Abstract: There has been considerable attention paid to estimation of conditional variance functions in the literature. We propose a nonparametric model for the conditional covariance matrix. A kernel estimator is developed, its asymptotic bias and variance are derived, and its asymptotic normality is established. A data example is used to illustrate the proposed procedure.
Key words and phrases: Conditional variance, heteroscedasticitym kernel regression, nonparametric covariance model, volatility.