Var Comparative Study Of Some Typical Methods Of Measurement

Currently, we can use a lot of models to estimate value-at-risk, but different models often deduce different VaR values. Risk managers are therefore often left with the daunting task of choosing from a plethora of models. This paper brings about an accuracy evaluate method which combined theories analysis and empirical analysis together. And furthermore this paper compares and tests six typical methods and get out the models' series in descending order based on superiority.The Value at risk (VaR) is the maximum expected loss over a given horizon period at a given level of confidence. A crucial factor for the accuracy of VaR models that are based on the parametric approach is the measure of volatility. In this paper, 4 parametric models are compared, they are equal weighted moving average model (EQMA); exponentially weighted moving average model (EWMA); GARCH model; exponential GARCH (EGARCH) model. In addition, historical simulation (HS) and extreme value theory based model (POT) are used and compared here. We compare the estimations in an application to daily returns on the Nasdaq index.The test processes are carried out in two steps: the lst step is to test the statistical validity of all the 6 methods and the 2nd step is to use the loss function evaluation to evaluate the performance and testing for superiority of the models. The empirical results of the study show that at 95% confidence level, POT is the best then followed by HS, EQMA, EWMA, GARCH, and EGARCH should be rejected; at 99% confidence level, POT is the best, then followed by HS, EQMA, EGARCH, GARCH, and EWMA should be rejected.The EVT-based method POT is recommended to estimate VaR when samples arelarge.