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

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.

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)
Pages: 23 pages
Date: 2013-11
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

Downloads: (external link)
https://cama.crawford.anu.edu.au/sites/default/fil ... _2013_chan_hsiao.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2013-74

Access Statistics for this paper

More papers in CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University Contact information at EDIRC.
Bibliographic data for series maintained by Cama Admin (cama.admin@anu.edu.au).