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  • 统计与管理学院2017年学术报告第83期

    【主 题】 Estimating the Integrated Volatility Using High-Frequency Data with Zero Durations

    【报告人】 刘 志 助教授

    澳门大学

    【时 间】 2017年12月25日(星期一)13:30-14:30

    【地 点】 上海财经大学统计与管理学院大楼1316会议室

    摘 要】In estimating integrated volatility using high-frequency data, it is well documented that the presence of microstructure noise presents a major challenge. Recent literature has shown that the presence of multiple observations, a common feature in datasets, brings additional difficulty. In this study, we show that the pre-averaging estimator is still consistent under multiple observations, and the related asymptotic distribution of the estimator is established. We also show that the pre-averaging estimator based on multiple observations achieves the same asymptotic efficiency as the “ideal” estimator that assumes we know the exact trading times of all transactions. Simulation studies support the theoretical results, and we also illustrate the estimator using real.

    嘉宾简介】刘志,澳门大学数学系助理教授。2011年博士毕业于香港科技大学。主要研究方向包括:金融高频数据分析,随机过程统计推断,生物信息等。其研究近年来获得了多项基金的资助,在统计学、金融和生物信息国际期刊发表论文30余篇,其中包括AoS, JASA, JoE, JBES, Bioinformatics等优秀期刊。2017年获澳门大学优秀研究奖。