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dc.contributor.authorBade, Alexanderen_US
dc.contributor.authorFrahm, Gabrielen_US
dc.contributor.authorJaekel, Uween_US
dc.description.abstractWe develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm. The portfolio optimization is done using the first two moments of the predictive discrete asset return distribution. For illustration purposes we apply our method to empirical stock market data where daily asset logreturns are assumed to follow an orthogonal MGARCH process with t-distributed perturbations. Our results are compared with other portfolios suggested by popular optimization strategies.en_US
dc.publisher|aUniv., Seminar für Wirtschafts- und Sozialstatistik |cKölnen_US
dc.relation.ispartofseries|aDiscussion papers in statistics and econometrics |x1/08en_US
dc.subject.keywordBayesian portfolio optimizationen_US
dc.subject.keywordGordin's conditionen_US
dc.subject.keywordMarkov chain Monte Carloen_US
dc.subject.keywordStylized factsen_US
dc.titleA general approach to Bayesian portfolio optimizationen_US
dc.typeWorking Paperen_US

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