A0720
Title: Change point estimation for Gaussian time series data with copula-based Markov chain models
Authors: Yu Kai Wang - Graduate Institute of Statistics of National Central University (Taiwan) [presenting]
Li-Hsien Sun - National Central University (Taiwan)
Lien Hsi Liu - Graduate Institute of Statistics of National Central University (Taiwan)
Takeshi Emura - Hiroshima University (Japan)
Chi-Yang Chiu - University of Tennessee Health Science Center (United States)
Abstract: The aim is to propose a method for change-point estimation, focusing on detecting structural shifts within time series data. Traditional maximum likelihood estimation (MLE) methods assume either independence or linear dependence via auto-regressive models. To address this limitation, copula-based Markov chain models are introduced, offering more flexible dependence modeling. These models treat a Gaussian time series as a Markov chain and utilize copula functions to handle serial dependence. The profile MLE procedure is then employed to estimate the changepoint and other model parameters, with the Newton-Raphson algorithm facilitating numerical calculations for the estimators. The proposed approach is evaluated through simulations and real stock return data, considering two distinct periods: the 2008 financial crisis and the COVID-19 pandemic in 2020.
