Abstract
Tsay (1987) developed the conditional heteroscedastic autoregressive moving-average model, which includes the conditional heteroscedastic autoregressive and random coefficient autoregressive models as special cases. This paper establishes the multivariate conditional heteroscedastic autoregressive moving-average model, and considers its theoretical properties and applications. Maximum likelihood estimation of the model is discussed in detail. A representation of the information matrix is obtained using the star product. This enhances estimation and statistical inferences procedures. Some simulation results and an application to the volatility of the Standard & Poor's 500 and Sydney's All Ordinaries indices are also considered.
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