Dynamic Modelling of Large Dimensional Covariance Matrices
21 Pages Posted: 9 May 2006
Date Written: February 7, 2007
Modelling and forecasting the covariance of fiancial return series has always been a challenge due to the so-called curse of dimensionality. This paper proposes a methodology that is applicable in large dimensional cases and is based on a time series of realized covariance matrices. Some solutions are also presented to the problem of non-positive definite forecasts. This methodology is then compared to some traditional models on the basis of its forecasting performance employing Diebold-Mariano tests. We show that our approach is better suited to capture the dynamic features of volatilities and covolatilities compared to the sample covariance based models.
Keywords: Forecasting, realized covariance, shrinking
JEL Classification: C13, C32, G10
Suggested Citation: Suggested Citation