摘要
The GARCH diffusion model has received much attention in recent years, as it describes financial time series better when compared to many other models. In this paper, the authors study the empirical performance of American option pricing model when the underlying asset follows the GARCH diffusion. The parameters of the GARCH diffusion model are estimated by the efficient importance sampling-based maximum likelihood （EIS-ML） method. Then the least-squares Monte Carlo （LSMC） method is introduced to price American options. Empirical pricing results on American put options in Hong Kong stock market shows that the GARCH diffusion model outperforms the classical constant volatility （CV） model significantly.
基金
supported by the National Natural Science Foundations of China under Grant No.71201013
the National Science Fund for Distinguished Young Scholars of China under Grant No.70825006
the Program for Changjiang Scholars and Innovative Research Team in University under Grant No.IRT0916
the National Natural Science Innovation Research Group of China under Grant No.71221001
