Abstract
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Award date  23 Jun 2015 
Place of Publication  Tilburg 
Publisher  
Print ISBNs  9789056684426 
Publication status  Published  2015 
Fingerprint
Cite this
}
Managing longevity risk. / Li, Hong.
Tilburg : CentER, Center for Economic Research, 2015. 131 p.Research output: Thesis › Doctoral Thesis
TY  THES
T1  Managing longevity risk
AU  Li, Hong
PY  2015
Y1  2015
N2  The thesis first examines the choice of sample size for mortality forecasting, and then deal with the hedging of longevity risk using longevitylinked 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. Meanvariance and meanconditionalvalueatrisk objective functions are used. Chapter 4 focuses on the dynamic hedging of longevity risk in the case where the trading frequency of the longevitylinked derivatives is limited. A minimumvariance objective function is used, and timeconsistent hedging strategies are derived in both the benchmark case, where all assets can be traded continuously, and a constrained case, where the longevitylinked derivatives can only be traded at a low and deterministic frequency.
AB  The thesis first examines the choice of sample size for mortality forecasting, and then deal with the hedging of longevity risk using longevitylinked 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. Meanvariance and meanconditionalvalueatrisk objective functions are used. Chapter 4 focuses on the dynamic hedging of longevity risk in the case where the trading frequency of the longevitylinked derivatives is limited. A minimumvariance objective function is used, and timeconsistent hedging strategies are derived in both the benchmark case, where all assets can be traded continuously, and a constrained case, where the longevitylinked derivatives can only be traded at a low and deterministic frequency.
M3  Doctoral Thesis
SN  9789056684426
T3  CentER Dissertation Series
PB  CentER, Center for Economic Research
CY  Tilburg
ER 