A segmented generalized Markov regime-switching model with its application in financial time series data. Issue 5 (23rd March 2020)

• Record Type: Journal Article
• Authors: Lin, Yufeng; Wu, Yuehua; Wang, Xiaogang; Ding, Hao
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 5(2020)
• Page Start: 839
• Page End: 853
• Publication Date: 2020-03-23
• Subjects: Change-point detection algorithm -- log-returns -- Markov process -- maximum likelihood estimation -- generalized Markov regime-switching model -- stock market index; 60J22 -- 62C12 -- 65C40; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1709972
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Movements of equity indices are very important information for an investment decision. Empirical studies illustrate that the movements switch among different regimes. The Markov regime-switching model has important applications to such analysis. However, parameters estimated under normality assumption might not be stable and the corresponding change-point detection algorithm might face some challenges when either the error distribution is heavy-tailed or observed data contain outliers. In this paper, we relax the normality assumption and propose a generalized Markov regime-switching (GMRS) model. We propose a GMRS model based change-point detection algorithm, which is tested on both simulation data and Hang Seng monthly index. Simulation studies show that this algorithm can improve the accuracy of identifying change-points when either the error distribution is heavy-tailed or observed data contain outliers. It is also evident that the identified change-points on Hang Seng monthly index data match the observed market behaviours.