Abstract #301496

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JSM 2003 Abstract #301496
Activity Number: 19
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: Business & Economics Statistics Section
Abstract - #301496
Title: Flexible Multivariate GARCH Modeling
Author(s): Michael Wolf*+ and Pedro Santa-Clara and Olivier Ledoit
Companies: Universitat Pompeu Fabra and Anderson School UCLA and Credit Suisse First Boston
Address: Dept. of Economics & Business, Barcelona, , 08005, Spain
Keywords: multivariate GARCH ; forecasting ; risk management
Abstract:

The goal of this paper is to estimate time-varying covariance matrices. Since the covariance matrix of financial returns is known to change through time and is an essential ingredient in risk measurement, portfolio selection, and tests of asset pricing models, this is a very important problem in practice. Our model of choice is the Diagonal-Vech version of the Multivariate GARCH(1,1) model. The problem is that the estimation of the general Diagonal-Vech model model is numerically infeasible in dimensions higher than 5. The common approach is to estimate more restrictive models which are tractable but may not conform to the data. Our contribution is to propose an alternative estimation method that is numerically feasible, produces positive semidefinite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the new estimator to a number of existing ones.

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