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dc.contributor.advisorCairns, Professor Andrew
dc.contributor.advisorKleinow, Doctor Torsten
dc.contributor.authorChen, Liang
dc.date.accessioned2018-10-15T14:44:10Z
dc.date.available2018-10-15T14:44:10Z
dc.date.issued2017-06
dc.identifier.urihttp://hdl.handle.net/10399/3372
dc.description.abstractPension schemes are facing more difficulties on matching their underlying liabilities with assets, mainly due to faster mortality improvements for their underlying populations, better environments and medical treatments and historically low interest rates. Given most of the pension schemes are relatively much smaller than the national population, modelling and forecasting the small populations’ longevity risk become urgent tasks for both the industrial practitioners and academic researchers. This thesis starts with a systematic analysis on the influence of population size on the uncertainties of mortality estimates and forecasts with a stochastic mortality model, based on a parametric bootstrap methodology with England and Wales males as our benchmark population. The population size has significant effect on the uncertainty of mortality estimates and forecasts. The volatilities of small populations are over-estimated by the maximum likelihood estimators. A Bayesian model is developed to improve the estimation of the volatilities and the predictions of mortality rates for the small populations by employing the information of larger population with informative prior distributions. The new model is validated with the simulated small death scenarios. The Bayesian methodologies generate smoothed estimations for the mortality rates. Moreover, a methodology is introduced to use the information of large population for obtaining unbiased volatilities estimations given the underlying prior settings. At last, an empirical study is carried out based on the Scotland mortality dataset.en_US
dc.language.isoenen_US
dc.publisherHeriot-Watt Universityen_US
dc.publisherMathematical and Computer Sciencesen_US
dc.rightsAll items in ROS are protected by the Creative Commons copyright license (http://creativecommons.org/licenses/by-nc-nd/2.5/scotland/), with some rights reserved.
dc.subjectSmall population, age effect, period effect, cohort effect, bootstrap, parameter uncertainty, systematic parameter difference, likelihood ratio test, power of test, Bayesian inference, MCMC, informative prior, longevity risk, sampling variation, ARIMAen_US
dc.titleSmall population bias and sampling effects in stochastic mortality modellingen_US
dc.typeThesisen_US


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