In this study, we aim to improve inference capabilities of risk models for energy commodities by employing statistical procedures to identify outliers in the prices for 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 (VaR) and Expected Shortfall (CVaR or ES). We illustrate using crude oil and natural gas futures contracts how risk metrics based on raw data can lead to higher than expected actual losses. As a result, a firm may be placing itself unknowingly at precarious financial risk. Our research demonstrates that it is crucial to include intervention parameters to address outlier impacts in order to obtain robust risk metrics. Outlier intervention models will allow manager in firms with trading operations and financial services to make more informed decisions in regards to risk management, credit management, governance and compliance activities.