On a multivariate conditional heteroscedastic model

Heung WONG, Wai Keung LI

Research output: Contribution to journalArticlespeer-review

47 Citations (Scopus)

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. Copyright © 1997 Oxford University Press.
Original languageEnglish
Pages (from-to)111-123
JournalBiometrika
Volume84
Issue number1
DOIs
Publication statusPublished - Mar 1997

Citation

Wong, H., & Li, W. K. (1997). On a multivariate conditional heteroscedastic model. Biometrika, 84(1), 111-123. doi: 10.1093/biomet/84.1.111

Keywords

  • Causality in volatility
  • Conditional heteroscedastic ARMA model
  • Random coefficient model
  • Volatility

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