Dividends of Life Insurance Companies and the Solvency Capital Requirements

2020 ◽  
pp. 221-230
Author(s):  
Joanna Głód ◽  
Lyubov Klapkiv ◽  
Anna Białek-Jaworska ◽  
Krzysztof Opolski
Keyword(s):  
Life Insurance ◽  
Capital Requirements ◽  
Insurance Companies ◽  
Solvency Capital

scholarly journals OPTIMAL ASSET ALLOCATION IN LIFE INSURANCE: THE IMPACT OF REGULATION

2016 ◽  
Vol 46 (3) ◽  
pp. 605-626 ◽  
Author(s):  
An Chen ◽  
Peter Hieber
Keyword(s):  
Life Insurance ◽  
Asset Allocation ◽  
Regulatory Policy ◽  
Capital Requirements ◽  
Insurance Company ◽  
Traffic Light ◽  
Optimal Insurance ◽  
Insurance Contracts ◽  
Solvency Capital ◽  
The Impact

AbstractIn a typical equity-linked life insurance contract, the insurance company is entitled to a share of return surpluses as compensation for the return guarantee granted to the policyholders. The set of possible contract terms might, however, be restricted by a regulatory default constraint — a fact that can force the two parties to initiate sub-optimal insurance contracts. We show that this effect can be mitigated if regulatory policy is more flexible. We suggest that the regulator implement a traffic light system where companies are forced to reduce the riskiness of their asset allocation in distress. In a utility-based framework, we show that the introduction of such a system can increase the benefits of the policyholder without deteriorating the benefits of the insurance company. At the same time, default probabilities (and thus solvency capital requirements) can be reduced.

Quantifying Operational Risk in Life Insurance Companies. Developed by the Life Operational Risk Working Party

2007 ◽  
Vol 13 (2) ◽  
pp. 257-337 ◽  
Author(s):  
N. C. Dexter ◽  
C. L. Ford ◽  
P. C. Jakhria ◽  
P. O. J. Kelliher ◽  
D. McCall ◽  
...  
Keyword(s):  
Risk Management ◽  
Life Insurance ◽  
Best Practice ◽  
Early Stage ◽  
Working Party ◽  
Operational Risk ◽  
Capital Requirements ◽  
Senior Management ◽  
Insurance Companies ◽  
Stage Of Development

ABSTRACTThis paper overviews a practical approach to the assessment of operational risk in life insurance companies. It considers how actuaries, working in conjunction with risk management professionals and senior management, can develop a framework to assess the capital requirements relating to operational risk, taking into account the capital requirements of other risks and their interaction.This paper recognises that we do not live in an ideal world, and that a lot of the data which one might want for operational risk assessment are not, and in some cases never will be, available. Consequently, the approach outlined in this paper takes into account the fact that management and assessment of operational risk is at an early stage of development in the life industry. In addition, it outlines some of the areas where development is necessary or desirable in the coming years.There is a section on the operational risks against which it is appropriate to hold capital. As this is a new area for insurance companies, and given the governance requirements for Individual Capital Assessments, it is important to explain the results effectively to senior management. Therefore, a brief review of techniques for reporting the results of the assessment is provided.The paper concludes with some thoughts on how operational risk management can be embedded more in the business, and then considers what future work will help develop the framework. To echo the thoughts of the authors of the general insurance paper on this topic (Tripp et al., 2004), we hope that the paper will sow seeds for the development of best practice in dealing with operational risk, and will raise the awareness and increase the interest of actuaries in this emerging topic.This paper represents the views of the individuals in the working party, and not necessarily the views of their employers or the Actuarial Profession.

scholarly journals Solvency II Calibrations: Where Curiosity Meets Spuriosity

2016 ◽  
Author(s):  
Stefan Mittnik
Keyword(s):  
Significant Risk ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Insurance Companies ◽  
Individual Risk ◽  
Standard Formula ◽  
Standard Scr ◽  
Solvency Capital ◽  
The Individual ◽  
Parameter Values

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

scholarly journals Stress and scenario tests in the context of a Romanian non-life insurance company

2019 ◽  
Vol 13 (1) ◽  
pp. 149-161
Author(s):  
Aurora Elena Dina Manolache
Keyword(s):  
Life Insurance ◽  
Stress Testing ◽  
Capital Requirements ◽  
Stress Factors ◽  
Insurance Company ◽  
Life Insurance Company ◽  
High Exposure ◽  
Natural Catastrophe ◽  
Best Estimate ◽  
Solvency Capital

Abstract The main aim of the article is to assess the vulnerabilities and resilience of a Romanian non-life insurance company in the context of different predefined insurance stress scenarios: natural catastrophe scenario and business scenario. The natural catastrophe scenario consists in two distinct scenarios: earthquake and flood, which were carried out separately and aggregated based on three stress factors: increasing by 15% of PML, increasing by 5% of the gross best estimate claim provisions and reinsurer’s incapacity to pay. The business stress scenario was based on four stress parameters: increasing by 3 % of the claims provisions due to the inflation impact, increasing by 10% of the gross earned premium for MTPL due to the legislative changes, increasing by 15% of the claims provisions for MTPL due to the increase of frequency and severity of the losses induced by the exposure growth as a result of the lower premiums and decreasing by 10% of the ceded best estimate. The results of the stress testing shown that the insurer is more sensitive to business scenario compared to natural catastrophe scenario due to the significant exposure on the MTPL line of business. High exposure to earthquake risk is a characteristic for Romania and the stress testing results confirm the vulnerability of the insurer to the earthquake scenario (non-compliance of the solvency capital requirements), due to the biggest impact of the factor: reinsurer’s incapacity to pay. Therefore, the quality of reinsurance is very important for Romanian insurance companies to be able to manage the risks arising from the seismic events and to be compliant with the regulatory solvency capital requirements.

scholarly journals SOLVENCY REQUIREMENT IN A UNISEX MORTALITY MODEL

2018 ◽  
Vol 48 (3) ◽  
pp. 1219-1243
Author(s):  
An Chen ◽  
Montserrat Guillen ◽  
Elena Vigna
Keyword(s):  
Theoretical Result ◽  
Capital Requirements ◽  
Numerical Results ◽  
Insurance Companies ◽  
Survival Probabilities ◽  
Mortality Model ◽  
Solvency Capital ◽  
Gender Based ◽  
Fairness Principle ◽  
The Eu

AbstractFollowing the EU Gender Directive, that obliges insurance companies to charge the same premium to policyholders of different genders, we address the issue of calculating solvency capital requirements (SCRs) for pure endowments and annuities issued to mixed portfolios. The main theoretical result is that, if the unisex fairness principle is adopted for the unisex premium, the SCR at issuing time of the mixed portfolio calculated with unisex survival probabilities is greater than the sum of the SCRs of the gender-based subportfolios. Numerical results show that for pure endowments the gap between the two is negligible, but for lifetime annuities the gap can be as high as 3–4%. We also analyze some conservative pricing procedures that deviate from the unisex fairness principle, and find that they lead to SCRs that are lower than the sum of the gender-based SCRs because the policyholders are overcharged at issuing time.

scholarly journals Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 21 ◽  
Author(s):  
Anne-Sophie Krah ◽  
Zoran Nikolić ◽  
Ralf Korn
Keyword(s):  
Machine Learning ◽  
Monte Carlo ◽  
Least Squares ◽  
Life Insurance ◽  
Capital Requirements ◽  
Multivariate Adaptive Regression Splines ◽  
Insurance Companies ◽  
Least Squares Regression ◽  
Least Squares Monte Carlo ◽  
Proxy Modeling

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have to rely on suitable approximation techniques such as the least-squares Monte Carlo (LSMC) method. The key idea of LSMC is to run only a few wisely selected simulations and to process their output further to obtain a risk-dependent proxy function of the loss. In this paper, we present and analyze various adaptive machine learning approaches that can take over the proxy modeling task. The studied approaches range from ordinary and generalized least-squares regression variants over generalized linear model (GLM) and generalized additive model (GAM) methods to multivariate adaptive regression splines (MARS) and kernel regression routines. We justify the combinability of their regression ingredients in a theoretical discourse. Further, we illustrate the approaches in slightly disguised real-world experiments and perform comprehensive out-of-sample tests.

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