Selective Linear Segmentation For Detecting Relevant Parameter Changes
Arnaud Dufays, Elysee Aristide Houndetoungan, Alain Coën, Selective Linear Segmentation for Detecting Relevant Parameter Change s, Journal of Financial Econometrics, Volume 20, Issue 4, Fall 2022, Pages 762–805
68 Pages Posted: 10 Oct 2019 Last revised: 15 May 2023
Date Written: August 20, 2019
Change-point processes are one flexible approach to model long time series. We propose a method to uncover which model parameter truly vary when a change-point is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of 14 Hedge funds (HF) strategies, using an asset based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.
Keywords: change-point, structural change, time-varying parameter, model selection, Hedge funds
JEL Classification: C11, C12, C22, C32, C52, C53
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