Longevity Risk Management and the Development of a Life Annuity Market in Australia

Author(s):  
Michael Sherris ◽  
John R. Evans
2016 ◽  
Vol 47 (1) ◽  
pp. 43-77 ◽  
Author(s):  
Craig Blackburn ◽  
Katja Hanewald ◽  
Annamaria Olivieri ◽  
Michael Sherris

AbstractThe life annuity business is heavily exposed to longevity risk. Risk transfer solutions are not yet fully developed, and when available they are expensive. A significant part of the risk must therefore be retained by the life insurer. So far, most of the research work on longevity risk has been mainly concerned with capital requirements and specific risk transfer solutions. However, the impact of longevity risk on shareholder value also deserves attention. While it is commonly accepted that a market-consistent valuation should be performed in this respect, the definition of a fair shareholder value for a life insurance business is not trivial. In this paper, we develop a multi-period market-consistent shareholder value model for a life annuity business. The model allows for systematic and idiosyncratic longevity risk and includes the most significant variables affecting shareholder value: the cost of capital (which in a market-consistent setting must be quantified in terms of frictional and agency costs, net of the value of the limited liability put option), policyholder demand elasticity and the cost of alternative longevity risk management solutions, namely indemnity-based and index-based solutions. We show how the model can be used for assessing the impact of different longevity risk management strategies on life insurer shareholder value and solvency.


2014 ◽  
Author(s):  
Hong Li ◽  
Anja De Waegenaere ◽  
Bertrand Melenberg

Author(s):  
Walter Onchere ◽  
Richard Tinega ◽  
Patrick Weke ◽  
Jam Otieno

Aims: As shown in literature, several authors have adopted various individual frailty mixing distributions as a way of dealing with possible heterogeneity due to unobserved covariates in a group of insurers. This research contribution is to generalize the frailty mixing distribution to nest other classes of frailty distributions not in literature and apply the proposed distributions in valuation of life annuity business. Methodology: A simulation study is done to assess the performance of the aforementioned models. The baseline parameters is estimated using Bayesian Inference and a better model is suggested for valuation of life annuity business. Results: As a result of generalizing the frailty some new classes of frailty distributions are constructed such as; the Reciprocal Inverse Gaussian Frailty, the Inverse Gamma Frailty, the Harmonic Frailty and the Positive Hyperbolic Frailty. From the simulation study, the proposed new frailty models shows that ignoring frailty leads to an underestimation of future residual lifetime since the survival curve shifts to the right when heterogeneity is accounted for. This is consistent with frailty literature. The Reciprocal Inverse Gaussian model closely represents the Association of Kenya Insurers graduated rates with a slight increase in survival due to longevity risk. Conclusion: The proposed new frailty models show an increase in the insurers expected liability when unobserved heterogeneity is accounted for. This is consistent with frailty literature and thus can be applied to avoid underestimating the insurer’s liability in the context of life annuity business. The RIG model as proposed in estimating future liability by directly adjusting the AKI mortality rates shows an increase in longevity risk. The extent of heterogeneity of the insured group determines the level of risk. The RIG frailties should be considered for multivariate cases where the insureds are clustered in groups.


2018 ◽  
Vol 5 (3) ◽  
pp. 73-92
Author(s):  
Koray D. Simsek ◽  
Min Jeong Kim ◽  
Woo Chang Kim ◽  
John M. Mulvey

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 31
Author(s):  
Pauline Milaure Ngugnie Diffouo ◽  
Pierre Devolder

This paper captures and measures the longevity risk generated by an annuity product. The longevity risk is materialized by the uncertain level of the future liability compared to the initially foretasted or expected value. Herein we compute the solvency capital (SC) of an insurer selling such a product within a single risk setting for three different life annuity products. Within the Solvency II framework, we capture the mortality of policyholders by the mean of the Hull–White model. Using the numerical analysis, we identify the product that requires the most SC from an insurer and the most profitable product for a shareholder. For policyholders we identify the cheapest product by computing the premiums and the most profitable product by computing the benefit levels. We further study how sensitive the SC is with respect to some significant parameters.


2020 ◽  
Vol 2020 (7) ◽  
pp. 650-676
Author(s):  
Michael Sherris ◽  
Yajing Xu ◽  
Jonathan Ziveyi

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ming Zhao ◽  
Ziwen Li ◽  
Yinge Cai ◽  
Weiting Li

This paper constructs a model to measure longevity risk and explains the reasons for restricting the supply of annuity products in life insurance companies. According to the Lee–Carter Model and the VaR-based stochastic simulation, it can be found that the risk margin of the first type of longevity risk for ignoring the improvement of mortality rate is about 7%, and the risk margin of the second type of longevity risk for underestimating mortality improvement is about 7%. Therefore, the insurer needs to use cohort life table pricing premium and gradually prepares longevity risk capital during the insurance period.


2014 ◽  
Vol 56 ◽  
pp. 14-27 ◽  
Author(s):  
Catherine Donnelly ◽  
Montserrat Guillén ◽  
Jens Perch Nielsen

Sign in / Sign up

Export Citation Format

Share Document