Tail Risk of Electricity Futures

TL;DR

This study evaluates models for predicting the tail risk of electricity futures across European markets, highlighting the AR-GARCH model with Student-t distribution as the most effective, especially for the left tail, with implications for market regulation.

Contribution

It compares in-sample and out-of-sample tail risk models for electricity futures, identifying the AR-GARCH (1,1) with Student-t as the best performer across multiple markets.

Findings

AR-GARCH (1,1) with Student-t performs best for VaR estimation.

Model passes adequacy tests in all markets for Expected Shortfall.

Right tail modeling is more challenging than the left tail.

Abstract

This paper compares the in-sample and out-of-sample performance of several models for computing the tail risk of one-month and one-year electricity futures contracts traded in the NordPool, French, German, and Spanish markets in 2008-2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk (VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model with Student-t distribution is the best-performing specification with 88% cases in which the Fisher test accepts the model, with a success rate of 94% in the left tail and of 81% in the right tail. The model passes the test of model adequacy in the 100% of the cases in the NordPool and German markets, but only in the 88% and 63% of the cases in the Spanish and French markets. With ES, this model passes the test of model adequacy in 100% of cases in all markets. Historical Simulation and Quantile Regression-based approaches misestimate tail risks. The right-hand tail of the returns is more difficult to model than the left-hand tail and therefore financial regulators and the administrators of futures markets should take these results into account when setting additional regulatory capital requirements and margin account regulations to short positions.