A three-step estimation strategy for dynamic conditional correlation (DCC) 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 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 optimally smoothed. Due to its scant computational burden, the proposed regularized semiparametric DCC model (RSP-DCC) allows to estimate high dimensional conditional covariance and correlation matrices. An application to global minimum variance portfolio is also provided, confirming that RSP-DCC is a simple and viable alternative to existing DCC models.

Morana, C. (2019). Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices. ECONOMETRICS AND STATISTICS, 12, 42-65 [10.1016/j.ecosta.2019.04.001].

Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices

Morana, C
2019

Abstract

A three-step estimation strategy for dynamic conditional correlation (DCC) 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 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 optimally smoothed. Due to its scant computational burden, the proposed regularized semiparametric DCC model (RSP-DCC) allows to estimate high dimensional conditional covariance and correlation matrices. An application to global minimum variance portfolio is also provided, confirming that RSP-DCC is a simple and viable alternative to existing DCC models.
Articolo in rivista - Articolo scientifico
Scientifica
Dynamic conditional correlation model, Conditional covariance, Semiparametric dynamic conditional correlation model, Multivariate GARCH
English
Morana, C. (2019). Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices. ECONOMETRICS AND STATISTICS, 12, 42-65 [10.1016/j.ecosta.2019.04.001].
Morana, C
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/228504
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