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- W2271946858 abstract "I adopt a regime shift model to investigate a shift of distribution of each regime during a time series data. Unlike previous studies, I applied three types of distribution to use a regime shift model, i.e., normal, GEV and stable distribution, which allows me to consider a heavy tail regime in the model. From some theoretical basis and empirical results, I find that the regime shift model in stable distribution is best appropriate. I also find that tail index of the innovation and dependence measure move together, implying dependence among a consecutive data may lead extreme event and vice versa." @default.
- W2271946858 created "2016-06-24" @default.
- W2271946858 creator A5067574474 @default.
- W2271946858 date "2014-01-01" @default.
- W2271946858 modified "2023-09-25" @default.
- W2271946858 title "Regime Shift Model by Three Types of Distribution Considering a Heavy Tail and Dependence" @default.
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- W2271946858 doi "https://doi.org/10.2139/ssrn.2481956" @default.
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