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. Copyright © 1997 Oxford University Press.
CitationWong, H., & Li, W. K. (1997). On a multivariate conditional heteroscedastic model. Biometrika, 84(1), 111-123. doi: 10.1093/biomet/84.1.111
- Causality in volatility
- Conditional heteroscedastic ARMA model
- Random coefficient model