Electricity auction market risk analysis based on EGARCH-EVT-CVaR model

In the competitive power market of generation side, the bidding strategies with taking into account the profit and risk are essential for generation companies. This paper proposes a dynamic risk model of bidding strategy of generation companies based on the EGARCH-EVT-CVaR method. In this model, the tail of return is modeled by the extreme value theory (EVT). The EGARCH model is used to achieve auto-regression weekly and seasonally in both the conditional mean and conditional volatility of return as well as leverage effect. In addition, the conditional value at risk (CVaR) is adopted as a risk measurement tool. Taking the California electricity market as an example, the price return at the same hour in every day is modeled. The empirical analysis results show that the proposed EGARCH-EVT-based model rationally forecasts dynamic VaR and CVaR in the electricity auction market. In addition, the results indicate that the proposed model is a useful technique for generation companies to deal with market risks.