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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 inner
parameters. 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 filter
electricity price series. In this way, an approximately independently and identically distributed residual series with better
statistical properties is acquired. Then extreme value theory is adopted to explicitly model the tails of the residuals of
GM(1,2) model, and accurate estimates of electricity market value-at-risk can be produced. The empirical analysis based
on the historical data of the PJM electricity market shows that the proposed model can be rapidly reflect the most recent
and relevant changes of electricity prices and can produce accurate forecasts of value-at-risk at all confidence levels, and
the computational cost is far less than the existing two-stage value-at-risk estimating models, further improving the ability
of risk management for electricity market participants.