Break Detection Using AutoPARM and an Application on the Stock Market
Wing Chan , Master's Candidate

Department of Statistics, Colorado State University

Friday, November 2, 2007
2;00 p.m.
006 Statistics


This paper builds on the work of Dr. Richard Davis, Dr. Thomas Lee, and Dr. Gabriel Rodriguez-Yam on the algorithm of AutoPARM.  AutoPARM is an algorithm that uses a genetic algorithm to select the optimal number and locations of structural breaks in a the piecewise autoregressive (AR) process.  Using the algorithm as a method to detect break points in a non-stationary time series, this paper will apply the AutoPARM algorithm to the historical stock prices of a single company – in this case, Merck & Co. (MRK).  The objective of this paper will be to compare the outcomes of the AutoPARM procedure and real-life timing of the events that would influence the breakpoints of the time series model.




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