Analyzing MSCI global healthcare return and volatility with structural change based on residual CUSUM GARCH approach

• Main Authors: Nantiworn Thianpaen, Songsak Sriboonchitta
• Format: Book Series
• Published: 2018
• Subjects: Computer Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952682939&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/55586
• Institution: Chiang Mai University

© Springer International Publishing Switzerland 2016. This study aims to analyze the Morgan Stanley Capital International (MSCI) world return and volatility of the healthcare price index using daily time series data. Since the data of MSCI healthcare returns cannot be described by linear models, the residual CUSUM GARCH(1,1) model is applied in this paper. The CUSUM test is used to estimate the optimal change point. The findings of this paper are (1) the estimated point is at day 1,201 of the entire daily data set of 4,209 observations; (2) if the change point is not taken into consideration, the estimated parameters of GARCH(1,1) become (Formula presented.), i.e., we encounter the “IGARCH effect”, which leads to an infinite variance for a model. The contribution of this paper is the recommendation for the analysis of the change point as the necessary condition, rather than jumping into using the whole data set to estimate all parameters of the model without testing nonlinearity, especially for financial time series data.