scholarly journals TONUITY: A NOVEL INDIVIDUAL-ORIENTED RETIREMENT PLAN

2018 ◽  
Vol 49 (1) ◽  
pp. 5-30 ◽  
Author(s):  
An Chen ◽  
Peter Hieber ◽  
Jakob K. Klein
Keyword(s):  
Risk Aversion ◽  
Switching Time ◽  
Extreme Case ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Insurance Companies ◽  
Longevity Risk ◽  
Insurance Company ◽  
Risk Capital ◽  
Medium Risk

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.

scholarly journals The impact of lending on bancassurance activity

2019 ◽  
Vol 13 (1) ◽  
pp. 171-181
Author(s):  
Elda Marzai Abliz
Keyword(s):  
Negative Impact ◽  
Distribution Channel ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Financial Supervision ◽  
Insurance Companies ◽  
Insurance Company ◽  
Customer Base ◽  
Premium Income ◽  
The Impact

Abstract Due to financial crisis, and especially because of prudence in lending (retail, micro, and corporate), banks are looking for new sources of income, and bancasurance is clearly a potential source of revenue. Thus, in the financial market, the interests of two major components of it are met: banks maximize commission income, and insurers make access to the large customer base of banks. Bancassurance is a distribution channel of insurance products through bank branches, bringing important advantages for banks, insurance companies and customers. The main advantage for the bank is that earns fee amount from the insurance company, the insurance company increases customers data base and market share, the client satisfy his financial needs and requests in the same institution. Considering that in Romania, banks and insurers do not provide information on the number of insurances sold via the bancassurance distribution channel, as well as commissions obtained by banks for the insurance sale, to determine the development of bancassurance in Romania, we used the statistical data provided by the National Bank of Romania, on credit growth and data provided by The Financial Supervision Association, on the evolution of gross written premiums. Bancassurance is one of the most important insurance distribution channels, accounting for approximately 36% of the global insurance market, in 2016, Europe’s insurers generated total premium income of €1 189bn and had €10 112bn invested in the economy. Regarding to the risks of bancassurance business for banks and insurers, they mainly concern distinct capital requirements for the banking and insurance systems, which will be covered by the Basel III and Solvency II directives. This paper aims to analyze the influence of credit on the bancassurance activity in the last 5 years in Romania, the economic, political and legal factors that have a negative impact on the development of bancassurance, and also the calculating the correlation coefficient r (Pearson’s coefficient) and his result.

scholarly journals Improvement of operational risk measurement under the Solvency II framework

2015 ◽  
Vol 5 (2) ◽  
pp. 135-141
Author(s):  
Darja Stepchenko ◽  
Gaida Pettere ◽  
Irina Voronova
Keyword(s):  
Operational Risk ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Risk Measurement ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Risk Capital ◽  
Mathematical Methods ◽  
Solvency Capital

Operational risk is one of the core risks of every insurance company in accordance to the solvency capital requirement under the Solvency II regime. The target of the research is to investigate the improvement possibilities of the operational risk measurement under Solvency II regime. The authors have prepared the algorithm of the operational risk measurement under Solvency II framework that helps improve the understanding of the operational risk capital requirements. Moreover, the authors have prepared the case study about a practical usage of the suggested algorithm through the example of one non-life insurance company. The authors use, in order to perform the research, such corresponding methods as theoretical and methodological analysis of scientific literature, analytical, statistical and mathematical methods.

scholarly journals Systemic Risk and Insurance Regulation †

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 74 ◽  
Author(s):  
Fabiana Gómez ◽  
Jorge Ponce
Keyword(s):  
Systemic Risk ◽  
Formal Model ◽  
Public Support ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Insurance Companies ◽  
Prudential Regulation ◽  
High Exposure ◽  
Insurance Firms ◽  
Low Exposure

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.

scholarly journals Maximum Market Price of Longevity Risk Under Solvency Regimes: The Case Of Solvency II

2017 ◽  
Author(s):  
Susanna Levantesi ◽  
Massimiliano Menzietti
Keyword(s):  
Market Price ◽  
Solvency Ii ◽  
Insurance Companies ◽  
Longevity Risk ◽  
Risk Margin ◽  
Maximum Price ◽  
Upper Limits ◽  
Forward Prices ◽  
Pricing Measure ◽  
Solvency Regimes

Longevity risk constitutes an important risk factor for life insurance companies and it can be managed through longevity-linked securities. The market of longevity-linked securities is at present far from being complete and does not allow to find a unique pricing measure. We propose a method to estimate the maximum market price of longevity risk depending on the risk margin implicit within the calculation of the technical provisions as defined by Solvency II. The maximum price of longevity risk is determined for a survivor forward (S-forward), an agreement between two counterparties to exchange at maturity a fixed survival-dependent payment for a payment depending on the realized survival of a given cohort of individuals. The maximum prices determined for the S-forwards can be used to price other longevity-linked securities, such as q-forwards. The Cairns-Blake-Dowd model is used to represent the evolution of mortality over time, that combined with the information on the risk margin, enables us to calculate upper limits for the risk-adjusted survival probabilities, the market price of longevity risk and the S-forward prices. Numerical results can be extended for the pricing of other longevity-linked securities.

scholarly journals The Story Of 100 Actuarially Guaranteed No-Ruin Casualty Insurance Companies

1975 ◽  
Vol 8 (3) ◽  
pp. 364-377
Author(s):  
Hilary L. Seal
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Flood Damage ◽  
Rate Of Return ◽  
Insurance Companies ◽  
Insurance Company ◽  
Risk Capital ◽  
The Public ◽  
For Profit

“Most people think that an insurance company's business is to make money out of insuring things. They are wrong. Its business is to take as much money off the public as possible, invest it successfully and hope that the conditions on which it was taken never happen.”The Economist, April 13, 1974 (p. 119)In order to motivate the series of Monte Carlo simulations we have carried out in the following article we would like readers to imagine that a small rural casualty insurance company, the Farm Fire and Flood Damage Ins. Co. (FFFDIC), is to be bought by an entrepreneur (whom we shall designate by EP) provided his consulting actuary (the author of this article) can satisfy his requirements which are as follow:(i) A 15-year investment is foreseen at the end of which time EP wishes to be able to sell, hopefully without loss.(ii) The risk-capital is to be invested and (although some of it must be in easily liquidable securities) should yield a rate of return comparable with that obtainable on the same amount of capital invested in the market.(iii) The premiums will not have risk-loadings, as such, but will be loaded for profit by 15%.(iv) The risk-capital should, on the average, be returnable at the end of the 15-year investment.

scholarly journals MODELING THE MORTALITY TREND UNDER MODERN SOLVENCY REGIMES

2013 ◽  
Vol 44 (1) ◽  
pp. 1-38 ◽  
Author(s):  
Matthias Börger ◽  
Daniel Fleischer ◽  
Nikita Kuksin
Keyword(s):  
Solvency Ii ◽  
Capital Requirements ◽  
Longevity Risk ◽  
Mortality Trend ◽  
Swiss Solvency Test ◽  
Solvency Capital ◽  
Simultaneous Modeling ◽  
One Year ◽  
The One ◽  
Solvency Regimes

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.

scholarly journals Measurement of Longevity Risk of Life Annuity Based on C-ROSS Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ming Zhao ◽  
Ziwen Li ◽  
Yinge Cai ◽  
Weiting Li
Keyword(s):  
Mortality Rate ◽  
Life Table ◽  
Life Insurance ◽  
Insurance Companies ◽  
Longevity Risk ◽  
Risk Capital ◽  
Risk Margin ◽  
Life Annuity ◽  
Mortality Improvement ◽  
Carter Model

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.

scholarly journals The Effect of Risk-Based Capital on Investment Returns of Insurance Companies in Kenya

2020 ◽  
Vol 16 (31) ◽  
Author(s):  
Willys Obuba Chache ◽  
Cyrus Iraya Mwangi ◽  
Winnie Nyamute ◽  
Caren Angima
Keyword(s):  
Linear Regression ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Coefficient Of Determination ◽  
Ratio Test ◽  
Insurance Company ◽  
Risk Capital ◽  
Significance Level ◽  
Investment Returns ◽  
Capital Charge

This paper focuses on analyzing the effect of risk-based capital on investment returns of insurance companies in Kenya. The study population comprised of 63 insurance companies licensed by Insurance Regulatory Authority (IRA). A longitudinal (panel) design was used to describe the association amongst variables on the study duration. Moreover, secondary data was collected from the insurance companies’ annual returns submitted to IRA for five-year duration (2014-2018), which yielded adequate data points for each insurance company deeming it viable. Risk-based capital was determined by the standard formulae as per the risk-based supervision model. It was a composition of operational risk charge, market risk charge, insurance risk charge, credit risk capital charge, and an adjustment which considered the lossabsorbing capacity of technical provisions and deferred taxes. Investment returns in insurance companies was calculated using the investment income ratio. Test of normality, linearity, multicollinearity, and independence were conducted and were found suitable for linear regression to be conducted. Linear regression was used to evaluate the nature of the relationship between the variables based on the hypothesis in the study and at a significance level of 5%. Coefficient of determination ( ) was derived to show how the model fits the data. The study findings revealed a positive and significant relationship between risk-based capital and investment returns, thus allowing investment portfolio managers in the insurance industry to justify their investments in high risk areas that may attract a high capital charge.

scholarly journals Operational risk quantification and modelling within Romanian insurance industry

2017 ◽  
Vol 11 (1) ◽  
pp. 637-648
Author(s):  
Răzvan Tudor ◽  
Dumitru Badea
Keyword(s):  
Standard Model ◽  
Insurance Industry ◽  
Operational Risk ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Capital Requirement ◽  
Risk Capital ◽  
Operational Risks ◽  
The Standard Model ◽  
Solvency Capital

Abstract This paper aims at covering and describing the shortcomings of various models used to quantify and model the operational risk within insurance industry with a particular focus on Romanian specific regulation: Norm 6/2015 concerning the operational risk issued by IT systems. While most of the local insurers are focusing on implementing the standard model to compute the Operational Risk solvency capital required, the local regulator has issued a local norm that requires to identify and assess the IT based operational risks from an ISO 27001 perspective. The challenges raised by the correlations assumed in the Standard model are substantially increased by this new regulation that requires only the identification and quantification of the IT operational risks. The solvency capital requirement stipulated by the implementation of Solvency II doesn’t recommend a model or formula on how to integrate the newly identified risks in the Operational Risk capital requirements. In this context we are going to assess the academic and practitioner’s understanding in what concerns: The Frequency-Severity approach, Bayesian estimation techniques, Scenario Analysis and Risk Accounting based on risk units, and how they could support the modelling of operational risk that are IT based. Developing an internal model only for the operational risk capital requirement proved to be, so far, costly and not necessarily beneficial for the local insurers. As the IT component will play a key role in the future of the insurance industry, the result of this analysis will provide a specific approach in operational risk modelling that can be implemented in the context of Solvency II, in a particular situation when (internal or external) operational risk databases are scarce or not available.

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