报告题目：Exterior algorithm for change point detection and estimation of financial time series data

报告时间：2019年11月20日 9:30:00

报告地点： 旭日楼306教室

报告内容简介：An efficient exterior point algorithm is proposed for smoothing and change point detection of financial time series data under the penalized likelihood approach. The proposed method has O(n) computational complexity and is applicable to a broad class of time series model proposed in Bardet and Wintenberger (2009) that encompasses ARMA-GARCH as a special case. Under certain conditions, the estimated model has piecewise constant coefficients. Asymptotic properties of the penalized likelihood estimators are established. The possibility of real-time forecasting that update the prediction within O(1) time upon arrival of new signal is also discussed.

报告人简介：吴自添现为韩国全南大学统计系副教授，他的研究方向为时间序列分析和金融计量。曾在统计著名期刊《Quantitative Finance》，《Bernoulli》、《Statistica Sinica》，《Journal of Computational and Graphical Statistics》,《Statistics and Its Interface》, 《Journal of Multivariate Analysis》等数理统计和金融计量领域著名期刊发表论文数十篇，是《Journal of Forecasting》等国际著名期刊的副主编。