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Stochastic volatility with leverage: fast likelihood inference

Author

Listed:
  • Neil Shephard
  • Yashurio Omori
  • Faculty of Economics
  • University of Tokyo
  • Siddhartha Chib
  • Olin School of Business
  • Washington University
  • Jouchi Nakajima
  • Faculty of Economics
  • University of Tokyo

Abstract

Kim, Shephard, and Chib (1998) provided a Bayesian analysis of stochastic volatility models based on a fast and reliable Markov chain Monte Carlo (MCMC) algorithm. Their method rules out the leverage effect, which is known to be important in applications. Despite this, their basic method has been extensively used in the financial economics literature and more recently in macroeconometrics. In this paper we show how the basic approach can be extended in a novel way to stochastic volatility models with leverage without altering the essence of the original approach. Several illustrative examples are provided.

Suggested Citation

  • Neil Shephard & Yashurio Omori & Faculty of Economics & University of Tokyo & Siddhartha Chib & Olin School of Business & Washington University & Jouchi Nakajima & Faculty of Economics & University of, 2004. "Stochastic volatility with leverage: fast likelihood inference," Economics Series Working Papers 2004-FE-16, University of Oxford, Department of Economics.
    • Handle: RePEc:oxf:wpaper:2004-fe-16
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    References listed on IDEAS

    as
    1. Chib, Siddhartha & Greenberg, Edward, 1994. "Bayes inference in regression models with ARMA (p, q) errors," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 183-206.
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    3. Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
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