报 告 人： 孔新兵 副教授
复旦大学
报告题目：Testing for pure-jump processes using high-frequency data
报告时间：
报告地点：静远楼1506学术报告厅
主办单位：太阳成集团、科技处
报告摘要：Abstract: Pure-jump processes have been increasingly popular in modeling the high-frequency financial data, due partially to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as rather stringent conditions and slow rates of convergence. In this paper, we propose a different test, based on the realized characteristic function, to check whether the underlying process of a high frequency data can be modeled by a pure-jump process. It turns out that the proposed test has a much faster convergence rate of order $O(n^{1/2})$ (n is the sample size) versus the usually $o(n^{1/4})$ available for existing tests; it is applicable much more generally than previous tests, e.g., it is robust to jumps of infinite variation and flexible modeling of the diffusion component. Simulation studies justify our findings and the test is also applied to some real high-frequency financial data.
孔新兵副教授简介：
复旦大学管理学院统计系副教授，曾被评为2012年度复旦管院学术新星（唯一获得者）。现担任SCI 杂志Journal of Business and Economic Statistics, Quantitive Finance, Journal of Non-parametric Statistics, Science in China 匿名审稿人。研究兴趣为金融统计、高维数据分析、多重检验等。在统计学和计量经济学杂志发表论文多篇，其中包括顶级杂志Journal of the American Statistical Association, Annals of Statistics, Journal of Econometrics， 以及权威杂志 PLoS ONE, Test, Scandinavion Journal of Statistic。