Large dynamic covariance matrices: enhancements based on intraday data

Gianluca De Nard, Robert Engle, Olivier Ledoit and Michael Wolf

No 356, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: 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.

Keywords: Dynamic conditional correlations; intraday data; Markowitz portfolio selection; multivariate GARCH; nonlinear shrinkage (search for similar items in EconPapers)
JEL-codes: C13 C58 G11 (search for similar items in EconPapers)
Date: 2020-07, Revised 2022-01
New Economics Papers: this item is included in nep-ecm and nep-ets
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Journal Article: Large dynamic covariance matrices: Enhancements based on intraday data (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:356

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