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Multivariate autoregressive extreme value process and its application for modeling the time series properties of the extreme daily asset prices

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  • Rostyslav Bodnar
  • Taras Bodnar
  • Wolfgang Schmid

Abstract

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.

Suggested Citation

  • Rostyslav Bodnar & Taras Bodnar & Wolfgang Schmid, 2016. "Multivariate autoregressive extreme value process and its application for modeling the time series properties of the extreme daily asset prices," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(12), pages 3421-3440, June.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:12:p:3421-3440
    DOI: 10.1080/03610926.2013.791370
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