Calculation Of Value At Risk In Electricity Market By Extreme Value Theory And Bayes Estimation

Electric power industry is national basic industry. Its reform will influence development of our country. The reform of electricity market poses a considerable financial risk to the participator. How to manage and measure the financial risk in the competitive electricity market will be important aspect of study in the electricity market. The violent fluctuation of electricity price poses a considerable financial risk to power plants and grid corporations in the electricity market. Presently, the calculation of the Value at Risk in the electricity market is only limited to the study on such traditional calculating methods as historical simulation method, variance-covariance method and Monte Carlo method. In spite of these methods improve Value at Risk, but they have a lot of disadvantage. Historical Simulation method isn't agile. Variance-covariance method can only predict accurately the middle part, i.e. the normal part, of the distribution function. As for the extreme parts of the distribution function, the correlative information provided is enormously limited. Monte Carlo method needs expend a lot of money and manpower. The Extreme Value Theory, the focus of this paper, is the particular theory aiming at the distribution features of the extreme value of the order statistic. Its application to the calculation of Value at Risk will efficiently improve the calculating accuracy.The paper considers how to calculate Value at Risk by Extreme Value Theory and Bayes estimation. What's more, we also use MCMC algorithm in order to calculate parametric Bayes estimation. The approach combines Extreme Value Theory and Bayesian statistics with calculation of Value at Risk. So it can help investor adjust Value at Risk model by sample and prior information controlled. Then, this Value at Risk model efficiently reflects characteristic of financial market. Its application to the calculation of Value at Risk will efficiently help the investor make more correct investment decisionsNow we shall list our contribution to calculation of Value at Risk:At first, though calculating Value at Risk by Extreme Value Theory is very prevalent in traditional financial market, we can not find the method in electricity market. The paper will apply Extreme Value Theory to calculate Value at Risk in electricity.Secondly, we combine Bayes estimation and with calculation of Value at Risk. Of course, we also use MCMC algorithm and Gibbs sampler in order to calculate parametric Bayes estimation.