Selective linear segmentation for detecting relevant parameter changes

Arnaud Dufays, Aristide Houndetoungan and Alain Co\"en

Papers from arXiv.org

Abstract: 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.

Date: 2024-02
New Economics Papers: this item is included in nep-ecm and nep-ets
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http://arxiv.org/pdf/2402.05329 Latest version (application/pdf)

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Journal Article: Selective Linear Segmentation for Detecting Relevant Parameter Changes* (2022)
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