Extreme Value Statistical Methods Applied Research, In The Calculation Of Value At Risk

The core of financial risk management is to measure the risk quantitatively.In recent years,among various methods of financial risk measurement,VaR(Value at Risk) receives the attention of the financial world most.More and more banks and other financial institutions have already adopted VaR and ES(Expected Loss) as a prediction and an important index of preventing and controlling financial risks. In order to measureVaR and ES accurately,the statistical distribution must be described. In the normal condition, the financial data is credible, the estimated VaR is accurate. But in the abnormal condition,the trustworthy data can not be got and the estimated VaR is not accurate.Financial risk is as sociated with low-probability eventsin the tails of asset price distribution. To capture the behavior of these tails, one should therefore Rely on models that explicitly focus on the ta ils.Extremev alue theory-based models do exactly it.Extreme Value Theory is used to analysis the extreme values of random vectors and processes by the statistic methods.The classic extreme value theory requests that series is independent and has identical distribution. But in reality this is not the case. The adjacent excess are not independent for enough threshold was found by research, The trouble was brought about maximum likelihood estimation.Therefore,This paper introduces the extremal index under the assumption that that the series is stationary, builds a GPD model by using the method of declustering,and then calculates the estimates of VaR and ES.This is a Innovation point in the paper.In the end of paper,we choose data from 1972 to 2008 the JPY/USA exchange rate as analytic to carry on the analysis of real example and with help of Matlaband R Statistical software proved the rationality of the improved model.This method can improve the accuracy for estimating VaR value and has important reference value and directive significance to financial institutions and individual investors employing VaR to control the market risk.