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Comparing and evaluating Bayesian predictive distributions of asset returns

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  • Geweke, John
  • Amisano, Gianni

Abstract

Bayesian inference in a time series model provides exact out-of-sample predictive distributions that fully and coherently incorporate parameter uncertainty. This study compares and evaluates Bayesian predictive distributions from alternative models, using as an illustration five alternative models of asset returns applied to daily S&P 500 returns from the period 1976 through 2005. The comparison exercise uses predictive likelihoods and is inherently Bayesian. The evaluation exercise uses the probability integral transformation and is inherently frequentist. The illustration shows that the two approaches can be complementary, with each identifying strengths and weaknesses in models that are not evident using the other.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 26 (2010)
Issue (Month): 2 (April)
Pages: 216-230

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Handle: RePEc:eee:intfor:v:26:y::i:2:p:216-230

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Forecasting GARCH Inverse probability transformation Markov mixture Predictive likelihood S&P 500 returns Stochastic volatility;

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  1. John Geweke & Gianni Amisano, 2008. "Optimal Prediction Pools," Working Paper Series 22-08, The Rimini Centre for Economic Analysis, revised Jan 2008.
  2. Valentina Corradi & Norman Swanson, 2004. "Predictive Density Evaluation," Departmental Working Papers 200419, Rutgers University, Department of Economics.
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  12. BAUWENS , Luc & GIOT, Pierre & GRAMMIG, Joachim & VEREDAS, David, 2000. "A comparison of financial duration models via density forecasts," CORE Discussion Papers 2000060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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  14. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
  15. Lewis, Kurt F. & Whiteman, Charles H., 2006. "Empirical Bayesian density forecasting in Iowa and shrinkage for the Monte Carlo era," Discussion Paper Series 1: Economic Studies 2006,28, Deutsche Bundesbank, Research Centre.
  16. Geweke, John, 2001. "Bayesian econometrics and forecasting," Journal of Econometrics, Elsevier, vol. 100(1), pages 11-15, January.
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