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 Simulation-based capital models: testing, justifying and communicating choices. A report from the life aggregation and simulation techniques working party

2017 ◽  
Vol 22 (2) ◽  
pp. 257-335
Author(s):  
T. Androschuck ◽  
S. Gibbs ◽  
N. Katrakis ◽  
J. Lau ◽  
S. Oram ◽  
...  
Keyword(s):  
Working Party ◽  
Tail Dependence ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Economic Capital ◽  
Capital Adequacy ◽  
Individual Risk ◽  
Aggregation Techniques ◽  
Simulation Based ◽  
The Individual

AbstractThe development of an economic capital model requires a decision to be made regarding how to aggregate capital requirements for the individual risk factors while taking into account the effects of diversification. Under the Individual Capital Adequacy Standards framework, UK life insurers have commonly adopted a correlation matrix approach due to its simplicity and ease in communication to the stakeholders involved, adjusting the result, where appropriate, to allow for non-linear interactions. The regulatory requirements of Solvency II have been one of the principal drivers leading to an increased use of more sophisticated aggregation techniques in economic capital models. This paper focusses on a simulation-based approach to the aggregation of capital requirements using copulas and proxy models. It describes the practical challenges in parameterising a copula including how allowance may be made for tail dependence. It also covers the challenges associated with fitting and validating a proxy model. In particular, the paper outlines how insurers could test, communicate and justify the choices made through the use of some examples.

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 A Critical Analysis of Risk Factors for Selected Tropical Diseases

2021 ◽  
Author(s):  
Faith-Michael Uzoka ◽  
Christie Akwaowo ◽  
Chinyere Nwafor Okoli ◽  
Victory Ekpin ◽  
Chukwudi Nwokoro ◽  
...  
Keyword(s):  
Risk Factors ◽  
Typhoid Fever ◽  
Demographic Data ◽  
Significant Risk ◽  
Personal Hygiene ◽  
Individual Risk ◽  
Tropical Diseases ◽  
Economic Implications ◽  
Frequency Distributions ◽  
The Individual

Abstract Aim: The aim of this study was to examine the impacts of different (non-clinical) risk factors on the populations’ predisposition to tropical diseases specifically Malaria, yellow fever, typhoid fever, chicken pox, measles, hepatitis B and UTI.Subject and Methods: Data for this study was obtained through patient diagnosis forms, distributed to physicians in Nigeria. A total of 2199 patient consultation forms were returned by 102 (out of 125) physicians, and considered useful for analysis. Demographic data of patients, physicians, and diagnosis outcomes were analysed descriptively through frequency distributions, aggregate analysis, and graphs, while the influence of risk factors on the disease manifestations (diagnosis outcomes) were determined using regression analysis.Results: Findings from our study demonstrated that the difficulty in diagnosing tropical disease was associated with significant increase in morbidity and mortality especially in patients with malaria, UTI and typhoid fever. Factors such as contact with an infected person and poor personal hygiene posed significant risk, while urbanization and homelessness, posed very low risks across all the diseases. Conclusion: The risk factors identified in our study exert differential and discriminating influences in the causation, predisposition, and transmission of these conditions, understanding the individual risk factors for each condition have significant socio-economic implications for people living in tropical and endemic regions, especially with respect to management and prevention of these conditions.

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 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 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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