• Estimating Value at Risk in Financial Assets; A Parametric and Non Parametric Approach

Abstract

Value at Risk (VaR) which is introduced in the recent decades is one of the main concepts used to estimate market risk, at the same time it estimate the expected loss too.  In this paper we examine the parametric and nonparametric method of estimating Value at Risk and in parametric method we assume the normality of stock prices although it is non-normal. We discussed how the VaR value is less in parametric method compared to non parametric method. Here we justify our result by showing the normal probability curve. Also we compared our result with Monte Carlo simulation. With the existing methods we applied Kernal smoothing and GARCH Fit to the financial return series and estimated the VaR. Non parametric with Kernal smoothing gives less VaR compared to all other methods.