Stochastic Volatility Models: Conditional Normality Versus Heavy-Tailed Distributions

22 Pages Posted: 30 Dec 1997

See all articles by Roman Liesenfeld

Roman Liesenfeld

University of Cologne, Department of Economics

Robert Jung

University of Hohenheim - Institute of Economics

Date Written: September 1997

Abstract

Most of the empirical applications of the stochatic volatility (SV) model are based on the assumption that the conditional distribution of returns given the latent volatility process is normal. In this paper the SV model based on a conditional normal distribution is compared with SV specifications using conditional heavy-tailed distributions, especially Student's t-distribution and the generalized error distribution. To estimate the SV specifications a simulated maximum likelihood approach is applied. The results based on German stock market data reveal that the SV model with a conditional normal distribution does not adequately account for the two following empirical facts simultaneously: the leptokurtic distribution of the returns and the low but slowly decaying autocorrelation functions of the squared returns. It is shown that these empirical facts are more adequately captured by a SV model with a conditional heavy-tailed distribution. Finally, it turns out that the choice of the conditional distribution has systematic effects on the parameter estimates of the volatility process.

JEL Classification: C22, C52, C15

Suggested Citation

Liesenfeld, Roman and Jung, Robert C., Stochastic Volatility Models: Conditional Normality Versus Heavy-Tailed Distributions (September 1997). Available at SSRN: https://ssrn.com/abstract=41549 or http://dx.doi.org/10.2139/ssrn.41549

Roman Liesenfeld

University of Cologne, Department of Economics ( email )

Albertus-Magnus-Platz
D-50931 Köln
Germany

Robert C. Jung (Contact Author)

University of Hohenheim - Institute of Economics ( email )

Schloss-Mittelhof (Ost)
70593 Stuttgart
Germany

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