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Bayesian estimation of the global minimum variance portfolio
Stockholm University, Faculty of Science, Department of Mathematics.
Number of Authors: 3
2017 (English)In: European Journal of Operational Research, ISSN 0377-2217, E-ISSN 1872-6860, Vol. 256, no 1, 292-307 p.Article in journal (Refereed) Published
Abstract [en]

In this paper we consider the estimation of the weights of optimal portfolios from the Bayesian point of view under the assumption that the conditional distributions of the logarithmic returns are normal. Using the standard priors for the mean vector and the covariance matrix, we derive the posterior distributions for the weights of the global minimum variance portfolio. Moreover, we reparameterize the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio. The posterior distributions of the portfolio weights are derived in explicit form for almost all models. The models are compared by using the coverage probabilities of credible intervals. In an empirical study we analyze the posterior densities of the weights of an international portfolio.

Place, publisher, year, edition, pages
2017. Vol. 256, no 1, 292-307 p.
Keyword [en]
Global minimum variance portfolio, Posterior distribution, Credible interval, Wishart distribution
National Category
Mathematics
Identifiers
URN: urn:nbn:se:su:diva-136245DOI: 10.1016/j.ejor.2016.05.044ISI: 000384854500027OAI: oai:DiVA.org:su-136245DiVA: diva2:1053161
Available from: 2016-12-08 Created: 2016-12-01 Last updated: 2016-12-08Bibliographically approved

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Bodnar, Taras
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