
36
Numéro
12
Pages
3415-3426
Langue
anglais
Résumé
Intraday Value-at-Risk (VaR) is one of the risk measures used by market participants involved in high-frequency trading. High-frequency log-returns feature important kurtosis (fat tails) and volatility clustering (extreme log-returns appear in clusters) that VaR models should take into account. We propose a marked point process model for the excesses of the time series over a high threshold that combines Hawkes processes for the exceedances with a generalized Pareto distribution model for the marks (exceedance sizes). The conditional approach features intraday clustering of extremes and is used to calculate instantaneous conditional VaR. The models are backtested on real data and compared to a competitor approach that proposes a nonparametric extension of the classical peaks-over-threshold method. Maximum likelihood estimation is computationally intensive; we use a differential evolution genetic algorithm to find adequate starting values for the optimization process.
Mots-clé
Hawkes process, High-frequency data, Peaks-over-threshold, Self-exciting process, Value-at-risk
Création de la notice
04/11/2011 8:38
Dernière modification de la notice
20/08/2019 12:52
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