Measuring risk based on skewed t distribution approach

Abstract

This paper analyses a Skewed t Distribution approach to estimate Value at Risk (VaR) as a tool that can measure a risk investment. The method can estimate an investment risk that can overcome the shortcoming of classical VaR, which cannot capture the existence of fat tail and skewness. The application of the method was utilized to evaluate the individual risk of four stocks taken from the NYSE Index, namely Advance Micro Devices Inc (AMD), The Coca-Cola Company (KO), Pfizer Inc. (PFE), and Walmart Inc (WMT). It can be summarized from the result of the analysis that VaR (in several confidence levels) based on the distribution approach is powerful in risk measurement and can give an alternative to the investor for estimating the risk.