Estimating value-at-risk: A point process approach

V. Chavez-Demoulin; A. C. Davison; A. J. Mcneil

doi:10.1080/14697680500039613

Estimating value-at-risk: A point process approach

V. Chavez-Demoulin^*, A. C. Davison, A. J. Mcneil

^*Corresponding author for this work

The York Management School

Research output: Contribution to journal › Article › peer-review

Abstract

We consider the modelling of extreme returns in financial time series, and introduce a marked point process model for the exceedances of a high threshold. This model has a self-exciting, Hawkes-process structure in which recent events affect the current intensity of threshold exceedances more than distant ones. Estimates of value-at-risk are derived for real datasets and the success of the estimation method is evaluated in backtests.

Original language	English
Pages (from-to)	227-234
Number of pages	8
Journal	Quantitative Finance
Volume	5
Issue number	2
DOIs	https://doi.org/10.1080/14697680500039613
Publication status	Published - Apr 2005

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10.1080/14697680500039613

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abstract = "We consider the modelling of extreme returns in financial time series, and introduce a marked point process model for the exceedances of a high threshold. This model has a self-exciting, Hawkes-process structure in which recent events affect the current intensity of threshold exceedances more than distant ones. Estimates of value-at-risk are derived for real datasets and the success of the estimation method is evaluated in backtests.",

author = "V. Chavez-Demoulin and Davison, {A. C.} and Mcneil, {A. J.}",

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AB - We consider the modelling of extreme returns in financial time series, and introduce a marked point process model for the exceedances of a high threshold. This model has a self-exciting, Hawkes-process structure in which recent events affect the current intensity of threshold exceedances more than distant ones. Estimates of value-at-risk are derived for real datasets and the success of the estimation method is evaluated in backtests.

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Estimating value-at-risk: A point process approach

Abstract

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