关于举行新加坡国立大学周望教授和澳门大学刘志教授学术报告会的通知

报告1题目：Large dimensional empirical likelihood
报 告 人：周望 教授 （新加坡国立大学）
报告时间：2017年9月15日（星期五）上午 9:30--10:30

报告2题目：Efficient estimation of spot volatility with presence of infinite variation jumps
报 告 人：刘志 教授  （澳门大学）
报告时间：2017年9月15日（星期五）上午10:30-11:30
报告地点: 4号楼4318室

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                                                                      数学学院
                                                                  2017年09月11日
 
报告1摘要: In this talk, by adding two pseudo-observations to the original data set, I will talk about the asymptotic normality of the log empirical likelihood ratio statistic. In practice, I suggest to use the normalized F distribution to approximate the distribution of the log empirical likelihood ratio statistic. Simulation results show the excellent performance of this approximation.

报告2摘要: We propose a kernel estimator for the spot volatility of a semi-martingale at a given time point by using high frequency data, where the underlying process accommodates a jump part of infinite variation. The estimator is based on the representation of the characteristic function of Levy processes. The consistency of the proposed estimator is established under some mild assumptions. By assuming that the jump part of the underlying process behaves like a symmetric stable Levy process around 0, we establish the asymptotic normality of the proposed estimator. In particular, with a specific kernel function, the estimator is variance efficient. We conduct Monte Carlo simulation studies to assess our theoretical results and compare our estimator with existing ones.