Large dynamic covariance matrices: Enhancements based on intraday data

Multivariate GARCH models do 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 perform...

Full description

Saved in:
Bibliographic Details
Published in:Journal of banking & finance 2022-05, Vol.138, p.106426, Article 106426
Main Authors: De Nard, Gianluca, Engle, Robert F., Ledoit, Olivier, Wolf, Michael
Format: Article
Language:English
Subjects:
Dynamic conditional correlations
Intraday data
Markowitz portfolio selection
Multivariate GARCH
Nonlinear shrinkage
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Multivariate GARCH models do 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 correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.
ISSN:0378-4266
1872-6372
DOI:10.1016/j.jbankfin.2022.106426