Stepan Mazur: Bayesian inference for the tangent portfolio

Abstract: We consider the estimation of the weights of tangent portfolios from a 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 portfolios 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 with a simulation study, where the precision of coverage intervals is assessed.