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A0734
Title: Regularized semiparametric estimation of vast dynamic conditional covariance matrices Authors:  Claudio Morana - Universita di Milano Bicocca (Italy) [presenting]
Abstract: A three-step estimation strategy for dynamic conditional correlation models is proposed. In the first step, conditional variances for individual and aggregate series are estimated by means of QML equation by equation. In the second step, conditional covariances are estimated by means of the polarization identity and consistent estimates of the conditional correlations are obtained by their usual normalization. In the third step, the two-step conditional covariance and correlation matrices are regularized by means of a new non-linear shrinkage procedure and used as starting value for the maximization of the joint likelihood of the model. This yields the final, third step smoothed estimate of the conditional covariance and correlation matrices. Due to its scant computational burden, the proposed strategy allows to estimate vast conditional covariance and correlation matrices. An application to financial data is also provided.