Managing longevity risk

Hong Li

Research output: ThesisDoctoral ThesisScientific

694 Downloads (Pure)

Abstract

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.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Tilburg University
Supervisors/Advisors
  • De Waegenaere, Anja, Promotor
  • Melenberg, Bertrand, Promotor
Award date23 Jun 2015
Place of PublicationTilburg
Publisher
Print ISBNs9789056684426
Publication statusPublished - 2015

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Longevity risk
Objective function
Derivatives
Sample size
Mortality forecasting
Benchmark
Conditional value at risk
Hedging
Hedge
Mortality
Mortality rate
Extrapolation
Bayesian learning
Dynamic hedging
Assets
Minimum variance
Mean-variance
Hedging strategies
Probability distribution
Posterior distribution

Cite this

Li, H. (2015). Managing longevity risk. Tilburg: CentER, Center for Economic Research.
Li, Hong. / Managing longevity risk. Tilburg : CentER, Center for Economic Research, 2015. 131 p.
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Li, H 2015, 'Managing longevity risk', Doctor of Philosophy, Tilburg University, Tilburg.

Managing longevity risk. / Li, Hong.

Tilburg : CentER, Center for Economic Research, 2015. 131 p.

Research output: ThesisDoctoral ThesisScientific

TY - THES

T1 - Managing longevity risk

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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 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.

AB - 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.

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T3 - CentER Dissertation Series

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Li H. Managing longevity risk. Tilburg: CentER, Center for Economic Research, 2015. 131 p. (CentER Dissertation Series).