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Regime Shift Model by Three Types of Distribution Considering a Heavy Tail and Dependence
| Authors and Corporations: | |
|---|---|
| Title: | Regime Shift Model by Three Types of Distribution Considering a Heavy Tail and Dependence |
| Language: | English |
| published: |
[S.l.]
SSRN
[2014]
|
| Item Description: | 1 Online-Ressource (14 p) ; Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments August 17, 2014 erstellt |
| DOI: | 10.2139/ssrn.2481956 |
| 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 |