This paper proposes AR–GARCH–type–EVT model with various innovations based on Value–at–Risk (VaR) and Conditional Value–at–Risk (CVaR) for energy price risk quantification for different emerging energy markets. We assess the models of best fit, AR–EGARCH–EVT and AR–TGARCH–EVT models forPowernext and European Energy Exchange, respectively. Extreme Value theory (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 of Value–at–Risk and Conditional Value–at–Risk via EVT on both the lower and the upper tails of the return distribution in order to capture the extreme events of the distribution.This study also contributes to the literature in by analyzing both the upper and the lower tails because of the different perspective of regulators and investors present in the energy market.
Estimation of risk measures on electricity markets with fat tailed distributions
GROSSI, Luigi;
2015-01-01
Abstract
This paper proposes AR–GARCH–type–EVT model with various innovations based on Value–at–Risk (VaR) and Conditional Value–at–Risk (CVaR) for energy price risk quantification for different emerging energy markets. We assess the models of best fit, AR–EGARCH–EVT and AR–TGARCH–EVT models forPowernext and European Energy Exchange, respectively. Extreme Value theory (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 of Value–at–Risk and Conditional Value–at–Risk via EVT on both the lower and the upper tails of the return distribution in order to capture the extreme events of the distribution.This study also contributes to the literature in by analyzing both the upper and the lower tails because of the different perspective of regulators and investors present in the energy market.
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