Estimating Commodity Term Structure Volatilities

This chapter describes two methods commonly used in estimating the volatility term structure in energy and commodity markets. The first tool estimates the parameters of a spot price-convenience yield model using the Kalman filter. The chapter also describes the method on the Gibson-Schwartz model. An application is given for West Texas Intermediate (WTI) crude oil. The chapter presents a case study in which the volatility term structure generated by the model and the empirical term structure are shown to match closely. The second tool captures the risk factors of price movements using a data reduction technique. The methodology is based on Principal components analysis (PCA) and allows reducing the dimensionality of the data. This is achieved by transforming into new variables that are uncorrelated and ordered in terms of their contribution. A case study on WTI and Henry Hub futures is presented, showcasing the strength of this estimation tool.