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Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices

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  • Claudio, Morana

Abstract

This paper proposes a three-step estimation strategy for dynamic conditional correlation models. 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 conditional correlations are estimated 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 high dimensional conditional covariance and correlation matrices. An application to global minimum variance portfolio is also provided, confirming that SP-DCC is a simple and viable alternative to existing DCC models.

Suggested Citation

  • Claudio, Morana, 2018. "Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices," Working Papers 382, University of Milano-Bicocca, Department of Economics, revised 04 Jun 2018.
  • Handle: RePEc:mib:wpaper:382
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    References listed on IDEAS

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    Keywords

    Conditional covariance; Dynamic conditional correlation model; Semiparametric dynamic conditional correlation model; Multivariate GARCH.;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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