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

25 Pages Posted: 1 Nov 2008

See all articles by Nikolaus Hautsch

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

Yangguoyi Ou

Humboldt University of Berlin - School of Business and Economics

Date Written: October 30, 2008

Abstract

In this paper, we review the most common specifications of discrete-time stochastic volatility (SV) models and illustrate the major principles of corresponding Markov Chain Monte Carlo (MCMC) based statistical inference. We provide a hands-on approach 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 algorithm, Jump Processes

JEL Classification: C15, C22, G12

Suggested Citation

Hautsch, Nikolaus and Ou, Yangguoyi, Discrete-Time Stochastic Volatility Models and MCMC-Based Statistical Inference (October 30, 2008). Available at SSRN: https://ssrn.com/abstract=1292494 or http://dx.doi.org/10.2139/ssrn.1292494

Nikolaus Hautsch (Contact Author)

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Gr├╝neburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

Yangguoyi Ou

Humboldt University of Berlin - School of Business and Economics ( email )

Spandauer Str. 1
Berlin, D-10099
Germany

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