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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Large dynamic covariance matrices: enhancements based on intraday data
Organization Unit
Authors
  • Gianluca De Nard
  • Robert F Engle
  • Olivier Ledoit
  • Michael Wolf
Language
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 356
ISSN 1664-705X
Number of Pages 40
Date 2022
Abstract Text 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.
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Keywords Dynamic conditional correlations, intraday data, Markowitz portfolio selection, multivariate GARCH, nonlinear shrinkage
Additional Information Revised version