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

Samuel Darwin Dona Kouton, Karim Barigou () and Thai Nguyen
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Karim Barigou: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2026019, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

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.

Keywords: Life insurance; mortality modeling; regime-switching; seasonality; temperature; climate risk (search for similar items in EconPapers)
Pages: 26
Date: 2026-05-29
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvad:2026019

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