Discussion Paper Series 2008-E-23

EGARCH and Stochastic Volatility:Modeling Jumps and Heavy-tails for Stock Returns

Jouchi Nakajima

This paper proposes the EGARCH model with jumps and heavy-tailed errors, and studies the empirical performance of different models including the stochastic volatility models with leverage, jumps and heavy-tailed errors for daily stock returns. In the framework of a Bayesian inference, the Markov chain Monte Carlo estimation methods for these models are illustrated with a simulation study. The model comparison based on the marginal likelihood estimation is provided with data on the U.S. stock index.

Keywords: Bayesian analysis; EGARCH; Heavy-tailed error; Jumps; Marginal likelihood; Markov chain Monte Carlo; Stochastic volatility

Views expressed in the paper are those of the authors and do not necessarily reflect those of the Bank of Japan or Institute for Monetary and Economic Studies.

Copyright © 2008 Bank of Japan All Rights Reserved.

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