Estimation of risk measures on electricity markets with fat-tailed distributions

This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH)-type extreme value theory (EVT) model with various innovations based on value-at-risk (VaR) and conditional value-at-risk (CVaR) for energy price risk quantification in different emerging energy markets.We assess the best-fitting AR-exponential GARCH-EVT and AR-threshold GARCH-EVT models for Powernext and the European Energy Exchange, respectively. EVT is adopted explicitly to model the tails of the return distribution in order to capture extremal events. One of the main contributions of this paper is the estimation ofVaR and CVaR via EVT on the lower and upper tails of the return distribution in order to capture the extreme events of the distribution. This paper also contributes to the literature by analyzing both the upper and lower tails, in order to satisfy the different perspectives of regulators and investors in the energy market.