Selective Linear Segmentation for Detecting Relevant Parameter Changes*

Risks and Portfolio Decisions Involving Hedge Funds

Arnaud Dufays, Aristide Houndetoungan and Alain Coën

Journal of Financial Econometrics, 2022, vol. 20, issue 4, 762-805

Abstract: Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP 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 fourteen hedge fund (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; Hedge funds; model selection; structural change; time-varying parameter (search for similar items in EconPapers)
JEL-codes: C11 C12 C22 C32 C52 C53 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Working Paper: Selective linear segmentation for detecting relevant parameter changes (2024)
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