Univariate and Multivariate Stochastic Volatility Models: Estimation and Diagnostics

Posted: 12 Dec 2002

See all articles by Roman Liesenfeld

Roman Liesenfeld

University of Cologne, Department of Economics

Jean-Francois Richard

University of Pittsburgh - Department of Economics

Abstract

A Maximum Likelihood (ML) approach based upon an Efficient Importance Sampling (EIS) procedure is used to estimate several extensions of the standard Stochastic Volatility (SV) model for daily financial return series. EIS provides a highly generic procedure for a very accurate Monte Carlo evaluation of the marginal likelihood which depends upon high-dimensional interdependent integrals. Extensions of the standard SV model being analyzed only require minor modifications in the ML-EIS procedure. Furthermore, EIS can also be applied for filtering which provides the basis for several diagnostic tests. Our empirical analysis indicates that extensions such as a semi-nonparametric specification of the error term distribution in the return equation dominate the standard SV model. Finally, we also apply the ML-EIS approach to a multivariate factor model with stochastic volatility.

Keywords: Efficient Importance Sampling, Latent Variables, Maximum likelihood

JEL Classification: C15, C22, C52

Suggested Citation

Liesenfeld, Roman and Richard, Jean-Francois, Univariate and Multivariate Stochastic Volatility Models: Estimation and Diagnostics. Available at SSRN: https://ssrn.com/abstract=357820

Roman Liesenfeld (Contact Author)

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
D-50931 Köln
Germany

Jean-Francois Richard

University of Pittsburgh - Department of Economics ( email )

4901 Wesley Posvar Hall
230 South Bouquet Street
Pittsburgh, PA 15260
United States
412-648-1750 (Phone)

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