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Bayesian estimation...
Bayesian estimation of the global minimum variance portfolio
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- Bodnar, Taras (författare)
- Stockholm University,Stockholms universitet,Matematiska institutionen
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- Mazur, Stepan, 1988- (författare)
- Lund University,Lunds universitet,Statistiska institutionen,Ekonomihögskolan,Department of Statistics,Lund University School of Economics and Management, LUSEM
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- Okhrin, Yarema (författare)
- Department of Statistics, University of Augsburg, Augsburg, Germany
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(creator_code:org_t)
- Elsevier, 2017
- 2017
- Engelska.
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Ingår i: European Journal of Operational Research. - : Elsevier. - 0377-2217 .- 1872-6860. ; 256:1, s. 292-307
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
Nyckelord
- Global minimum variance portfolio
- Posterior distribution
- Credible interval
- Wishart distribution
- Statistik
- Statistics
- Credible interval
- Global minimum variance portfolio
- Posterior distribution
- Wishart distribution
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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