Estimate And Applications Of VaR Based On The Model Of The G-h

Many financial institutions generate huge loss because of mismanagement, the main reason is lack of effective risk measurable tool, so market risk can not be assessed well. It is visible that effective assessment of market risk is particularly important. There are several market risk measurement tools, VaR is one of popular methods of financial market risk management, it denotes maximal possible loss of a financial asset or portfolio in a certain holding period and under the confident level. We can find fraction matching with confidence according to statistical distribution of the changes of portfolio value, i.e VaR. Generally, we suppose that interests series of the financial asset or portfolio is normal distribution, but lots of empirical evidences show that return rate of asset is non-normal and has obvious peak fat-tail features. Extreme value theory(EVT) can handle peak fat-tailed features well, but this method has many defects, such as complicated calculations and subjectivity of the threshold’s selection. For these deficiencies of extreme value theory, we use the g-h distribution to derive the VaR calculation formula of portfolio based on the g-h distribution in this paper and compare with extreme value theory. By empirical analysis, results show that VaR method based on g-h more accurately describe the risk of interests series and that the calculation results is better than the VaR estimation based on the extreme value theory.