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Bayesian inference of the multi-period optimal portfolio for an exponential utility
Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.ORCID-id: 0000-0001-7855-8221
Antal upphovsmän: 42020 (Engelska)Ingår i: Journal of Multivariate Analysis, ISSN 0047-259X, E-ISSN 1095-7243, Vol. 175, artikel-id 104544Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

We consider the estimation of the multi-period optimal portfolio obtained by maximizing an exponential utility. Employing the Jeffreys non-informative prior and the conjugate informative prior, we derive stochastic representations for the optimal portfolio weights at each time point of portfolio reallocation. This provides a direct access not only to the posterior distribution of the portfolio weights but also to their point estimates together with uncertainties and their asymptotic distributions. Furthermore, we present the posterior predictive distribution for the investor's wealth at each time point of the investment period in terms of a stochastic representation for the future wealth realization. This in turn makes it possible to use quantile-based risk measures or to calculate the probability of default, i.e the probability of the investor wealth to become negative. We apply the suggested Bayesian approach to assess the uncertainty in the multi-period optimal portfolio by considering assets from the FTSE 100 in the weeks after the British referendum to leave the European Union. The behaviour of the novel portfolio estimation method in a precarious market situation is illustrated by calculating the predictive wealth, the risk associated with the holding portfolio, and the probability of default in each period.

Ort, förlag, år, upplaga, sidor
2020. Vol. 175, artikel-id 104544
Nyckelord [en]
Bayesian estimation, Credible sets, Multi-period optimal portfolio, Posterior predictive distribution, Stochastic representation
Nationell ämneskategori
Matematik
Identifikatorer
URN: urn:nbn:se:su:diva-178652DOI: 10.1016/j.jmva.2019.104544ISI: 000504777800001OAI: oai:DiVA.org:su-178652DiVA, id: diva2:1394590
Tillgänglig från: 2020-02-19 Skapad: 2020-02-19 Senast uppdaterad: 2020-03-05Bibliografiskt granskad

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