Essentials of Time Series for Financial Applications

Chapter 6

Multivariate GARCH and Conditional Correlation Models

Abstract

This chapter deals with multivariate time series methods applied to jointly model and forecast variances and covariances when more than two series are involved. Section 6.1 introduces the importance of multivariate applications in finance, and in particular explaining the problem of parameter proliferation typical of sample-based, model-free approaches. Section 6.2 shows how the simple models presented in Chapter 5, can be extended to forecast also conditional covariances and hence correlations. Section 6.3 focuses on full multivariate extensions of GARCH models and explains how to address the problem of over-parameterization. Section 6.4 gives the details of models that can ...