GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study
通过蒙特卡洛方法比较了随机波动率模型的不同广义矩估计方法，发现矩数量与目标函数质量之间存在权衡，并探讨了最优加权矩阵对估计和检验的影响，为处理条件异方差和矩相关提供了实用指南。
The authors examine alternative generalized method of moments procedures for estimation of a lognormal stochastic autoregressive volatility model by Monte Carlo methods. They document the existence of a trade-off 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 utilized 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 heteroskedasticity and correlation among the moments.