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dc.contributor.authorRavazzolo F
dc.contributor.authorPaap R
dc.contributor.authorvan Dijk D
dc.contributor.authorFranses PH
dc.contributor.editorRapach DE
dc.date.accessioned2019-02-14T16:54:12Z
dc.date.available2019-02-14T16:54:12Z
dc.date.issued2008
dc.identifier.isbn978-0-444-52942-8
dc.identifier.issn1574-8715
dc.identifier.urihttp://dx.doi.org/10.1016/S1574-8715(07)00215-1
dc.identifier.urihttps://www.emeraldinsight.com/doi/abs/10.1016/S1574-8715(07)00215-1
dc.identifier.urihttp://hdl.handle.net/10863/8240
dc.description.abstractThis chapter develops a return forecasting methodology that allows for instability in the relationship between stock returns and predictor variables, model uncertainty, and parameter estimation uncertainty. The predictive regression specification that is put forward allows for occasional structural breaks of random magnitude in the regression parameters, uncertainty about the inclusion of forecasting variables, and uncertainty about parameter values by employing 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 investor 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.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.publisherEmerald Publishing Groupen_US
dc.relation.ispartofseriesFrontiers of Economics and Globalization;
dc.rights
dc.titleBayesian model averaging in the presence of structural breaksen_US
dc.typeBook chapteren_US
dc.date.updated2019-01-07T09:01:46Z
dc.publication.titleForecasting in the presence of structural breaks and model uncertainty
dc.language.isiEN-GB
dc.description.fulltextreserveden_US


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