Calculating Capital Requirements for Longevity Risk in Life Insurance Products Using an Internal Model in Line with Solvency II

Author(s):  
Ralph Stevens ◽  
Anja De Waegenaere ◽  
Bertrand Melenberg
2020 ◽  
Vol 14 (2) ◽  
pp. 420-444
Author(s):  
Fabrice Balland ◽  
Alexandre Boumezoued ◽  
Laurent Devineau ◽  
Marine Habart ◽  
Tom Popa

AbstractIn this paper, we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the Solvency 2 framework. In particular, we are concerned with abnormal cohort effects such as those for generations 1919 and 1920, for which the period tables provided by the Human Mortality Database show particularly low and high mortality rates, respectively. To provide corrected tables for the three countries of interest here (France, Italy and West Germany), we use the approach developed by Boumezoued for countries for which the method applies (France and Italy) and provide an extension of the method for West Germany as monthly fertility histories are not sufficient to cover the generations of interest. These mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios, from which the extreme 0.5% longevity improvement can be extracted, allowing for the calculation of the solvency capital requirement. More precisely, to assess the impact of such anomalies in the Solvency II framework, we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages. Correcting this bias obviously improves the data quality of the mortality inputs, which is of paramount importance today, and slightly decreases the capital requirement. Overall, the longevity risk assessment remains stable, as well as the selection of the stochastic mortality model. As a collateral gain of this data quality improvement, the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk.


2013 ◽  
Vol 44 (1) ◽  
pp. 1-38 ◽  
Author(s):  
Matthias Börger ◽  
Daniel Fleischer ◽  
Nikita Kuksin

AbstractStochastic modeling of mortality/longevity risks is necessary for internal models of (re)insurers under the new solvency regimes, such as Solvency II and the Swiss Solvency Test. In this paper, we propose a mortality model which fulfills all requirements imposed by these regimes. We show how the model can be calibrated and applied to the simultaneous modeling of both mortality and longevity risk for several populations. The main contribution of this paper is a stochastic trend component which explicitly models changes in the long-term mortality trend assumption over time. This allows to quantify mortality and longevity risk over the one-year time horizon prescribed by the solvency regimes without relying on nested simulations. We illustrate the practical ability of our model by calculating solvency capital requirements for some example portfolios, and we compare these capital requirements with those from the Solvency II standard formula.


2018 ◽  
Vol 49 (1) ◽  
pp. 5-30 ◽  
Author(s):  
An Chen ◽  
Peter Hieber ◽  
Jakob K. Klein

AbstractFor insurance companies in Europe, the introduction of Solvency II leads to a tightening of rules for solvency capital provision. In life insurance, this especially affects retirement products that contain a significant portion of longevity risk (e.g., conventional annuities). Insurance companies might react by price increases for those products, and, at the same time, might think of alternatives that shift longevity risk (at least partially) to policyholders. In the extreme case, this leads to so-called tontine products where the insurance company’s role is merely administrative and longevity risk is shared within a pool of policyholders. From the policyholder’s viewpoint, such products are, however, not desirable as they lead to a high uncertainty of retirement income at old ages. In this article, we alternatively suggest a so-called tonuity that combines the appealing features of tontine and conventional annuity. Until some fixed age (the switching time), a tonuity’s payoff is tontine-like, afterwards the policyholder receives a secure payment of a (deferred) annuity. A tonuity is attractive for both the retiree (who benefits from a secure income at old ages) and the insurance company (whose capital requirements are reduced compared to conventional annuities). The tonuity is a possibility to offer tailor-made retirement products: using risk capital charges linked to Solvency II, we show that retirees with very low or very high risk aversion prefer a tontine or conventional annuity, respectively. Retirees with medium risk aversion, however, prefer a tonuity. In a utility-based framework, we therefore determine the optimal tonuity characterized by the critical switching time that maximizes the policyholder’s lifetime utility.


2011 ◽  
Vol 16 (1) ◽  
pp. 121-159 ◽  
Author(s):  
E. M. Varnell

AbstractThe Solvency II Directive mandates insurance firms to value their assets and liabilities using market consistent valuation. For many types of insurance business Economic Scenario Generators (ESGs) are the only practical way to determine the market consistent value of liabilities. The directive also allows insurance companies to use an internal model to calculate their solvency capital requirement. In particular, this includes use of ESG models. Regardless of whether an insurer chooses to use an internal model, Economic Scenario Generators will be the only practical way of valuing many life insurance contracts. Draft advice published by the Committee of European Insurance and Occupational Pensions Supervisors (CEIOPS) requires that insurance firms who intend to use an internal model to calculate their capital requirements under Solvency II need to comply with a number of tests regardless of whether the model (or data) is produced internally or is externally sourced. In particular the tests include a ‘use test’, mandating the use of the model for important decision making within the insurer. This means that Economic Scenario Generators will need to subject themselves to the governance processes and that senior managers and boards will need to understand what ESG models do and what they don't do. In general, few senior managers are keen practitioners of stochastic calculus, the building blocks of ESG models. The paper therefore seeks to explain Economic Scenario Generator models from a non-technical perspective as far as possible and to give senior management some guidance of the main issues surrounding these models from an ERM/Solvency II perspective.


2017 ◽  
Vol 23 (2) ◽  
pp. 428-440 ◽  
Author(s):  
Casian BUTACI ◽  
Simona DZITAC ◽  
Ioan DZITAC ◽  
Gabriela BOLOGA

The directive 2009/138/EC „Solvency II”, provides the determination of insurance capital requirements based either on a standard formula or an internal model built by the company and approved by the regulatory authority. The build of an internal model involves the determination of an extreme quantile from the empirical distribution of portfolio. An estimate of this quantile, with a 99.5% confidence level, requires a large number of simulations, each taking into account different scenarios as: insufficient reserves, unfavourable developments of financial assets, etc. The present paper proposes to argue the necessity of the extreme value theory approach in order to estimate the risk of loss for the insurance issue, in accordance with European Directive „Solvency II”, from the perspective of making prudent decisions for the assessment of insurance capital requirements.


Author(s):  
Ilze Zariņa ◽  
Irina Voronova ◽  
Gaida Pettere

Purpose – solvency II framework regulates how much capital the European Union insurance companies must hold. The amount of necessary capital can be calculated using a standard formula or an internal model. On the basis of the review of other authors’ empirical research, the present paper aim at identifying factors that influence necessary capital and propos-ing necessary areas of improvement for the methodology of an internal capital model. Research methodology – to conduct the paper, the authors have used the extended literature review. Analytical methods and comparative methods have been used for the Baltic non-life insurance market analysis. Findings – the Baltic market does not use an internal model even for a major risk – premium and reserve risks. A review of the current literature findings shows that the main weakness of the standard formula is risk aggregation. Research limitations – identified factors apply to non-life insurance companies under the Solvency II framework with a focus on reserve risk. Practical implications – factors are identified that should be implemented in the internal model methodology. The paper will help avoid using internal models as only a modern risk management tool and improve risk profile accuracy. Originality/Value – improvements of the internal model methodology are proposed based on a literature review. The au-thors have identified the main directions, issues and improvement possibilities for reaching modern risk management.


2009 ◽  
Vol 15 (2) ◽  
pp. 367-459 ◽  
Author(s):  
D. Brooks ◽  
R. J. Care ◽  
M. B. Chaplin ◽  
A. M. Kaufman ◽  
K. A. Morgan ◽  
...  

ABSTRACTThe draft paper sets out the authors' views of what good practice for the actuarial aspects of internal models will look like in 2012, the year Solvency II is expected to be implemented. Actuaries working on internal models can expect to have to follow such practices if their internal models are to be approved for use in calculating regulatory capital. The paper is therefore relevant for actuaries who plan to work on internal model implementation for Solvency II.Moreover, the risk quantification techniques discussed in the paper can also be used in the Own Risk Solvency Assessment (ORSA) process also required by Solvency II. The paper is therefore relevant to actuaries working in companies that are not planning to apply to use an internal model.The paper covers both life and non-life insurance and reinsurance, and reviews current practice as well as setting out possible future practice. This leads to identification of areas for research by the Profession to prepare for 2012 and an indication of the directions this work might take.The paper is effectively a work in progress, and readers should ask themselves what they should do in response to the ideas discussed.


Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 74 ◽  
Author(s):  
Fabiana Gómez ◽  
Jorge Ponce

This paper provides a rationale for the macro-prudential regulation of insurance companies, where capital requirements increase in their contribution to systemic risk. In the absence of systemic risk, the formal model in this paper predicts that optimal regulation may be implemented by capital regulation (similar to that observed in practice, e.g., Solvency II ) and by actuarially fair technical reserve. However, these instruments are not sufficient when insurance companies are exposed to systemic risk: prudential regulation should also add a systemic component to capital requirements that is non-decreasing in the firm’s exposure to systemic risk. Implementing the optimal policy implies separating insurance firms into two categories according to their exposure to systemic risk: those with relatively low exposure should be eligible for bailouts, while those with high exposure should not benefit from public support if a systemic event occurs.


Author(s):  
Karen Tanja Rödel ◽  
Stefan Graf ◽  
Alexander Kling ◽  
Andreas Reuß
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