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.