Prediction of mortality rates using a model with stochastic parameters

Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been appli...

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Main Authors: Tan Chon Sern, Pooi, Ah Hin *
Format: Article
Language:English
Published: AIP Publishing 2016
Subjects:
Online Access:http://eprints.sunway.edu.my/428/
http://eprints.sunway.edu.my/428/1/Pooi%20Ah%20Hin%205.pdf
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author Tan Chon Sern,
Pooi, Ah Hin *
author_facet Tan Chon Sern,
Pooi, Ah Hin *
author_sort Tan Chon Sern,
building SU Institutional Repository
collection Online Access
description Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths.
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spelling sunway-4282019-07-03T08:36:24Z http://eprints.sunway.edu.my/428/ Prediction of mortality rates using a model with stochastic parameters Tan Chon Sern, Pooi, Ah Hin * HA Statistics RA0421 Public health. Hygiene. Preventive Medicine Prediction of future mortality rates is crucial to insurance companies because they face longevity risks while providing retirement benefits to a population whose life expectancy is increasing. In the past literature, a time series model based on multivariate power-normal distribution has been applied on mortality data from the United States for the years 1933 till 2000 to forecast the future mortality rates for the years 2001 till 2010. In this paper, a more dynamic approach based on the multivariate time series will be proposed where the model uses stochastic parameters that vary with time. The resulting prediction intervals obtained using the model with stochastic parameters perform better because apart from having good ability in covering the observed future mortality rates, they also tend to have distinctly shorter interval lengths. AIP Publishing 2016 Article PeerReviewed text en http://eprints.sunway.edu.my/428/1/Pooi%20Ah%20Hin%205.pdf Tan Chon Sern, and Pooi, Ah Hin * (2016) Prediction of mortality rates using a model with stochastic parameters. AIP Conference Proceedings, 1782 (050016). 050016-1. ISSN 1551 7616 http://www.aip.scitation.org http://dx.doi.org/10.1063/1.4966106
spellingShingle HA Statistics
RA0421 Public health. Hygiene. Preventive Medicine
Tan Chon Sern,
Pooi, Ah Hin *
Prediction of mortality rates using a model with stochastic parameters
title Prediction of mortality rates using a model with stochastic parameters
title_full Prediction of mortality rates using a model with stochastic parameters
title_fullStr Prediction of mortality rates using a model with stochastic parameters
title_full_unstemmed Prediction of mortality rates using a model with stochastic parameters
title_short Prediction of mortality rates using a model with stochastic parameters
title_sort prediction of mortality rates using a model with stochastic parameters
topic HA Statistics
RA0421 Public health. Hygiene. Preventive Medicine
url http://eprints.sunway.edu.my/428/
http://eprints.sunway.edu.my/428/
http://eprints.sunway.edu.my/428/
http://eprints.sunway.edu.my/428/1/Pooi%20Ah%20Hin%205.pdf