Abstract
In this study, we aim to build better risk models for energy commodities by employing statistical procedures to identify outliers in the prices for all crude oil and natural gas futures contracts traded on the CME over the period of December 2003 through March 2017. Our results show that it is important to investigate and control for potential outlier effects when performing parametric estimation of risk parameters because outliers can have a large impact on the estimation of value at risk. We illustrate for actual crude oil and natural gas contract how the use of value at risk metrics based on raw data can lead to higher than expected actual losses. Our research demonstrates that it is crucial to include intervention parameters to address outlier impacts in order to obtain robust risk metrics.
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