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Pricing life insurance using bivariate temperature-mortality seasonal hidden Markov models

Author

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  • Kouton, Samuel Darwin Dona
  • Barigou, Karim

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Nguyen, Thai

Abstract

We study the dependence between weekly mortality and temperature through a non-homogeneous Seasonal Hidden Markov Model (SHMM) with trend. The framework jointly models mortality and temperature while allowing for regime switches, explicit seasonality, smooth longterm trends, and time-varying transition probabilities in latent dynamics. We show that the general identifiability and strong consistency results of Touron (2018) apply to our Poissonand Gaussian-emission specifications. We estimate the model on weekly Italian mortality and temperature data using two- and three-state specifications. Empirically, the SHMM improves fit relative to non-switching benchmarks and yields an interpretable state-dependent characterization of the temperature–mortality relationship. We then develop a change-of-measure framework for pricing mortality-linked insurance contracts that incorporates temperature-related mortality risk and latent regime uncertainty.

Suggested Citation

  • Kouton, Samuel Darwin Dona & Barigou, Karim & Nguyen, Thai, 2026. "Pricing life insurance using bivariate temperature-mortality seasonal hidden Markov models," LIDAM Discussion Papers ISBA 2026019, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2026019
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