On a multivariate conditional heteroscedastic model

Heung WONG, Wai Keung LI

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45 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

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Heteroscedastic Model
Conditional Model
Volatilization
Autoregressive Moving Average Model
Random Coefficient Models
Star Products
Information Matrix
Autoregressive Model
Statistical Inference
Maximum Likelihood Estimation
Volatility
Maximum likelihood estimation
Stars
Autoregressive moving average model
Conditional model
Simulation
Model
Statistical inference
Random coefficients
Autoregressive model

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