Bayesian Estimation of the Global Minimum Variance Portfolio
(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:
https://lup.lub.lu.se/record/8052712
- author
- Bodnar, Taras ; Mazur, Stepan LU and Okhrin, Yarema
- organization
- publishing date
- 2015
- type
- Working paper/Preprint
- 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
- 2016-04-04 12:23:33
- date last changed
- 2018-11-21 21:10:41
@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}}, keywords = {{global minimum variance portfolio; posterior distribution; credible interval; Wishart distribution}}, language = {{eng}}, note = {{Working Paper}}, number = {{4}}, publisher = {{Department of Statistics, Lund university}}, series = {{Working Papers in Statistics}}, title = {{Bayesian Estimation of the Global Minimum Variance Portfolio}}, url = {{https://lup.lub.lu.se/search/files/5994420/8054231.pdf}}, year = {{2015}}, }