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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月18日

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  • Wanying Fu,
  • Sean Droms,
  • Patrick Brewer,
  • Barry R. Smith

摘要

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.