BAYESIAN INFERENCE FOR THE TANGENT PORTFOLIO

David Bauder, Taras Bodnar, Stepan Mazur and Yarema Okhrin
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David Bauder: Department of Mathematics, Humboldt-University of Berlin, D-10099 Berlin, Germany
Taras Bodnar: Department of Mathematics, Stockholm University, SE-10691 Stockholm, Sweden
Yarema Okhrin: Department of Statistics, University of Augsburg, D-86159 Augsburg, Germany

International Journal of Theoretical and Applied Finance (IJTAF), 2018, vol. 21, issue 08, 1-27

Abstract: In this paper, we consider the estimation of the weights of tangent portfolios from the Bayesian point of view assuming normal conditional distributions of the logarithmic returns. For diffuse and conjugate priors for the mean vector and the covariance matrix, we derive stochastic representations for the posterior distributions of the weights of tangent portfolio and their linear combinations. Separately, we provide the mean and variance of the posterior distributions, which are of key importance for portfolio selection. The analytic results are evaluated within a simulation study, where the precision of coverage intervals is assessed.

Keywords: Asset allocation; tangent portfolio; Bayesian analysis; diffuse and conjugate priors; stochastic representation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (12)

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Related works:
Journal Article: BAYESIAN INFERENCE FOR THE TANGENT PORTFOLIO (2018)
Working Paper: Bayesian inference for the tangent portfolio (2018)
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DOI: 10.1142/S0219024918500541

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