Modeling non-linearity in mortality data: application to longevity bond pricing
| dc.contributor.author | Li, Huijing | |
| dc.contributor.examiningcommittee | Pai, Jeffrey (Warren Centre for Actuarial Studies and Research) Hao, Xuemiao (Warren Centre for Actuarial Studies and Research) Thavaneswaran, Aerambamoorthy (Statistics) | en_US |
| dc.contributor.supervisor | Zhou, Rui (Warren Centre for Actuarial Studies and Research) | en_US |
| dc.date.accessioned | 2015-09-18T15:25:54Z | |
| dc.date.available | 2015-09-18T15:25:54Z | |
| dc.date.issued | 2015 | |
| dc.degree.discipline | Management | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Human mortality has been improving faster than expected over the past few decades. This unprecedented improvement has caused significant financial stress to pension plan sponsors and annuity providers. To better model and forecast mortality rates, we examine the nonlinearity in mortality data from England and Wales with a sample period of 1900-2011. More specifically, we consider four nonlinear time series models: threshold autoregressive model, Markov regime switching model, structural change model, and auto-regressive conditional heteroskedasticity model. We then compare their goodness of fit and forecasting performance. Finally, we study the impact of different nonlinear models on longevity bond pricing. | en_US |
| dc.description.note | October 2015 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/30834 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Mortality Improvement, Non-linearity, Longevity bond pricing | en_US |
| dc.title | Modeling non-linearity in mortality data: application to longevity bond pricing | en_US |
| dc.type | master thesis | en_US |