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Leverage, heavy-tails and correlated jumps in stochastic volatility models

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Author Info
Jouchi Nakajima (Bank of Japan)
Yasuhiro Omori (Faculty of Economics, University of Tokyo)

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Abstract

This paper proposes the efficient and fast Markov chain Monte Carlo estimation methods for the stochastic volatility model with leverage effects, heavy-tailed errors and jump components, and for the stochastic volatility model with correlated jumps. We illustrate our method using simulated data and analyze daily stock returns data on S&P500 index and TOPIX. Model comparisons are conducted based on the marginal likelihood for various SV models including the superposition model.

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File URL: http://www.e.u-tokyo.ac.jp/cirje/research/dp/2007/2007cf514.pdf
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Publisher Info
Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-514.

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Length: 34 pages
Date of creation: Sep 2007
Date of revision:
Handle: RePEc:tky:fseres:2007cf514

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This paper has been announced in the following NEP Reports: Cited by:
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  1. Luc Bauwens & Jeroen V.K. Rombouts, 2009. "On Marginal Likelihood Computation in Change-point Models," Cahiers de recherche 0942, CIRPEE. [Downloadable!]
  2. Jouchi Nakajima, 2008. "EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns," IMES Discussion Paper Series 08-E-23, Institute for Monetary and Economic Studies, Bank of Japan. [Downloadable!]
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