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Modeling multivariate extreme events using self-exciting point processes
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DOI：10.1016/j.jeconom.2014.03.011.png)
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Abstract
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We propose a model that can capture the typical features of multivariate extreme events observed in financial time series, namely, clustering behaviors in magnitudes and arrival times of multivariate extreme events, and time-varying dependence. The model is developed within the framework of the peaks-over-threshold approach in extreme value theory and relies on a Poisson process with self-exciting intensity. We discuss the properties of the model, treat its estimation, and address testing its goodness-of-fit. The model is applied to the return data of two stock markets. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Time series
Peaks-over-threshold
Hawkes processes
Extreme value theory
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