TY - JOUR
T1 - A continuous-time stochastic model for the mortality surface of multiple populations
AU - Jevtić, P.
AU - Regis, Luca
N1 - Funding Information:
The Authors would like to thank conference participants at the 19th IME Conference and SIMC 2015 conference as well as seminar participants at Waterloo University, Concordia University, HEC Lausanne and McMaster University for helpful comments. Luca Regis gratefully acknowledges financial support from the Crisis Lab project funded by the Italian Ministry of Education. ☆ The Authors would like to thank conference participants at the 19th IME Conference and SIMC 2015 conference as well as seminar participants at Waterloo University, Concordia University, HEC Lausanne and McMaster University for helpful comments. Luca Regis gratefully acknowledges financial support from the Crisis Lab project funded by the Italian Ministry of Education.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/9
Y1 - 2019/9
N2 - We formulate, study and calibrate a continuous-time model for the joint evolution of the mortality surface of multiple populations. We model the mortality intensity by age and population as a mixture of stochastic latent factors, that can be either population-specific or common to all populations. These factors are described by affine time-(in)homogeneous stochastic processes. Traditional, deterministic mortality laws can be extended to multi-population stochastic counterparts within our framework. We detail the calibration procedure when factors are Gaussian, using centralized data-fusion Kalman filter. We provide an application based on the joint mortality of UK and Dutch males and females. Although parsimonious, the specification we calibrate provides a good fit of the observed mortality surface (ages 0–89) of both sexes and populations between 1960 and 2013.
AB - We formulate, study and calibrate a continuous-time model for the joint evolution of the mortality surface of multiple populations. We model the mortality intensity by age and population as a mixture of stochastic latent factors, that can be either population-specific or common to all populations. These factors are described by affine time-(in)homogeneous stochastic processes. Traditional, deterministic mortality laws can be extended to multi-population stochastic counterparts within our framework. We detail the calibration procedure when factors are Gaussian, using centralized data-fusion Kalman filter. We provide an application based on the joint mortality of UK and Dutch males and females. Although parsimonious, the specification we calibrate provides a good fit of the observed mortality surface (ages 0–89) of both sexes and populations between 1960 and 2013.
KW - Centralized data fusion
KW - Continuous-time stochastic mortality
KW - Kalman filter estimation
KW - Mortality surface
KW - Multi-population mortality
UR - http://www.scopus.com/inward/record.url?scp=85069730776&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85069730776&partnerID=8YFLogxK
U2 - 10.1016/j.insmatheco.2019.07.001
DO - 10.1016/j.insmatheco.2019.07.001
M3 - Article
AN - SCOPUS:85069730776
SN - 0167-6687
VL - 88
SP - 181
EP - 195
JO - Insurance: Mathematics and Economics
JF - Insurance: Mathematics and Economics
ER -