In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using nonnested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail. Copyright 1998 by The Review of Economic Studies Limited.
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Gourieroux, C & Monfort, A & Renault, E, 1993.
"Indirect Inference,"
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 8(S), pages S85-118, Suppl. De.
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Gourieroux, C. & Monfort, A. & Renault, E., 1992.
"Indirect Inference,"
Papers
92.279, Toulouse - GREMAQ.
Gourieroux, C. & Monfort, A & Renault, E., 1992.
"Indirect Inference,"
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Ghysels, E. & Harvey, A. & Renault, E., 1996.
"Stochastic Volatility,"
Cahiers de recherche
9613, Universite de Montreal, Departement de sciences economiques.
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Ghysels, E. & Harvey, A. & Renault, E., 1996.
"Stochastic Volatility,"
Cahiers de recherche
9613, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
repec:cup:etheor:v:12:y:1996:i:4:p:657-81 is not listed on IDEAS
repec:cup:etheor:v:12:y:1996:i:3:p:409-31 is not listed on IDEAS
Gourieroux, C. & Monfort, A., 1991.
"Testing Non Nested Hypotheses,"
Papers
9207, Institut National de la Statistique et des Etudes Economiques-.
Other versions:
Gourieroux, C. & Monfort, A., 1986.
"Testing non-nested hypotheses,"
Handbook of Econometrics,
in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 44, pages 2583-2637
Elsevier.
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