Arnaud Dufays, Aristide Houndetoungan, Alain Coën
arXiv 8 Feb 2024 · Econometrics
arXiv:2402.05329 · PDF · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Yau, C. Y. and Z. Zhao (2016) Inference for multiple change points in time series via likelihood ratio scan statistics | 1.000 | 12 | 5 | 100% |
| 2 | Bai, J. and P. Perron (1998) Estimating and testing linear models with multiple structural breaks | 1.000 | 5 | 3 | 100% |
| 3 | Meligkotsidou, L. and I. D. Vrontos (2008) Detecting structural breaks and identifying risk factors in hedge fund returns: A Bayesian approach | 0.874 | 11 | 2 | 100% |
| 4 | Dicker, L., B. Huang, and X. Lin (2013) Variable selection and estimation with the seamless-L 0 penalty | 0.874 | 6 | 2 | 100% |
| 5 | Bai, J. and P. Perron (2003) Computation and analysis of multiple structural change models | 0.811 | 4 | 2 | 100% |
| 6 | Eo, Y (2016) Structural changes in inflation dynamics: multiple breaks at different dates for different parameters | 0.737 | 5 | 3 | 40% |
| 7 | Huber, F., G. Kastner, and M. Feldkircher (2019) Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models | 0.737 | 5 | 3 | 40% |
| 8 | Fernandez, C., E. Ley, and M. F. Steel (2001) Benchmark priors for Bayesian model averaging | 0.737 | 4 | 2 | 75% |
| 9 | Chan, N. H., C. Y. Yau, and R.-M. Zhang (2014) Group LASSO for structural break time series | 0.737 | 3 | 2 | 100% |
| 10 | Fung, W. and D. Hsieh (2001) The risk in hedge fund strategies: theory and evidence from trend followers | 0.737 | 3 | 2 | 100% |
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