Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence
Joshua Chan and
Cody Yu-Ling Hsiao ()
CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University
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.
Keywords: stochastic volatility; scale mixture of normal; state space model; Markov chain Monte Carlo; financial data (search for similar items in EconPapers)
JEL-codes: C11 C22 C58 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2013-74
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