统计与管理学院2018年学术报告第15期

【主 题】 On estimation of Hurst parameter under noisy observations

【报告人】 刘广应  副教授

南京审计大学

【时 间】 2018年05月19日（星期六）09:30-10:15

【地 点】 上海财经大学统计与管理学院大楼1114报告厅

【摘 要】It is widely accepted that some financial data exhibit long memory or long dependence, and that the observed data usually possess noise. In the continuous time situation, the factional Brownian motion BH and its extension are an important class of models to characterize the long memory or short memory of data, and Hurst parameter H is an index to describe the degree of dependence. In this paper, we estimate the Hurst parameter of a discretely sampled fractional integral process corrupted by noise. We use the pre–average method to diminish the impact of noise, employ the filter method to exclude the strong dependence and obtain the smoothed data, and estimate the Hurst parameter by the smoothed data. The asymptotic properties such as consistency and asymptotic normality of the estimator are established. Simulations for evaluating the performance of the estimator are conducted.