English, Article, Journal or magazine article edition: EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns Jouchi Nakajima

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Bookmark: http://trove.nla.gov.au/version/35325
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns
Author
  • Jouchi Nakajima
Physical Description
  • preprint
Notes
  • 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.
  • Bayesian analysis, EGARCH, Heavy-tailed error, Jumps, Marginal likelihood, Markov chain Monte Carlo, Stochastic volatility
  • RePEc:ime:imedps:08-e-23
Language
  • English
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