EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns
No 08-E-23, IMES Discussion Paper Series from Institute for Monetary and Economic Studies, Bank of Japan
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 (search for similar items in EconPapers)
JEL-codes: C11 C15 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fmk and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:ime:imedps:08-e-23
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