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dc.contributor.authorPettenuzzo, Davide
dc.contributor.authorRavazzolo, Francesco
dc.date.accessioned2015-11-20T13:51:35Z
dc.date.available2015-11-20T13:51:35Z
dc.date.issued2015
dc.identifier.issn1892-2198
dc.identifier.urihttp://hdl.handle.net/11250/2365020
dc.description.abstractWe extend the density combination approach of Billio et al. (2013) to feature combination weights that depend on the past forecasting performance of the individual models entering the combination through a utility-based objective function. We apply our model combination scheme to forecast stock returns, both at the aggregate level and by industry, and investigate its forecasting performance relative to a host of existing combination methods. Overall, we find that our combination scheme produces markedly more accurate predictions than the existing alternatives, both in terms of statistical and economic measures of out-of-sample predictability. We also investigate the performance of our model combination scheme in the presence of model instabilities, by considering individual predictive regressions that feature time-varying regression coefficients and stochastic volatility. We find that the gains from using our combination scheme increase significantly when we allow for instabilities in the individual models entering the combination.nb_NO
dc.language.isoengnb_NO
dc.publisherBI Norwegian Business Schoolnb_NO
dc.relation.ispartofseriesCAMP Working Paper Series;9/2015
dc.subjectBayesian econometricsnb_NO
dc.subjectTime-varying parametersnb_NO
dc.subjectModel combinationsnb_NO
dc.subjectPortfolio choicenb_NO
dc.titleOptimal Portfolio Choice under Decision-Based Model Combinationsnb_NO
dc.typeWorking papernb_NO
dc.source.pagenumber63nb_NO
dc.source.issue9/2015nb_NO


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