Vasyl Golosnoy

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We propose a Conditional Autoregressive Wishart (CAW) model for the analysis of realized covariance matrices of asset returns. Our model assumes an autoregressive moving average structure for the scale matrix of the Wishart distribution. It accounts for positive definiteness of covariance matrices without imposing parametric restrictions, and can be(More)
The improvement of portfolio selection by means of multivariate shrinkage estimator for the optimal portfolio weights is a subject of this paper. The estimated classical Markowitz weights are shrunk to the vector of current portfolio weights, which is chosen as a shrinkage target. Assuming log asset returns to be Gaussian and i.i.d., the explicit solutions(More)
Using a novel three-phase model based upon a conditional autoregressive Wishart (CAW) framework for the realized (co)variances of the US Dow Jones and the German stock index DAX, we analyze intra-daily volatility spillovers between the US and German stock markets. The proposed model explicitly accounts for three distinct intraday periods resulting from the(More)
We consider a group of mean-variance investors with mimicking desire such that each investor is willing to penalize deviations of his portfolio composition from compositions of other group members. Penalizing norm constraints are already applied for statistical improvement of Markowitz portfolio procedure in order to cope with estimation risk. We relate(More)
Using a novel four-phase model based upon a conditional autoregressive Wishart framework for realized variances and covariances we quantify intra-daily volatility spillovers within and across the US, German and Japanese stock markets before and during the subprime crisis. We find significant short-term spillovers from one stock market to the next-trading(More)
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