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- W2092255802 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." @default.
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- W2092255802 date "2014-11-17" @default.
- W2092255802 modified "2023-09-27" @default.
- W2092255802 title "Multivariate autoregressive extreme value process and its application for modeling the time series properties of the extreme daily asset prices" @default.
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- W2092255802 doi "https://doi.org/10.1080/03610926.2013.791370" @default.
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