Bayesian Model Averaging in the Presence of Structural Breaks
Francesco Ravazzolo (),
Dick van Dijk (),
Richard Paap and
Philip Hans Franses
No EI 2006-33, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute
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.
Keywords: Bayesian model averaging; model uncertainty; portfolio selection; stock return predictability; structural breaks (search for similar items in EconPapers)
JEL-codes: C11 G11 G12 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:7904
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