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Stochastic mortality dynamics driven by mixed fractional Brownian motion. (English) Zbl 1498.91374

Summary: Recently, the long-range dependence (LRD) of mortality dynamics has been identified and studied in the actuarial literature. The non-Markovian feature caused by LRD can raise new challenges in actuarial valuation and risk management. This paper proposes a new modeling approach that uses a combination of independent Brownian motion and fractional Brownian motion to achieve a flexible setting on capturing the LRD in mortality dynamics. The closed-form solutions of survival probabilities are derived for valuation and hedging purposes. To obtain mortality sensitivity measures in the presence of LRD, we develop a novel derivation method using directional derivatives. Our method is flexible in the sense that it can not only reflect the effect of LRD on mortality sensitivities, but also include some existing sensitivity measures as a special case. Finally, we provide a numerical illustration to analyze the performance of different sensitivity measures in a natural hedge of mortality risk.

MSC:

91G05 Actuarial mathematics
91D20 Mathematical geography and demography
60G22 Fractional processes, including fractional Brownian motion
Full Text: DOI

References:

[1] Abi Jaber, E.; Larsson, M.; Pulido, S., Affine Volterra processes, The Annals of Applied Probability, 29, 3155-3200 (2019) · Zbl 1441.60052
[2] Andrews, B.; Hopper, C., The Ricci Flow in Riemannian Geometry, Lecture Notes in Mathematics (2011), Springer · Zbl 1214.53002
[3] Baudoin, F.; Nualart, D., Equivalence of Volterra processes, Stochastic Processes and Their Applications, 107, 327-350 (2003) · Zbl 1075.60519
[4] Baudoin, F.; Nualart, D., Corrigendum to “Equivalence of Volterra processes” [Stochastic Process. Appl. 107 (2003) 327-350], Stochastic Processes and Their Applications, 4, 701-703 (2005) · Zbl 1075.60519
[5] Biffis, E., Affine processes for dynamic mortality and actuarial valuations, Insurance. Mathematics & Economics, 37, 443-468 (2005) · Zbl 1129.91024
[6] Blackburn, C.; Sherris, M., Consistent dynamic affine mortality models for longevity risk applications, Insurance. Mathematics & Economics, 53, 64-73 (2013) · Zbl 1284.91208
[7] Brouhns, N.; Denuit, M.; Vermunt, J. K., A Poisson log-bilinear regression approach to the construction of projected lifetables, Insurance. Mathematics & Economics, 31, 373-393 (2002) · Zbl 1074.62524
[8] Cairns, A. J.; Blake, D.; Dowd, K., A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration, The Journal of Risk and Insurance, 73, 687-718 (2006)
[9] Cairns, A. J.; Blake, D.; Dowd, K.; Coughlan, G. D.; Epstein, D.; Ong, A.; Balevich, I., A quantitative comparison of stochastic mortality models using data from England and Wales and the United States, North American Actuarial Journal, 13, 1-35 (2009) · Zbl 1484.91376
[10] Cairns, A. J.; Blake, D.; Dowd, K.; Coughlan, G. D.; Khalaf-Allah, M., Bayesian stochastic mortality modelling for two populations, ASTIN Bulletin, 41, 29-59 (2011)
[11] Cheridito, P., Mixed fractional Brownian motion, Bernoulli, 7, 913-934 (2001) · Zbl 1005.60053
[12] Cox, S. H.; Lin, Y., Natural hedging of life and annuity mortality risks, North American Actuarial Journal, 11, 1-15 (2007) · Zbl 1480.91196
[13] Currie, I. D., Smoothing constrained generalized linear models with an application to the Lee-Carter model, Statistical Modelling, 13, 69-93 (2013) · Zbl 07257450
[14] Dahl, M., Stochastic mortality in life insurance: market reserves and mortality-linked insurance contracts, Insurance. Mathematics & Economics, 35, 113-136 (2004) · Zbl 1075.62095
[15] De Rosa, C.; Luciano, E.; Regis, L., Basis risk in static versus dynamic longevity-risk hedging, Scandinavian Actuarial Journal, 2017, 343-365 (2017) · Zbl 1401.91129
[16] Delgado-Vences, F., Ornelas, A., 2019. Modelling Italian mortality rates with a geometric-type fractional Ornstein-Uhlenbeck process. ArXiv preprint.
[17] Es-Sebaiy, K.; Alazemi, F.; Al-Foraih, M., Least squares type estimation for discretely observed non-ergodic Gaussian Ornstein-Uhlenbeck processes, Acta Mathematica Scientia, 39, 989-1002 (2019) · Zbl 1499.62286
[18] Gilli, M.; Schumann, E., Calibrating the Heston model with differential evolution, (Applications of Evolutionary Computation (2010)), 242-250
[19] Gilli, M.; Schumann, E., Heuristic optimization in financial modelling, Annals of Operations Research, 1-30 (2011)
[20] Gompertz, B., On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies, Philosophical Transactions of the Royal Society of London, 513-583 (1825)
[21] Gripenberg, G.; Norros, I., On the prediction of fractional Brownian motions, Journal of Applied Probability, 33, 400-410 (1996) · Zbl 0861.60049
[22] Hu, Y.; Nualart, D.; Zhou, H., Parameter estimation for fractional Ornstein-Uhlenbeck processes of general Hurst parameter, Statistical Inference for Stochastic Processes, 22, 111-142 (2019) · Zbl 1419.62211
[23] Hu, Y. Z., Prediction and translation of fractional Brownian motions, (Stochastics in Finite and Infinite Dimensions (2001)), 153-171 · Zbl 0973.60059
[24] Hunt, A.; Villegas, A. M., Robustness and convergence in the Lee-Carter model with cohort effects, Insurance. Mathematics & Economics, 64, 186-202 (2015) · Zbl 1348.62241
[25] Jevtić, P.; Luca, R., A continuous-time stochastic model for the mortality surface of multiple populations, Insurance. Mathematics & Economics, 88, 181-195 (2019) · Zbl 1425.91226
[26] Jevtić, P.; Luciano, E.; Vigna, E., Mortality surface by means of continuous time cohort models, Insurance. Mathematics & Economics, 53, 122-133 (2013) · Zbl 1284.91240
[27] Jevtić, P.; Regis, L., Assessing the solvency of insurance portfolios via a continuous-time cohort model, Insurance. Mathematics & Economics, 61, 36-47 (2015) · Zbl 1314.91140
[28] Lee, R. D.; Carter, L. R., Modeling and forecasting US mortality, Journal of the American Statistical Association, 87, 659-671 (1992) · Zbl 1351.62186
[29] Li, N.; Lee, R., Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method, Demography, 42, 575-594 (2005)
[30] Luciano, E.; Regis, L.; Vigna, E., Delta-gamma hedging of mortality and interest rate risk, Insurance. Mathematics & Economics, 50, 402-412 (2012) · Zbl 1237.91134
[31] Luciano, E.; Regis, L.; Vigna, E., Single- and cross-generation natural hedging of longevity and financial risk, The Journal of Risk and Insurance, 84, 961-986 (2017)
[32] Luciano, E.; Vigna, E., Mortality risk via affine stochastic intensities: calibration and empirical relevance, Belgian Actuarial Bulletin, 8, 5-16 (2008) · Zbl 1398.91345
[33] MacDonald, A.; Cairns, A.; Gwilt, P.; Miller, K., An international comparison of recent trends in population mortality, British Actuarial Journal, 4, 3-141 (1998)
[34] Makeham, W. M., On the law of mortality and the construction of annuity tables, Journal of the Institute of Actuaries, 8, 301-310 (1860)
[35] Milevsky, M.; Promislow, D., Mortality derivatives and the option to annuitise, Insurance. Mathematics & Economics, 29, 299-318 (2001) · Zbl 1074.62530
[36] Nualart, D., The Malliavin Calculus and Related Topics (2006), Springer · Zbl 1099.60003
[37] Renshaw, A.; Haberman, S., Lee-Carter mortality forecasting with age-specific enhancement, Insurance. Mathematics & Economics, 33, 255-272 (2003) · Zbl 1103.91371
[38] Renshaw, A. E.; Haberman, S., A cohort-based extension to the Lee-Carter model for mortality reduction factors, Insurance. Mathematics & Economics, 38, 556-570 (2006) · Zbl 1168.91418
[39] Schrager, D. F., Affine stochastic mortality, Insurance. Mathematics & Economics, 38, 81-97 (2006) · Zbl 1103.60063
[40] Villegas, A. M.; Haberman, S.; Kaishev, V. K.; Millossovich, P., A comparative study of two-population models for the assessment of basis risk in longevity hedges, ASTIN Bulletin, 47, 631-679 (2017) · Zbl 1390.91215
[41] Wang, L.; Chiu, M. C.; Wong, H. Y., Volterra mortality model: actuarial valuation and risk management with long-range dependence, Insurance. Mathematics & Economics, 96, 1-14 (2021) · Zbl 1460.91240
[42] Wang, L.; Chiu, M. C.; Wong, H. Y., Time-consistent mean-variance reinsurance-investment problem with long-range dependent mortality rate, Scandinavian Actuarial Journal, 1-30 (2022)
[43] Wang, L.; Wong, H. Y., Time-consistent longevity hedging with long-range dependence, Insurance. Mathematics & Economics, 99, 25-41 (2021) · Zbl 1467.91154
[44] Wilmoth, J.; Horiuchi, S., Rectangularization revisited: variability of age at death within human populations, Demography, 36, 475-495 (1999)
[45] Wong, A.; Sherris, M.; Stevens, R., Natural hedging strategies for life insurers: impact of product design and risk measure, The Journal of Risk and Insurance, 84, 153-175 (2017)
[46] Yan, H.; Peters, G. W.; Chan, J. S., Multivariate long-memory cohort mortality models, ASTIN Bulletin, 1-41 (2019)
[47] Yan, H.; Peters, G. W.; Chan, J. S., Mortality models incorporating long memory for life table estimation: a comprehensive analysis, Annals of Actuarial Science, 1-38 (2021)
[48] Yaya, O. S.; Gil-Alana, L. A.; Amoateng, A. Y., Under-5 mortality rates in G7 countries: analysis of fractional persistence, structural breaks and nonlinear time trends, European Journal of Population, 35, 675-694 (2019)
[49] Zhou, K. Q.; Li, J. S.H., Dynamic longevity hedging in the presence of population basis risk: a feasibility analysis from technical and economic perspectives, The Journal of Risk and Insurance, 84, 417-437 (2017)
[50] Zhou, K. Q.; Li, J. S.H., Longevity Greeks: what do insurers and capital market investors need to know?, North American Actuarial Journal, 25, S66-S96 (2021) · Zbl 1465.91099
[51] Zhu, N.; Bauer, D., A cautionary note on natural hedging of longevity risk, North American Actuarial Journal, 18, 104-115 (2014) · Zbl 1412.91061
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