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- W3125077607 abstract "Financial time series often exhibit properties that depart from the usual assumptions of serial independence and normality. These include volatility clustering, heavy-tailedness and serial dependence. A voluminous literature on different approaches for modeling these empirical regularities has emerged in the last decade. In this paper we review the estimation of a variety of highly flexible stochastic volatility models, and introduce some efficient algorithms based on recent advances in state space simulation techniques. These estimation methods are illustrated via empirical examples involving precious metal and foreign exchange returns. The corresponding Matlab code is also provided." @default.
- W3125077607 created "2021-02-01" @default.
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- W3125077607 date "2013-11-01" @default.
- W3125077607 modified "2023-09-27" @default.
- W3125077607 title "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence" @default.
- W3125077607 hasPublicationYear "2013" @default.
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