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Applying Markov-switching Bayesian vector autoregression to an age-partitioned Lee–Carter mortality model

Wanying Fu et al.

European Actuarial Journal2025https://doi.org/10.1007/s13385-025-00415-warticle
AJG 2ABDC B
Weight
0.41

What the paper says

Forecast accuracy and measures of uncertainty are important in mortality modeling, for instance in risk management and pricing of financial products. In this paper, we introduce a new mortality model that provides excellent mortality forecasts and accurate estimation of mortality risk; these merits persist as we extend the forecasting horizon to 30 years. We forecast with Markov-switching Bayesian vector autoregression (MSBVAR) and believe that this is the first time MSBVAR has been used in Lee–Carter-based mortality modeling. Our strategy begins by partitioning the full lifespan into age subgroups that experience different mortality dynamics. Applying Lee–Carter within each age subgroup, we generate a separate stochastic time factor for each. Then, we forecast mortality using a method that can capture and quantify both permanent and recurring structural changes to mortality. The recurring changes are modeled with MSBVAR, which also considers parameter correlation. The forecasting process provides parameter uncertainty estimates and mortality uncertainty estimates.

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https://doi.org/https://doi.org/10.1007/s13385-025-00415-w

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@article{wanying2025,
  title        = {{Applying Markov-switching Bayesian vector autoregression to an age-partitioned Lee–Carter mortality model}},
  author       = {Wanying Fu et al.},
  journal      = {European Actuarial Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s13385-025-00415-w},
}

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Applying Markov-switching Bayesian vector autoregression to an age-partitioned Lee–Carter mortality model

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.