Natural gas storage valuation via least squares Monte Carlo and support vector regression

Least squares Monte Carlo (LSMC) approaches represent a computationally efficient method for the valuation of natural gas storage facilities. LSMC methods are computationally tractable while they simultaneously allow for a decoupling of the price path simulation from the optimization of the decision vector. However, selecting the appropriate features using traditional regression techniques can be challenging, particularly when several factors of uncertainty are assumed to drive the price process. In this paper we analyze a natural gas storage contract using a two factor forward model whose parameters can be easily calibrated. For a forward curve derived from monthly averages of the NBP day-ahead contract from 2004 to 2009 we compute storage values based on a collection of spot price paths and price paths of a daily forward contract with a time to maturity of 30 days. We study the impact of additional pricing information in the form of a forward contract on the value of a gas storage facility. A comparison to the corresponding one factor model is also included in our experiments. Value function approximation is carried out by employing a kernel-based regression technique in the form of support vector machine regression (SVR). We report out-of-sample results by simulating the targets for the next stage. We also carry out a search in the space of SVR parameters to identify the appropriate parameters for our experiments. Applying a spot trading strategy we observe a higher storage value for the one factor model when compared to the corresponding two factor model. With respect to the two factor model we report that an approximation of the value function over both a spot and a forward contract increases storage value compared to a value function that is computed over a spot contract only.