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1

CALCULATION OF THE CAPITAL REQUIREMENT USING THE MONTE CARLO SIMULATION FOR NON-LIFE INSURANCE

100%
Mucha V., Páleš M., Sakálová K.
Ekonomický časopis (Journal of Economics)
|
2016
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vol. 64
|
issue 9
878 – 893
EN
The aim of the paper is to demonstrate the possibility of using the Monte Carlo method within the field of risk reduction within the framework of a developed model by applying a particular form of insurance. It is focused on the area of non-life insurance in which the collective risk model is suitable for describing the total claims in a given portfolio of insurance contracts. The Monte Carlo simulation method is the starting point, from which one can generate values of the total claim amount and their statistical treatment for the needs of measuring the value of the capital required to ensure solvency. As a final result the paper presents simulations as an effective problem solving tool, by enabling the development of interactive studies in the risk management process. The methodology presented makes use of Visual Basic for Applications under Microsoft Excel. This opens up the potential of developing actuarial software for solving risk reduction problems by applying various forms of insurance. Given the ability of the method to react flexibly to changes in the given form of insurance or its parameters can be used also to optimise the choice of suitable scenarios.
2

SOCIO-ECONOMIC IMPACTS OF PANDEMIC INFLUENZA MITIGATION SCENARIOS IN SLOVAKIA

88%
Brunovský P., Ševčovič D., Somorčík J., Hroncová D., Pospíšilová K.
Ekonomický časopis (Journal of Economics)
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2009
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vol. 57
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issue 2
163-178
EN
The aim of this paper is to assess the expected socio-economic impacts of various scenarios of pandemic influenza mitigation on the economy and mortality for Slovakia. Compared to similar past studies (e.g. Van Genugten et al. (2003)), the authors' approach bears a significant difference. Whereas those studies work from the very beginning with the expected values of the data, they have treated the data as well as the model parameters as random variables. Results in the form of probability distributions and their characteristics (expected values and tolerance intervals) were obtained by stochastic Monte Carlo simulations of random impacts on 5,400,000 inhabitants of Slovakia. Six scenarios of pandemic mitigation have been analyzed. Total costs of medical treatment, the number of casualties as well as social costs with casualties included were compared.
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