Selective Linear Segmentation for Detecting Relevant Parameter Changes

Arnaud Dufays, Elysee Aristide Houndetoungan, Alain Coën

Résultats de recherche: Contribution à un journal/une revueArticleRevue par des pairs

Résumé

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.

langue originaleAnglais
Pages (de - à)762-805
Nombre de pages44
journalJournal of Financial Econometrics
Volume20
Numéro de publication4
Les DOIs
Etat de la publicationPublié - 2022

Empreinte digitale

Examiner les sujets de recherche de « Selective Linear Segmentation for Detecting Relevant Parameter Changes ». Ensemble, ils forment une empreinte digitale unique.

Contient cette citation