GMM ESTIMATION OF A STOCHASTIC VOLATILITY MODEL - A MONTE-CARLO STUDY

We examine alternative generalized method of moments procedures for es timation of a stochastic autoregressive volatility model by Monte Carl o methods. We document the existence of a tradeoff between the number of moments, or information, included in estimation and the quality, or precision, of the objective function used for estimation. Furthermore , an approximation to the optimal weighting matrix is used to explore the impact of the weighting matrix for estimation, specification testi ng, and inference procedures. The results provide guidelines that help achieve desirable small-sample properties in settings characterized b y strong conditional heteroscedasticity and correlation among the mome nts.