Y
Yarema Okhrin
Researcher at University of Augsburg
Publications - 68
Citations - 1289
Yarema Okhrin is an academic researcher from University of Augsburg. The author has contributed to research in topics: Covariance matrix & Estimator. The author has an hindex of 16, co-authored 64 publications receiving 1123 citations. Previous affiliations of Yarema Okhrin include Goethe University Frankfurt & European University Viadrina.
Papers
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Distributional properties of portfolio weights
Yarema Okhrin,Wolfgang Schmid +1 more
TL;DR: In this paper, the conditional density for the Sharpe ratio optimal weights were derived and the asymptotic distributions of the estimated weights were determined under the assumption that the returns follow a multivariate stationary Gaussian process.
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On the structure and estimation of hierarchical Archimedean copulas
TL;DR: A hierarchical estimation procedure for the parameters and an asymptotic analysis for the marginal distributions is introduced and the effectiveness of the grouping procedure in the sense of structure selection is shown.
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Multivariate Shrinkage for Optimal Portfolio Weights
Vasyl Golosnoy,Yarema Okhrin +1 more
TL;DR: In this article, a multivariate shrinkage estimator for the optimal portfolio weights is proposed, where the estimated classical Markowitz weights are shrunk to the deterministic target portfolio weights.
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Properties of the singular, inverse and generalized inverse partitioned Wishart distributions
Taras Bodnar,Yarema Okhrin +1 more
TL;DR: In this paper, the distributions and independency properties of several generalizations of the Wishart distribution were discussed and an analog to Muirhead's theorem 3.2.10 was established for the partitioned matrix A=(A"i"j)"i,j"="1,1,2") in the case of arbitrary partitioning for singular and inverse Wishart distributions.
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Bayesian Estimation of the Global Minimum Variance Portfolio
TL;DR: This paper derives the posterior distributions of the portfolio weights using the standard priors for the mean vector and the covariance matrix and reparameterizes the model to allow informative and non-informative priors directly for the weights of the global minimum variance portfolio.