Dynamic Modelling of Large Dimensional Covariance Matrices

21 Pages Posted: 9 May 2006

See all articles by Valeri Voev

Valeri Voev

Aarhus University - CREATES

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

Voev, Valeri, Dynamic Modelling of Large Dimensional Covariance Matrices (February 7, 2007). Available at SSRN: https://ssrn.com/abstract=901072 or http://dx.doi.org/10.2139/ssrn.901072

Valeri Voev (Contact Author)

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics