aAssistant Professor, Economic, University of Economic Sciences, Tehran, Iran
bMaster Student, System Engineering, University of Economic Sciences, Tehran, Iran
Online published on 4 December, 2014.
Third party insurance is obligatory in Iran. Therefore a large proportion of portfolio in insurance companies dedicates to car policy, especially third party policy. Regarding to this misbalanced portfolio, we decided to represent a suitable model for heteroskedasticity of car insurance data in Dana insurance company. Conditional heteroskedasticity can increase the certitude of value at risk estimation considerably. The results showed that the best models for estimating conditional variance of collision and third party insurance profits are GJR(1,1) and EGARCH(1,1) models alternatively. Then we calculated value at risk with student's t-distribution and observed that this value in third party insurance is about % and much more than collision insurance value at risk. Thus insurance companies are persuaded to sell collision policy and represent an accurate rate to calculate premium. So value at risk can provide an instruction for more profitability in insurance companies.
Value at Risk (VaR), Autoregressive Conditional Heteroskedasticity (ARCH), Car Insurance, Heavy Tail Distribution
