scholarly journals Geometric-Gamma Collective Modified Value-at-Risk Model in Life Insurance Risk

2018 ◽  
Vol 7 (3.20) ◽  
pp. 372
Author(s):  
Muhammad Iqbal Al-Banna Ismail ◽  
Sukono . ◽  
Abdul Talib BIN Bon ◽  
Yuyun Hidayat ◽  
Eman Lesmana ◽  
...  
Keyword(s):  
At Risk ◽  
Life Insurance ◽  
Value At Risk ◽  
Risk Model ◽  
Risk Measure ◽  
Insurance Companies ◽  
Insurance Company ◽  
Insurance Risk ◽  
Collective Risk Model ◽  
Collective Risk

Claim risk is a payment made by the insurance company to the policyholder. Actuaries in insurance companies should be able to measure and control the risk of claims, in order to avoid losses to insurance companies. In this paper we analyze the Geometric-Gamma Collective Modified Value-at-Risk model in life insurance risk. In this research, there is a development of claim risk measure called Collective Modified Value-at-Risk, which is an extension of Collective Risk model. This Collective Modified Value-at-Risk model requires estimation of the mean, variance, skewness, and kurtosis parameters. The result of this research, is that the extent of this model can be applied to the risk of claims amount of non-normal distributed. Thus, the Collective Modified Value-at-Risk model can serve as one of the statistical alternatives for measuring the risk of claims on life insurance.  

scholarly journals Collective Value-At-Risk (Colvar) In Life Insurance Collection

2018 ◽  
Vol 7 (3.7) ◽  
pp. 25
Author(s):  
Abdul Talib Bon ◽  
Muhammad Iqbal Al-Banna Ismail ◽  
Sukono . ◽  
Adhitya Ronnie Effendie
Keyword(s):  
At Risk ◽  
Standard Deviation ◽  
Life Insurance ◽  
Value At Risk ◽  
Risk Model ◽  
Risk Measures ◽  
Model Development ◽  
Insurance Company ◽  
Insurance Claims ◽  
Level Of Significance

Analysis of risk in life insurance claims is very important to do by the insurance company actuary. Risk in life insurance claims are generally measured using the standard deviation or variance. The problem is, that the standard deviation or variance which is used as a measure of the risk of a claim can not accommodate any claims of risk events. Therefore, in this study developed a model called risk measures Collective Modified Value-at-Risk. Model development is done for several models of the distribution of the number of claims and the distribution of the value of the claim. Collective results of model development Modified Value-at-Risk is expected to accommodate any claims of risk events, when given a certain level of significance  

Collective Modified Value at Risk in Life Insurance

2021 ◽  
Vol 2 (1) ◽  
pp. 24-32
Author(s):  
Muhammad Iqbal Al-Banna Ismail ◽  
Abdul Talib Bon ◽  
Abdul Talib Bon ◽  
Sukono Sukono ◽  
Sukono Sukono ◽  
...  
Keyword(s):  
At Risk ◽  
Life Insurance ◽  
Value At Risk ◽  
Insurance Company ◽  
Insurance Claims ◽  
Insurance System ◽  
Collective Risk ◽  
Risk Loss

Insurance is seen as a tool which individuals can transfer risks to others, where insurance collect funds from individuals to meet financial needs related to damage. Therefore analysis of risk in life insurance claims is really be needed bt the insurance company actuary. In an insurance system, the risk is the event when an insured party puts forward a claim. Claim is the compensation for a risk loss. Individual claim in one period insurance is called aggregation claim while aggregation claim is collective risk

scholarly journals The Absolute Ruin Insurance Risk Model with a Threshold Dividend Strategy

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 377 ◽  
Author(s):  
Wenguang Yu ◽  
Yujuan Huang ◽  
Chaoran Cui
Keyword(s):  
Risk Model ◽  
Insurance Companies ◽  
Insurance Company ◽  
Insurance Risk ◽  
Threshold Dividend Strategy ◽  
Dividend Payments ◽  
Absolute Ruin ◽  
The Absolute ◽  
Debit Interest ◽  
Insurance Risk Model

The absolute ruin insurance risk model is modified by including some valuable market economic information factors, such as credit interest, debit interest and dividend payments. Such information is especially important for insurance companies to control risks. We further assume that the insurance company is able to finance and continue to operate when its reserve is negative. We investigate the integro-differential equations for some interest actuarial diagnostics. We also provide numerical examples to explain the effects of relevant parameters on actuarial diagnostics.

scholarly journals Optimal Retention for a Stop-loss Reinsurance Under the VaR and CTE Risk Measures

2007 ◽  
Vol 37 (1) ◽  
pp. 93-112 ◽  
Author(s):  
Jun Cai ◽  
Ken Seng Tan
Keyword(s):  
Value At Risk ◽  
Negative Binomial ◽  
Risk Model ◽  
Sufficient Conditions ◽  
Individual Risk ◽  
Risk Models ◽  
Optimization Criteria ◽  
Collective Risk Model ◽  
Collective Risk ◽  
Stop Loss

We propose practical solutions for the determination of optimal retentions in a stop-loss reinsurance. We develop two new optimization criteria for deriving the optimal retentions by, respectively, minimizing the value-at-risk (VaR) and the conditional tail expectation (CTE) of the total risks of an insurer. We establish necessary and sufficient conditions for the existence of the optimal retentions for two risk models: individual risk model and collective risk model. The resulting optimal solution of our optimization criterion has several important characteristics: (i) the optimal retention has a very simple analytic form; (ii) the optimal retention depends only on the assumed loss distribution and the reinsurer’s safety loading factor; (iii) the CTE criterion is more applicable than the VaR criterion in the sense that the optimal condition for the former is less restrictive than the latter; (iv) if optimal solutions exist, then both VaR- and CTE-based optimization criteria yield the same optimal retentions. In terms of applications, we extend the results to the individual risk models with dependent risks and use multivariate phase type distribution, multivariate Pareto distribution and multivariate Bernoulli distribution to illustrate the effect of dependence on optimal retentions. We also use the compound Poisson distribution and the compound negative binomial distribution to illustrate the optimal retentions in a collective risk model.

scholarly journals Optimal Retention for a Quota-Share Reinsurance

2018 ◽  
Vol 20 (1) ◽  
pp. 25-32
Author(s):  
Lienda Noviyanti ◽  
Achmad Zanbar Soleh ◽  
Anna Chadidjah ◽  
Hasna Afifah Rusyda
Keyword(s):  
Financial Services ◽  
Value At Risk ◽  
Risk Measure ◽  
Minimum Variance ◽  
Insurance Companies ◽  
Insurance Company ◽  
Bivariate Exponential Distribution ◽  
Property Insurance ◽  
Bivariate Exponential ◽  
Premium Income

The Indonesian Financial Services Authority (OJK) has instructed all insurance providers in Indonesia to apply a mandatory tariff for property insurance. The tariff has to be uniformly applied and the rule of set the maximum and minimum premium rates for protection against losses. Furthermore, the OJK issued the new rule regarding self-retention and domestic reinsurance. Insurance companies are obliged to have and implement self-retention for each risk in accordance with the self-retention limits. Fluctuations of total premium income and claims may lead the insurance company cannot fulfil the obligation to the insured, thus the company needs to conduct reinsurance. Reinsurance helps protect insurers against unforeseen or extraordinary losses by allowing them to spread their risks. Because reinsurer chargers premium to the insurance company, a properly calculated optimal retention would be nearly as high as the insurer financial ability.  This paper is aimed at determining optimal retentions indicated by the risk measure Value at Risk (VaR), Expected Shortfall (ES) and Minimum Variance (MV). Here we use the expectation premium principle which minimizes individual risks based on their quota share reinsurance. Regarding to the data in an insurance property, we use a bivariate lognormal distribution to obtain VaR, ES and MV, and a bivariate exponential distribution to obtain MV. The bivariate distributions are required to derive the conditional probability of the amount of claim occurs given the benefit has occurred.

scholarly journals Methods for Computing Numerical Standard Errors: Review and Application to Value-at-Risk Estimation

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
David Ardia ◽  
Keven Bluteau ◽  
Lennart F. Hoogerheide
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Simulation Experiment ◽  
Risk Model ◽  
Risk Measure ◽  
Risk Estimation ◽  
Standard Errors ◽  
Monte Carlo Experiments ◽  
Simulation Based ◽  
Underlying Risk

Abstract Numerical standard error (NSE) is an estimate of the standard deviation of a simulation result if the simulation experiment were to be repeated many times. We review standard methods for computing NSE and perform a Monte Carlo experiments to compare their performance in the case of high/extreme autocorrelation. In particular, we propose an application to risk management where we assess the precision of the value-at-risk measure when the underlying risk model is estimated by simulation-based methods. Overall, heteroscedasticity and autocorrelation estimators with prewhitening perform best in the presence of large/extreme autocorrelation.

scholarly journals Assessment of the Stability of Insurance Companies: The Case of Baltic Non-Life Insurance Market

2018 ◽  
Vol 32 (1) ◽  
pp. 102-111 ◽  
Author(s):  
Ilze Zariņa ◽  
Irina Voronova ◽  
Gaida Pettere
Keyword(s):  
European Union ◽  
Life Insurance ◽  
Value At Risk ◽  
Risk Measure ◽  
Market Competition ◽  
Solvency Ii ◽  
Insurance Market ◽  
Insurance Companies ◽  
Coverage Ratio ◽  
The Baltic

Abstract The study gives an overview of the Baltic non-life insurance market. The purpose of the research is to summarise stability statistics on solvency ratios, risk profiles and capital surplus, which was contained in Solvency and Financial Condition reports (SFCR) in 2016 published first time by non-life insurance companies in European Union and Baltic market (Latvia, Estonia, and Lithuania). Solvency II came into effect in 2016, and these reports have been prepared using the new requirements of the Solvency II framework. All non-life insurance companies are required to have eligible own funds at least equal to solvency capital requirement (SCR) in order to avoid supervisory intervention (own funds divided by SCR are required to be at least 100 %). The SCR is based on well known risk measure value at risk with 99.5 % confidence level over a one-year time horizon. Baltic non-life insurance companies were strong capitalized (median 155 %) in 2016. It means that all Baltic companies can survive even if 1 in 200 years events have occurred although Baltic solvency coverage ratio is lower than the median ratio in European Union (209 %). For Latvian non-life insurance market, solvency ratio median is the lowest in European Union comparing by countries. The authors have analysed the historical development of the market and have calculated financial ratios, Gini’s concentration index, as well as dissimilarity index. The authors have investigated the current and future internal and external risks and issues for the Baltic non-life insurance market, such as political environment, low-yield environment, and market competition due to new mergers and acquisitions (M&A) activities, and a new rule for accounting for insurance companies IFRS17.

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