The thesis first examines the choice of sample size for mortality forecasting, and then deal with the hedging of longevity risk using longevity-linked instruments. Chapter 2 proposes a Bayesian learning approach to determine the (posterior distribution of) the sample sizes for mortality forecasting using mortality models based on linear extrapolation approaches. Chapter 3 studies the static robust management of longevity risk in the situation that the hedger does not have precise knowledge of the underlying probability distribution of the future mortality rates. Mean-variance and mean-conditional-value-at-risk objective functions are used. Chapter 4 focuses on the dynamic hedging of longevity risk in the case where the trading frequency of the longevity-linked derivatives is limited. A minimum-variance objective function is used, and time-consistent hedging strategies are derived in both the benchmark case, where all assets can be traded continuously, and a constrained case, where the longevity-linked derivatives can only be traded at a low and deterministic frequency.
|Qualification||Doctor of Philosophy|
|Award date||23 Jun 2015|
|Place of Publication||Tilburg|
|Publication status||Published - 2015|