Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices

被引：6

作者：

Morana, Claudio ^{[1
,2
,3
,4
]}

机构：

[1] Univ Milano Bicocca, Dipartimento Econ Metodi Quantitat & Strategie Im, Piazza Ateneo Nuovo 1, I-20126 Milan, Italy

[2] Univ Milano Bicocca, Ctr European Studies CefES Italy, Piazza Ateneo Nuovo 1, I-20126 Milan, Italy

[3] Coll Carlo Alberto, Ctr Res Pens & Welf Policies CeRP Italy, Piazza Vincenzo Arbarello 8, I-10122 Turin, Italy

[4] Rimini Ctr Econ Anal RCEA Canada, 75 Univ Ave W, Waterloo, ON N2L 3C5, Canada

来源：

ECONOMETRICS AND STATISTICS | 2019年 / 12卷

关键词：

Conditional covariance; Dynamic conditional correlation model; Semiparametric dynamic conditional correlation model; Multivariate GARCH; NONLINEAR SHRINKAGE; MULTIVARIATE; MARGINALIZATION; AGGREGATION; MODEL;

D O I：

10.1016/j.ecosta.2019.04.001

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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. (C) 2019 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

引用

页码：42 / 65

页数：24

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