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Multivariate autoregressive extreme value process and its application for modeling the time series properties of the extreme daily asset prices
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 3
2016 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 45, no 12, 3421-3440 p.Article in journal (Refereed) Published
Abstract [en]

In this article we suggest a new multivariate autoregressive process for modeling time-dependent extreme value distributed observations. The idea behind the approach is to transform the original observations to latent variables that are univariate normally distributed. Then the vector autoregressive DCC model is fitted to the multivariate latent process. The distributional properties of the suggested model are extensively studied. The process parameters are estimated by applying a two-stage estimation procedure. We derive a prediction interval for future values of the suggested process. The results are applied in an empirically study by modeling the behavior of extreme daily stock prices.

Place, publisher, year, edition, pages
2016. Vol. 45, no 12, 3421-3440 p.
Keyword [en]
Multivariate extreme value distribution, Autoregressive process, Asymptotic normality, Prediction interval, Primary: 62M10, 62M20, 60G70, Secondary: 91B84
National Category
Mathematics
Identifiers
URN: urn:nbn:se:su:diva-130994DOI: 10.1080/03610926.2013.791370ISI: 000375864900002OAI: oai:DiVA.org:su-130994DiVA: diva2:935186
Available from: 2016-06-10 Created: 2016-06-09 Last updated: 2016-06-10Bibliographically approved

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Bodnar, Taras
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