The detection and estimation of long memory in stochastic volatility
提出长记忆随机波动率模型，用ARFIMA过程刻画条件方差的持久性，通过谱似然估计参数，蒙特卡洛模拟和股票数据验证了模型的有效性。
We propose a new time series representation of persistence in conditional variance called a long memory stochastic volatility (LMSV) model. The LMSV model is constructed by incorporating an ARFIMA process in a standard stochastic volatility scheme. Strongly consistent estimators of the parameters of the model are obtained by maximizing the spectral approximation to the Gaussian likelihood. The finite sample properties of the spectral likelihood estimator are analyzed by means of a Monte Carlo study. An empirical example with a long time series of stock prices demonstrates the superiority of the LMSV