Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference

Nikolaus Hautsch () and Yangguoyi Ou

No SFB649DP2008-063, SFB 649 Discussion Papers from Humboldt University, Collaborative Research Center 649

Abstract: In this paper, we review the most common specifications of discrete-time stochas- tic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on ap- proach which is easily implemented in empirical applications and financial practice and can be straightforwardly extended in various directions. We illustrate empirical results based on different SV specifications using returns on stock indices and foreign exchange rates.

Keywords: Stochastic Volatility; Markov Chain Monte Carlo; Metropolis-Hastings al- Jump Processes (search for similar items in EconPapers)
JEL-codes: C15 C22 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2008-09
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Persistent link: http://EconPapers.repec.org/RePEc:hum:wpaper:sfb649dp2008-063

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