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Large dynamic covariance matrices: enhancements based on intraday data


De Nard, Gianluca; Engle, Robert F; Ledoit, Olivier; Wolf, Michael (2020). Large dynamic covariance matrices: enhancements based on intraday data. Working paper series / Department of Economics 356, University of Zurich.

Abstract

Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic covariances, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign (function) of the observed return.

Abstract

Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic covariances, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign (function) of the observed return.

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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Working Paper Series > Department of Economics
Dewey Decimal Classification:330 Economics
JEL Classification:C13, C58, G11
Uncontrolled Keywords:Dynamic conditional correlations, intraday data, Markowitz portfolio selection, multivariate GARCH, nonlinear shrinkage
Language:English
Date:July 2020
Deposited On:28 Jul 2020 06:41
Last Modified:28 Jul 2020 07:00
Series Name:Working paper series / Department of Economics
Number of Pages:35
ISSN:1664-705X
OA Status:Green
Official URL:https://www.econ.uzh.ch/en/research/workingpapers.html?paper-id=1038

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