Large dynamic covariance matrices: Enhancements based on intraday data
针对多元GARCH模型在高维中的维度诅咒问题，本文利用OHLC价格数据替代日收益率，通过正则化回报概念改进动态方差和相关性建模，提升性能。
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.