@article {642785,
	title = {Fitting Vast Dimensional Time-Varying Covariance Models},
	journal = {Journal of Business and Economic Statistics},
	volume = {39},
	year = {2021},
	month = {2019},
	pages = {652-668},
	abstract = {Estimation of time-varying covariances is a key input in risk management and\&nbsp;asset allocation. ARCH-type multivariate models are used widely for this purpose.\&nbsp;Estimation of such models is computationally costly and parameter estimates are\&nbsp;meaningfully biased when applied to a moderately large number of assets. Here we\&nbsp;propose a novel estimation approach that suffers from neither of these issues, even\&nbsp;when the number of assets is in the hundreds. The theory of this new method is\&nbsp;developed in some detail. The performance of the proposed method is investigated\&nbsp;using extensive simulation studies and empirical examples.},
	author = {Robert F Engle and Pakel, Cavit and Kevin K. Shephard and Neil Shephard}
}
