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

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  • Jouchi Nakajima

    (Institute for Monetary and Economic Studies, Bank of Japan (E-mail: jouchi.nakajima-1@boj.or.jp))

Abstract

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.

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File URL: http://www.imes.boj.or.jp/research/papers/english/08-E-23.pdf
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Bibliographic Info

Paper provided by Institute for Monetary and Economic Studies, Bank of Japan in its series IMES Discussion Paper Series with number 08-E-23.

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Date of creation: Sep 2008
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Handle: RePEc:ime:imedps:08-e-23

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

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