Advanced

Bayesian Estimation of the Global Minimum Variance Portfolio

Bodnar, Taras; Mazur, Stepan LU and Okhrin, Yarema (2015) In Working Papers in Statistics
Abstract
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 distribution of the logarithmic returns is 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 andnon-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... (More)
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 distribution of the logarithmic returns is 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 andnon-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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Working Paper
publication status
published
subject
keywords
global minimum variance portfolio, posterior distribution, credible interval, Wishart distribution
in
Working Papers in Statistics
issue
4
pages
33 pages
publisher
Department of Statistics, Lund university
language
English
LU publication?
yes
id
a7497716-3353-455d-b253-7e6e37cb5fc5 (old id 8052712)
alternative location
http://journals.lub.lu.se/index.php/stat/article/view/15035
date added to LUP
2015-10-07 13:32:56
date last changed
2016-10-25 08:41:45
@misc{a7497716-3353-455d-b253-7e6e37cb5fc5,
  abstract     = {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 distribution of the logarithmic returns is 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 andnon-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.},
  author       = {Bodnar, Taras and Mazur, Stepan and Okhrin, Yarema},
  keyword      = {global minimum variance portfolio,posterior distribution,credible interval,Wishart distribution},
  language     = {eng},
  note         = {Working Paper},
  number       = {4},
  pages        = {33},
  publisher    = {Department of Statistics, Lund university},
  series       = {Working Papers in Statistics},
  title        = {Bayesian Estimation of the Global Minimum Variance Portfolio},
  year         = {2015},
}