Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices

Claudio Morana

Econometrics and Statistics, 2019, vol. 12, issue C, 42-65

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.

Keywords: Conditional covariance; Dynamic conditional correlation model; Semiparametric dynamic conditional correlation model; Multivariate GARCH (search for similar items in EconPapers)
JEL-codes: C32 C58 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Working Paper: Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:12:y:2019:i:c:p:42-65

DOI: 10.1016/j.ecosta.2019.04.001

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