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From regulatory life tables to stochastic mortality projections: The exponential decline model
Institution:1. Institute of Statistics, Biostatistics and Actuarial Science, Université Catholique de Louvain (UCL), Louvain-la-Neuve, Belgium;2. Department of Mathematics, Université Libre de Bruxelles (ULB), Bruxelles, Belgium;1. Department of Quantitative Economics, Maastricht University, P.O. Box 616, NL-6200 MD Maastricht, Netherlands;2. Department of Economics Lund University, P.O. Box 7082, SE-22007, Lund, Sweden;3. Department of Quantitative Economics, Maastricht University, P.O. Box 616, NL-6200 MD Maastricht, Netherlands;1. Actuarial Research Group, Faculty of Economics and Business, KU Leuven, Belgium;2. Faculty of Law, Faculty of Business and Economics, KU Leuven, Belgium;3. Institut de Statistique, Biostatistique et Sciences Actuarielles, UC Louvain, Belgium;4. Department of Mathematics, Université Libre de Bruxelles, Belgium;1. Department of Mathematics and Statistics, University of Strathclyde, Glasgow G1 1XH, UK;2. School of Economics and Management, Fuzhou University, China;3. Department of Mathematical Sciences, Loughborough University, Loughborough, LE11 3TU, UK;4. Department of Applied Mathematics, Donghua University, Shanghai 201620, China;5. Department of Information and Communication, Nanjing University of Information Science and Technology, Nanjing, 210044, China;1. ARC Centre of Excellence in Population Ageing Research (CEPAR), University of New South Wales, Sydney, NSW, 2052, Australia;2. Department of Econometrics and Business Statistics, Monash University, Melbourne, VIC, 3800, Australia
Abstract:Often in actuarial practice, mortality projections are obtained by letting age-specific death rates decline exponentially at their own rate. Many life tables used for annuity pricing are built in this way. The present paper adopts this point of view and proposes a simple and powerful mortality projection model in line with this elementary approach, based on the recently studied mortality improvement rates. Two main applications are considered. First, as most reference life tables produced by regulators are deterministic by nature, they can be made stochastic by superposing random departures from the assumed age-specific trend, with a volatility calibrated on market or portfolio data. This allows the actuary to account for the systematic longevity risk in solvency calculations. Second, the model can be fitted to historical data and used to produce longevity forecasts. A number of conservative and tractable approximations are derived to provide the actuary with reasonably accurate approximations for various relevant quantities, available at limited computational cost. Besides applications to stochastic mortality projection models, we also derive useful properties involving supermodular, directionally convex and stop-loss orders.
Keywords:Life tables  Risk measures  Longevity risk  Comonotonicity  Life annuity  Supermodular order  Directionally convex order  Increasing convex order
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