Reflected Ornstein-Uhlenbeck process is a process that returns continuously and immediately to the interior of the state space when it attains a certain boundary. It is an extended model of the traditional Ornstein-Uh...Reflected Ornstein-Uhlenbeck process is a process that returns continuously and immediately to the interior of the state space when it attains a certain boundary. It is an extended model of the traditional Ornstein-Uhlenbeck process being extensively used in finance as a one-factor short-term interest rate model. In this paper, under certain constraints, we are concerned with the problem of estimating the unknown parameter in the reflected Ornstein-Uhlenbeck processes with the general drift coefficient. The methodology of estimation is built upon the maximum likelihood approach and the method of stochastic integration. The strong consistency and asymptotic normality of estimator are derived. As a by-product of the use, we also establish Girsanov's theorem of our model in this paper.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.11326174,11401245 and 11225104)Natural Science Foundation of Jiangsu Province(Grant No.BK20130412)+3 种基金Natural Science Research Project of Ordinary Universities in Jiangsu Province(Grant No.12KJB110003)China Postdoctoral Science Foundation(Grant No.2014M551720)Jiangsu Government Scholarship for Overseas Studies,Zhejiang Provincial Natural Science Foundation(Grant No.R6100119)the Fundamental Research Funds for the Central Universities
文摘Reflected Ornstein-Uhlenbeck process is a process that returns continuously and immediately to the interior of the state space when it attains a certain boundary. It is an extended model of the traditional Ornstein-Uhlenbeck process being extensively used in finance as a one-factor short-term interest rate model. In this paper, under certain constraints, we are concerned with the problem of estimating the unknown parameter in the reflected Ornstein-Uhlenbeck processes with the general drift coefficient. The methodology of estimation is built upon the maximum likelihood approach and the method of stochastic integration. The strong consistency and asymptotic normality of estimator are derived. As a by-product of the use, we also establish Girsanov's theorem of our model in this paper.
