In this paper we derive the finite-sample distribution of the estimated weights of the tangency portfolio when both the population and the sample covariance matrices are singular. These results are used in the derivation of a statistical test on the weights of the tangency portfolio where the distribution of the test statistic is obtained under both the null and alternative hypotheses. Moreover, we establish the high-dimensional asymptotic distribution of the estimated weights of the tangency portfolio when both the portfolio dimension and the sample size increase to infinity. The theoretical findings are implemented in an empirical application dealing with the returns on the stocks included into the S&P 500 index
We study the consistency of large-dimensional minimum variance portfolios that are estimated on the ...
In this thesis the effects of utilizing the sample covariance matrix in the estimation of the global...
We study the realized variance of sample minimum variance portfolios of arbitrarily high dimension. ...
In this paper, we study the distributional properties of the tangency portfolio (TP) weights assumin...
In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP)...
Recently, a test dealing with the linear hypothesis for the global minimum variance portfolio weight...
In this paper we derive the asymptotic distribution of the test of the efficiency of the tangency po...
In this paper, a sample estimator of the tangency portfolio (TP) weights is con-sidered. The focus i...
Bodnar and Schmid (2008) derived the distribution of the global minimum variance portfolio weights a...
In this paper, we construct two tests for the weights of the global minimum variance portfolio (GMVP...
Due to the problem of parameter uncertainty, specifying the location of the tangency portfolio (TP) ...
In this paper we consider the estimated weights of tangency portfolio. The returns are assumed to be...
In this paper, using the shrinkage-based approach for portfolio weights and modern results from rand...
The inverse of the standard estimate of covariance matrix is frequently used in the portfolio theory...
Optimal portfolio selection problems are determined by the (unknown) parameters of the data generat...
We study the consistency of large-dimensional minimum variance portfolios that are estimated on the ...
In this thesis the effects of utilizing the sample covariance matrix in the estimation of the global...
We study the realized variance of sample minimum variance portfolios of arbitrarily high dimension. ...
In this paper, we study the distributional properties of the tangency portfolio (TP) weights assumin...
In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP)...
Recently, a test dealing with the linear hypothesis for the global minimum variance portfolio weight...
In this paper we derive the asymptotic distribution of the test of the efficiency of the tangency po...
In this paper, a sample estimator of the tangency portfolio (TP) weights is con-sidered. The focus i...
Bodnar and Schmid (2008) derived the distribution of the global minimum variance portfolio weights a...
In this paper, we construct two tests for the weights of the global minimum variance portfolio (GMVP...
Due to the problem of parameter uncertainty, specifying the location of the tangency portfolio (TP) ...
In this paper we consider the estimated weights of tangency portfolio. The returns are assumed to be...
In this paper, using the shrinkage-based approach for portfolio weights and modern results from rand...
The inverse of the standard estimate of covariance matrix is frequently used in the portfolio theory...
Optimal portfolio selection problems are determined by the (unknown) parameters of the data generat...
We study the consistency of large-dimensional minimum variance portfolios that are estimated on the ...
In this thesis the effects of utilizing the sample covariance matrix in the estimation of the global...
We study the realized variance of sample minimum variance portfolios of arbitrarily high dimension. ...