Solvency Capital, Pricing and Capitalization Strategies of Life Annuity Providers

2012 ◽  
Author(s):  
Maathumai Nirmalendran ◽  
Michael Sherris ◽  
Katja Hanewald
Keyword(s):  
Life Annuity ◽  
Solvency Capital

scholarly journals Longevity Risk Measurement of Life Annuity Products

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 31
Author(s):  
Pauline Milaure Ngugnie Diffouo ◽  
Pierre Devolder
Keyword(s):  
Numerical Analysis ◽  
Solvency Ii ◽  
Expected Value ◽  
Risk Measurement ◽  
Longevity Risk ◽  
Life Annuity ◽  
The Future ◽  
Solvency Capital ◽  
The Mean

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.

Multi-year analysis of solvency capital in life insurance

2021 ◽  
Author(s):  
Karen Tanja Rödel ◽  
Stefan Graf ◽  
Alexander Kling
Keyword(s):  
Life Insurance ◽  
Solvency Capital

Life Expectancy in Individuals With Type 2 Diabetes: Implications for Annuities

2009 ◽  
Vol 30 (3) ◽  
pp. 409-414 ◽  
Author(s):  
Hermione C. Price ◽  
Philip M. Clarke ◽  
Alastair M. Gray ◽  
Rury R. Holman
Keyword(s):  
Type 2 Diabetes ◽  
Life Expectancy ◽  
Cholesterol Level ◽  
Total Cholesterol Level ◽  
Insurance Companies ◽  
Age At Death ◽  
Life Annuity ◽  
Uk Government ◽  
The Uk

Background. Insurance companies often offer people with diabetes ‘‘enhanced impaired life annuity’’ at preferential rates, in view of their reduced life expectancy. Objective. To assess the appropriateness of ‘‘enhanced impaired life annuity’’ rates for individuals with type 2 diabetes. Patients. There were 4026 subjects with established type 2 diabetes (but not known cardiovascular or other life-threatening diseases) enrolled into the UK Lipids in Diabetes Study. Measurements. Estimated individual life expectancy using the United Kingdom Prospective Diabetes Study (UKPDS) Outcomes Model. Results. Subjects were a mean (SD) age of 60.7 (8.6) years, had a blood pressure of 141/83 (17/10) mm Hg, total cholesterol level of 4.5 (0.75) mmol/L, HDL cholesterol level of 1.2 (0.29) mmol/L, with median (interquartile range [IQR]) known diabetes duration of 6 (3—11) years, and HbA1c of 8.0% (7.2—9.0). Sixty-five percent were male, 91% white, 4% Afro-Caribbean, 5% Indian-Asian, and 15% current smokers. The UKPDS Outcomes Model median (IQR) estimated age at death was 76.6 (73.8—79.5) years compared with 81.6 (79.4—83.2) years, estimated using the UK Government Actuary’s Department data for a general population of the same age and gender structure. The median (IQR) difference was 4.3 (2.8—6.1) years, a remaining life expectancy reduction of almost one quarter. The highest value annuity identified, which commences payments immediately for a 60-year-old man with insulin-treated type 2 diabetes investing 100,000, did not reflect this difference, offering 7.4K per year compared with 7.0K per year if not diabetic. Conclusions. The UK Government Actuary’s Department data overestimate likely age at death in individuals with type 2 diabetes, and at present, ‘‘enhanced impaired life annuity’’ rates do not provide equity for people with type 2 diabetes. Using a diabetes-specific model to estimate life expectancy could provide valuable information to the annuity industry and permit more equitable annuity rates for those with type 2 diabetes.

A CONDITIONAL EQUITY RISK MODEL FOR REGULATORY ASSESSMENT

2018 ◽  
Vol 49 (1) ◽  
pp. 217-242
Author(s):  
A. Floryszczak ◽  
J. Lévy Véhel ◽  
M. Majri
Keyword(s):  
Risk Assessment ◽  
Risk Model ◽  
Capital Requirements ◽  
Market Failures ◽  
Equity Prices ◽  
Equity Risk ◽  
Solvency Capital ◽  
Functional Differential ◽  
Stochastic Functional

AbstractWe define and study in this work a simple model designed for managing long-term market risk of financial institutions with long-term commitments. It allows the assessment of solvency capital requirements and the allocation of risk budgets. This model allows one to avoid over-assessment of solvency capital requirements specifically after market disruptions. It relies on a dampener component in charge of refining risk assessment after market failures. Rather than aiming at a realistic and thus complex description of equity prices movements, this model concentrates on minimal features enabling accurate computation of capital requirements. It is defined both in a discrete and continuous fashion. In the latter case, we prove the existence, uniqueness and stability of the solution of the stochastic functional differential equation that specifies the model. One difficulty is that the proposed underlying stochastic process has neither stationary nor independent increments. We are however able to perform statistical analyses in view of its validation. Numerical experiments show that our model outperforms more elaborate ones of common use as far as medium-term (between 6 months and 5 years) risk assessment is concerned.

scholarly journals Restricted Coherent Risk Measures and Actuarial Solvency

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Christos E. Kountzakis
Keyword(s):  
Minimization Problem ◽  
Risk Measures ◽  
Insurance Company ◽  
Coherent Risk Measures ◽  
Dual Representation ◽  
Coherent Risk ◽  
Representation Form ◽  
Solvency Capital

We prove a general dual representation form for restricted coherent risk measures, and we apply it to a minimization problem of the required solvency capital for an insurance company.

scholarly journals The Reciprocal Generalized Inverse Gaussian Frailty with Application in Life Annuity Business

2020 ◽  
pp. 112-131
Author(s):  
Walter Onchere ◽  
Richard Tinega ◽  
Patrick Weke ◽  
Jam Otieno
Keyword(s):  
Simulation Study ◽  
Unobserved Heterogeneity ◽  
Survival Curve ◽  
Gaussian Model ◽  
Frailty Models ◽  
Residual Lifetime ◽  
Longevity Risk ◽  
Inverse Gaussian ◽  
Life Annuity ◽  
Valuation Of Life

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.

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