Value-at-risk in the European energy market: a comparison of parametric, historical simulation and quantile regression value-at-risk

Sjur Westgaard; Gisle Hoel Århus; Marina Frydenberg

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In this paper, we estimate value-at-risk models for the European Energy Markets at ICE, EEX and Nasdaq OMX.
We investigate value-at-risk for both short and long positions at several tail quantiles for crude oil, coal, natural gas and electricity markets.
Quantile regression performs best regarding unconditional and conditional coverage compared to Riskmetrics and Historical Simulation. This is because energy markets have conditional return distribution that change over time, which needs to be captured in risk modelling.

Abstract

This paper examines a set of value-at-risk (VaR) models and their ability to appropriately describe and capture price-change risk in the European energy market. We make in-sample, one-day-ahead VaR forecasts using one simple parametric model, one historical simulation model and one quantile regression (QR) model. We apply our models to nine different energy futures: Brent crude oil, API2 coal, UK natural gas, and three German and Nordic power futures in the period 2007–17. The models are tested at both long and short positions. Our research suggests that the QR model is easy to implement and offers accurate VaR forecasts in the European energy market.

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