Determining Capital Requirement for Market Risk in the Solvency Assessment Model of Insurance Companies

The most important challenge that solvency assessment system of insurance companies faces is understanding the concept of risk and then measuring and quantifying it. One of the most important risks of insurance companies is market risk stemming from investments. The main purpose of this paper is to correct the defects of the method of calculation of the solvency of insurance companies to consider more accurately the financial time series features for estimating the value of risk exposed investment portfolios (stocks of exchange companies, currency accounts, and real estate). First, the marginal distributions of log-returns of time series were modeled using GARCH models. Then, using the Genetic Algorithm (GA) in order to achieve the best threshold in Extreme Value Theory (EVT), distribution sequences were modeled and using copulas to model dependency structuresbetween marginal distributions. Finally, back-testing methods show that the proposed model had a better performance than the traditional simulation model. Also, the results of the student’s t copula function were more acceptable, and market risk factor was estimated as 9.403%.