報告題目：Exterior algorithm for change point detection and estimation of financial time series data

報告時間：2019年11月20日 9:30:00

報告地點： 旭日樓306教室

報告内容簡介：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.

報告人簡介：吳自添現為韓國全南大學統計系副教授，他的研究方向為時間序列分析和金融計量。曾在統計著名期刊《Quantitative Finance》，《Bernoulli》、《Statistica Sinica》，《Journal of Computational and Graphical Statistics》,《Statistics and Its Interface》, 《Journal of Multivariate Analysis》等數理統計和金融計量領域著名期刊發表論文數十篇，是《Journal of Forecasting》等國際著名期刊的副主編。