Multivariate ARCH and GARCH Models
In the previous chapters, we have discussed modelling the conditional mean of the data generation process of a multiple time series, conditional on the past at each particular time point. In that context, the variance or covariance matrix of the conditional distribution was assumed to be time invariant. In fact, in much of the discussion, the residuals or forecast errors were assumed to be independent white noise. Such a simplification is useful and justified in many applications.
KeywordsGARCH Model Conditional Volatility Conditional Covariance Conditional Heteroskedasticity Generalize Impulse Response
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