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Stock and Bond Return Predictability : The Discrimination Power of Model Selection Criteria

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  • Rosario Dell'Aquila
  • Elvezio Ronchetti
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    Abstract

    We analyze the discrimination power of well-known model selection criteria when R2 is low as in typical asset return predictability studies. We find that the discrimination power is low in this setup and in particular give another interpretation to the well-cited Bossaerts and Hillion (1999) study. We then look at model selection criteria in a testing framework and propose, as a diagnostic tool, a bootstrap based procedure to construct the class of models which are statistically undistinguishable from the best model chosen by a model selection criterion. As an empirical illustration we reanalyze the Pesaran and Timmerman (1995) results and show that the class of undistiguishable models can be large. Finally we show that the similar problems arise in a more hidden way in the context of recent model uncertainty studies such as the Bayesian model selection criteria proposed by Avramov (2002) and Cremers (2002).

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    Bibliographic Info

    Paper provided by Institut d'Economie et Econométrie, Université de Genève in its series Research Papers by the Institute of Economics and Econometrics, Geneva School of Economics and Management, University of Geneva with number 2004.05.

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    Length: 25 pages
    Date of creation: Jun 2004
    Date of revision:
    Handle: RePEc:gen:geneem:2004.05

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    1. Campbell, John, 1987. "Stock Returns and the Term Structure," Scholarly Articles 3207699, Harvard University Department of Economics.
    2. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    3. Keim, Donald B. & Stambaugh, Robert F., 1986. "Predicting returns in the stock and bond markets," Journal of Financial Economics, Elsevier, vol. 17(2), pages 357-390, December.
    4. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    5. Bossaerts, Peter & Hillion, Pierre, 1999. "Implementing Statistical Criteria to Select Return Forecasting Models: What Do We Learn?," Review of Financial Studies, Society for Financial Studies, vol. 12(2), pages 405-28.
    6. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-91, January.
    7. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. " Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-64, December.
    8. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06.
    9. Christoffersen & Diebold, . "Further Results on Forecasting and Model Selection Under Asymmetric Loss," Home Pages _059, University of Pennsylvania.
    10. Foster, F Douglas & Smith, Tom & Whaley, Robert E, 1997. " Assessing Goodness-of-Fit of Asset Pricing Models: The Distribution of the Maximal R-Squared," Journal of Finance, American Finance Association, vol. 52(2), pages 591-607, June.
    11. K. J. Martijn Cremers, 2002. "Stock Return Predictability: A Bayesian Model Selection Perspective," Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1223-1249.
    12. Granger, Clive W. J. & Jeon, Yongil, 2004. "Thick modeling," Economic Modelling, Elsevier, vol. 21(2), pages 323-343, March.
    13. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
    14. Carlo A. Favero & Marco Aiolfi & Giorgio Primiceri, . "Recursive `thick´ modeling of excess returns and portfolio allocation," Working Papers 197, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    15. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    16. Granger, C.W.J. & Pesaran, H., 1996. "A Decision_Theoretic Approach to Forecast Evaluation," Cambridge Working Papers in Economics 9618, Faculty of Economics, University of Cambridge.
    17. Pesaran, M. H. & Weeks, M., 1999. "Non-nested Hypothesis Testing: An Overview," Cambridge Working Papers in Economics 9918, Faculty of Economics, University of Cambridge.
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    Cited by:
    1. Arnulfo Rodríguez & Pedro N. Rodríguez, 2007. "Recursive Thick Modeling and the Choice of Monetary Policy in Mexico," Working Papers 2007-04, Banco de México.
    2. Arnulfo Rodriguez & Pedro N. Rodriguez, 2006. "Recursive Thick Modeling and the Choice of Monetary Policy in Mexico," Computing in Economics and Finance 2006 30, Society for Computational Economics.

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