The spate of resent derivatives disasters has focused the attention of the industry on the need to better control financial risks. This search has led to a uniform measure of risk called Value at Risk(VaR) as the expected worst loss over a given horizon at a given confidence interval. The simplicity of the VaR concept has led many to recommend that it become a standard risk measure, not only for financial institutions involved in large-scale trading operations, but also for retail banks, insurance companies, institutional investors and nonfinancial concerns.
This thesis explains the concept of VaR, and then describe in detail the three methods for computing it: historical simulation: the variance-covariance method: and Monte-Carlo simulation. Finally this thesis discusses the advantages and disadvantages of the three methods for computing VaR.
