A0358
Title: Testing stability in event observations with applications to IPO Performance
Authors: Lajos Horvath - University of Utah (United States)
Zhenya Liu - Renmin University of China (China)
Gregory Rice - University of Waterloo (Canada)
Shixuan Wang - University of Reading (United Kingdom) [presenting]
Yaosong Zhan - Renmin University of China (China)
Abstract: Many sequentially observed functional data objects are observable only at the times of certain events. For example, the trajectory of stock prices of companies after their initial public offering (IPO) can be observed when the offering occurs, and the resulting data may be affected by changing circumstances. It is of interest to investigate whether the mean behaviour of such functions is stable over time, and if not to estimate the times at which apparent changes occur. Since the frequency of events fluctuates each day, we propose a change point analysis that is comprised of two steps. In the first step, we segment the series into segments in which the frequency of events is approximately homogeneous using a new binary segmentation procedure for event frequencies. After adjusting the observed curves in each segment based on the frequency of events, we proceed in the second step by developing a method to test for and estimate change points in the mean of the observed functional data objects. We establish the consistency and asymptotic distribution of the change point detector and estimator in both steps, and study their performance using Monte Carlo simulations. An application to IPO performance data illustrates the proposed methods.
