报告时间：2017年10月25日（星期三）15:30-16:15
报告地点：翡翠湖校区科教B楼1208会议室
报 告 人：吴月华教授
工作单位：加拿大York大学统计与数学系
举办单位：经济学院
报告简介：
Change-point problems can be found in many areas including financial econometrics. To detect all the change-points in a data sequence before the modelling is of great importance. A statistical analysis without considering their existence may lead to an incorrect or improper conclusion. We will present some numerical examples to illustrate a change-point problem, and show the importance to include a change-point in data modelling.
A Markov regime-switching log-normal (RSLN) model has been used to capture the time-varying features of stock return and volatility. The model displays a better ability to depict a fat tail distribution as compared with using a log-normal model, which means that the RSLN model can describe observed market behaviour better. However, the analysis of the behaviour of calibrated regime-switching parameters over different lengths of time intervals reveals the existence of change-points. An algorithm is thus presented for identifying the change-points in the series corresponding to the times when there are changes in parameter estimates. This algorithm for identifying change-points is tested on the Standard and Poor’s 500 monthly index data from 1971 to 2008，and Nikkei 225 monthly index data from 1984 to 2008 . It is evident that the change -point we identify match the big events observed in the US stock market and the Japan stock market (e.g., the October 1987 stock market crash), and that the segmentations of stock index series, which are defined as the periods between change-points, match the observed bear–bull market phases. Further development will also be discussed.
