Řízení vybraných rizik dle směrnice Solvency II

Abstract

The subject of the dissertation is the analysis of the process of risk management in the insurance companies. The current legal regulation of the insurance companies Solvency I directive is criticized due to that the insurance companies are focusing only on fluctuations related to insurance business and thus do not take into account fluctuations in the financial markets achich may have a significant effect on the solvency of insurance companies. Solvency II directive represent an important review of the financial regulative of insurance industry within European Union. The development process of the Solvency II directive is dated from early 21st century. The insurance company will be legally obliged to report solvency capital requirements probably from 2016, in which it is supposed to come into force. The aim of this dissertation is to evaluate of chosen approaches for determination of solvency capital requirements for market and life underwriting risk in the context of Solvency II. Application part of the thesis is divided into two parts. The second part of the application part is devoted to determination of solvency capital requirement for life underwriting risk. Firstly, the parameters of the Lee-Carter model are estimated on the basis of data for mortality tables for men and women in the Czech Republic and then mortality index is predicted. Based on the results of the model parameters we analyzed the impact on the calculation of the amount of insurance company´s premium for chosen insurance product. The first part is devoted to the determination of the solvency capital requirement for market risk. We have considered three efficient portfolios. On the basis of the results of the basich characteristics for individual portfolios the Value at Risk and Expected Shortfall using analytical methods and Monte Carlo simulation for Gaussian and Student probability distribution are determined. Furthermore, these values are also determined using the Normal Inverse Gaussian model and Variance Gamma model. These processes allow us to model the higher moments of probability distributions - skewness and kurtosis, we should avoid of underestimation of risk.