Bayesian multiple change-point detection‎: ‎A methodological comparison of exact dynamic programming and nonparametric hidden Markov approaches

Document Type : Original Scientific Paper

Author

Department of Statistics‎, ‎Faculty of Mathematical Sciences‎, ‎Shahid Beheshti University‎, ‎Tehran‎, ‎Iran

10.22034/jsmta.2026.23821.1200

Abstract

Detecting structural breaks is crucial for understanding time series dynamics‎, ‎particularly in highly volatile financial markets‎. ‎This study systematically compares two prominent Bayesian change-point methodologies‎: ‎an exact dynamic programming approach that eliminates Markov chain Monte Carlo sampling‎, ‎and a Dirichlet process hidden Markov model that flexibly infers latent regime counts and persistence‎. ‎Analyzing daily S&P 500 returns spanning 2000 to 2024-a period encompassing major upheavals like the global financial crisis and the COVID-19 pandemic-we evaluate both models across detection accuracy‎, ‎parameter estimation‎, ‎computational efficiency‎, ‎and predictive performance‎. ‎Despite fundamentally different assumptions‎, ‎the models exhibit striking concordance‎, ‎identifying major macroeconomic shifts with timing differences of mere days and parameter variations of under 0.01%‎. ‎However‎, ‎a clear trade-off emerges‎: ‎the exact method is approximately 90 times faster‎, ‎whereas the Dirichlet process hidden Markov model provides marginally superior predictive accuracy‎. ‎These insights offer actionable guidelines for selecting the optimal framework based on computational and predictive priorities.

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References

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Journal of Statistical Modelling: Theory and Applications
Volume 7, Issue 1
January 2026
Pages 1-19

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History

  • Receive Date: 16 October 2025
  • Revise Date: 04 August 2026
  • Accept Date: 08 September 2026

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