Estimating Value-at-Risk in Electricity Market Based on Grey ExtremeValue Theory

摘要

How to effectively evaluate price of volatility risk is the basis of risk management in electricity market.Electricity price connotes a grey system, due to uncertainty and incomplete information for partial external or innerparameters. A two-stage model for estimating value-at-risk based on grey system and extreme value theory is proposed.Firstly, in order to capture the dependencies, seasonalities and volatility-clustering, a GM(1,2) model is used to filterelectricity price series. In this way, an approximately independently and identically distributed residual series with betterstatistical properties is acquired. Then extreme value theory is adopted to explicitly model the tails of the residuals ofGM(1,2) model, and accurate estimates of electricity market value-at-risk can be produced. The empirical analysis basedon the historical data of the PJM electricity market shows that the proposed model can be rapidly reflect the most recentand relevant changes of electricity prices and can produce accurate forecasts of value-at-risk at all confidence levels, andthe computational cost is far less than the existing two-stage value-at-risk estimating models, further improving the abilityof risk management for electricity market participants.