Adaptive Thresholding for Large Volatility Matrix Estimation Based on High-Frequency Financial Data
报告题目：Adaptive Thresholding for Large Volatility Matrix Estimation Based on High-Frequency Financial Data
报告人：李翠霞 博士 兰州大学
报告时间：2015年10月20日（周二）上午9:00-10:00
报告地点：数学楼二楼学术报告厅
报告摘要：Universal thresholding estimators have been developed to estimate the large sparse integrated volatility matrix. Since the integrated volatility matrix often has entries with a wide range of variability, universal thresholding estimators do not take the varying entries into consideration and have unsatisfactory performances.
This paper investigates adaptive thresholding estimation of large volatility matrix. We first construct an estimator for the asymptotic variance of the pre-averaging realized volatility estimator to develop an adaptive thresholding estimator of the large volatility matrix. It is shown that the adaptive thresholding estimator can achieve the optimal rate of convergence over the class of the sparse integrated volatility matrix when both the number of assets and sample size are allowed to go to infinity, while the universal thresholding estimator can achieve only the sub-optimal convergence rate. The simulation study is conducted to check the finite sample performance of the adaptive thresholding estimator.
报告时间：2015年10月20日（周二）上午9:00-10:00
报告地点：数学楼二楼学术报告厅
报告摘要：Universal thresholding estimators have been developed to estimate the large sparse integrated volatility matrix. Since the integrated volatility matrix often has entries with a wide range of variability, universal thresholding estimators do not take the varying entries into consideration and have unsatisfactory performances.
This paper investigates adaptive thresholding estimation of large volatility matrix. We first construct an estimator for the asymptotic variance of the pre-averaging realized volatility estimator to develop an adaptive thresholding estimator of the large volatility matrix. It is shown that the adaptive thresholding estimator can achieve the optimal rate of convergence over the class of the sparse integrated volatility matrix when both the number of assets and sample size are allowed to go to infinity, while the universal thresholding estimator can achieve only the sub-optimal convergence rate. The simulation study is conducted to check the finite sample performance of the adaptive thresholding estimator.