"Quasi-Maximum Likelihood Estimation of Conditional Autoregressive Wishart Models"

Abstract:

"In this paper, we consider a quasi-maximum likelihood (QML)

estimation of conditional autoregressive Wishart (CAW) models for the

covariance process. Strong consistency is established under the

existence of the expectation of the log of the determinant. Sufficient

conditions for asymptotic normality of the QML estimator are derived.

Monte Carlo experiments show an inefficiency caused by using

non-Wishart distributions, which are negligible for the sample size

T=500. We use the daily covariance matrix of the returns of the Nikkei

225 index and its futures for the QML estimation of the CAW model. The

results indicate its appropriateness for the QML estimation."