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Bayesian Model Averaging in the Presence of Structural Breaks

Author

Listed:
  • Ravazzolo, F.
  • van Dijk, D.J.C.
  • Paap, R.
  • Franses, Ph.H.B.F.

Abstract

This paper develops a return forecasting methodology that allows for instabil ity in the relationship between stock returns and predictor variables, for model uncertainty, and for parameter estimation uncertainty. The predictive regres sion speci¯cation that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, and for uncertainty about the inclusion of forecasting variables, and about the parameter values by em ploying Bayesian Model Averaging. The implications of these three sources of uncertainty, and their relative importance, are investigated from an active investment management perspective. It is found that the economic value of incorporating all three sources of uncertainty is considerable. A typical in vestor would be willing to pay up to several hundreds of basis points annually to switch from a passive buy-and-hold strategy to an active strategy based on a return forecasting model that allows for model and parameter uncertainty as well as structural breaks in the regression parameters.

Suggested Citation

  • Ravazzolo, F. & van Dijk, D.J.C. & Paap, R. & Franses, Ph.H.B.F., 2006. "Bayesian Model Averaging in the Presence of Structural Breaks," Econometric Institute Research Papers EI 2006-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:7904
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    References listed on IDEAS

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    3. Kandel, Shmuel & Stambaugh, Robert F, 1996. " On the Predictability of Stock Returns: An Asset-Allocation Perspective," Journal of Finance, American Finance Association, vol. 51(2), pages 385-424, June.
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    5. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
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    7. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    8. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
    9. Pettenuzzo, Davide & Timmermann, Allan, 2011. "Predictability of stock returns and asset allocation under structural breaks," Journal of Econometrics, Elsevier, vol. 164(1), pages 60-78, September.
    10. Gary Koop & Simon M. Potter, 2004. "Forecasting and estimating multiple change-point models with an unknown number of change points," Staff Reports 196, Federal Reserve Bank of New York.
    11. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
    12. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, February.
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    Citations

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    Cited by:

    1. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, Elsevier.
    2. Schrimpf, Andreas, 2010. "International stock return predictability under model uncertainty," Journal of International Money and Finance, Elsevier, vol. 29(7), pages 1256-1282, November.
    3. Francesco Ravazzolo & Marco J. Lombardi, 2012. "Oil price density forecasts: Exploring the linkages with stock markets," Working Papers No 3/2012, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    5. Massimo Guidolin & Francesco Ravazzolo & Andrea Tortora, 2014. "Myths and Facts about the Alleged Over-Pricing of U.S. Real Estate," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 477-523, November.
    6. Massimo Guidolin & Francesco Ravazzolo & Andrea Donato Tortora, 2011. "Myths and Facts about the Alleged Over-Pricing of U.S. Real Estate. Evidence from Multi-Factor Asset Pricing Models of REIT Returns," Working Papers 416, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. van Dijk, D.J.C. & Franses, Ph.H.B.F. & Ravazzolo, F., 2007. "Evaluating real-time forecasts in real-time," Econometric Institute Research Papers EI 2007-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Cem Cakmakli & Dick van Dijk, 2010. "Getting the Most out of Macroeconomic Information for Predicting Stock Returns and Volatility," Tinbergen Institute Discussion Papers 10-115/4, Tinbergen Institute.
    9. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    10. Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis.
    11. Guidolin, Massimo & Ravazzolo, Francesco & Tortora, Andrea Donato, 2013. "Alternative econometric implementations of multi-factor models of the U.S. financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 53(2), pages 87-111.
    12. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
    13. Ravazzolo, F. & van Dijk, H.K. & Verbeek, M.J.C.M., 2007. "Predictive gains from forecast combinations using time-varying model weights," Econometric Institute Research Papers EI 2007-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    More about this item

    Keywords

    Bayesian model averaging; model uncertainty; portfolio selection; stock return predictability; structural breaks;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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