Research On Value At Risk In Measuring Financial Market Risk And Its Applications

In recent years, a series of cases occured such that Barings Bank ,Southeast financial institutions and Long Term Capital Management companies and so on suffered huge losses even bankruptcy because of bearing the risk in market, which make not only the financial institutions but also the regulatory agencies increasingly pay attention to the management of market risk. In order to make risk management objective and scientific, it uses quantitative analysis technique, that is, largely uses mathematical statistics model to identify,measure and inspect ventures. VaR model is exactly such a technique, which currently has been widely accepted by this field of industry and adopted by a lot of financial institutions in the world. What VaR model measures is the most possible losses that the investment value suffers in certain period and under a given probability level. It is more practical and scientific than traditional risk measurement technology such as maturity,duration and standard error and so on. Just for this, VaR model is widely applied to financial risk control,financial supervision,the achievement evaluation of institutions and so on. This article primarily argues that the traditional VaR technique method is unpractical because it supposes the future changes of market factors follow the normal distribution, and a lot of articles and positive researches have suggested that financial data's actual distribution has much thicker tail and thinner waist, that is the so-called "heavy tail"distribution. This traditional VaR method usually underestimates the actual losses, especially the case is that if the credit degree is much higher, when investors make some high-ventured investments, and if he underestimates the risk that he will have to face, he will not be able to deal with the dangerous situation, and may runs the risk of bankruptcy. In order to solve the problem that normal distribution has not the character of "heavy tail", this article will respectively introduce the t distribution and hyperbolic distribution to VaR model, together with the positive research to suggest, the VaR numerical value that t distribution and hyperbolic distribution get doesn't underestimate the actual losses, so t distribution and hyperbolic distribution are more reliable. At the same time, compared the sample distribution with the normal distribution ,t distribution and hyperbolic distribution,we can see that t distribution and hyperbolic distribution have greatly improved the capability and trueness of combining the distribution of the financial data sample, so the application of t distribution and hyperbolic distribution to set the model of financial data can be more consistent with the practice.With the development of risk measurement technology,we find some disadvantage of VaR and come into a new method of CVaR.In this article,we compare them through coherent risk measurement and find each of them has its superiority.