GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
用蒙特卡洛方法研究随机自回归波动率模型的广义矩估计，发现矩数量与目标函数质量存在权衡，并探讨了最优加权矩阵对估计和检验的影响，为强条件异方差场景下的应用提供指导。
We examine alternative generalized method of moments procedures for estimation of a stochastic autoregressive volatility model by Monte Carlo 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 testing, and inference procedures. The results provide guidelines that help achieve desirable small-sample properties in settings characterized by strong conditional heteroscedasticity and correlation among the moments.