A1400
Title: Multiple change-point inference for locally stationary time series with applications to financial data
Authors: Wai Leong Ng - The Hang Seng University of Hong Kong (Hong Kong) [presenting]
Abstract: Change-point detection and locally stationary time series modelling are two major approaches for the analysis of non-stationary data. The former aims to identify stationary phases by detecting abrupt changes in the dynamics of a time series model, while the latter employs (locally) time-varying models to describe smooth changes in dependence structure of a time series. However, in some applications, abrupt and smooth changes can co-exist, and neither of the two approaches alone can model the data adequately. A novel likelihood-based procedure is proposed for the inference of multiple change-points in locally stationary time series. In contrast to traditional change-point analysis where an abrupt change occurs in a real-valued parameter, a change in locally stationary time series occurs in a parameter curve and can be classified as a jump or a kink depending on whether the curve is discontinuous or not. It is shown that the proposed method can consistently estimate the number, locations, and the types of change-points. Two different asymptotic distributions corresponding respectively to jump and kink estimators are also established. Extensive simulation studies and a real data application to financial time series are provided.
