A market consistent gas storage modelling framework: valuation, calibration, & model risk

A typical natural gas derivatives book within an energy trading business, bank, or even large utility will generally be exposed to two broad categories of market risk. The first being outright price volatility, where contracts such as caps/floors, options and swing, will have a non-linear exposure to the variability of the gas price level. The second, although equally as prominent, is time-spread volatility where gas storage, take-or-pay contracts, and calendar spread options will be exposed to the realized variability of different time-spreads. Developing a market consistent valuation framework capable of capturing both risk exposures, and thus allowing for risk diversification within a natural gas trading book, is the primary goal of this thesis. To accomplish this, we present a valuation methodology which is capable of pricing the two most actively traded natural gas derivative contracts, namely monthly options and storage, in a consistent manner. The valuation of the former is of course trivial as the prices are set by the market, therefore the primary focus of this thesis is in obtaining market-based pricing measures for the purpose of storage valuation. A consistent pricing and risk management framework will, by definition, accurately reflect the cost of hedging both outright and spread volatility and thus our work can be viewed as a basis capable of incorporating the other less actively traded contracts listed above. Further, we develop a methodology for estimating the model risk for general energy derivative pricing models. Such an analysis is a necessary pre-requisite to a model being used to manage the risk associated with a derivatives business. We begin by introducing the modeling framework and valuation methodology used throughout this thesis. Whilst there is general agreement on the main drivers of storage value, namely the volatility and correlation of the forward curve, there is no industry standard approach to price modeling. Models proposed in the literature to date focus heavily on replicating the statistical properties of the gas price and fail to address both the desire of storage traders to monetize their gamma exposure through hedging in the vanilla options market and also the constraint that they mark these products to observable volatilities. The primary goal of this work is to demonstrate how one can attain general consistency with the natural gas options market using Lévy-driven Ornstein-Uhlenbeck (OU) processes rather than traditional Gaussian models, and also analyze the impact of model choice on storage value. We provide a forward curve consistent Fourier-based pricing and calibration tool-kit which relies only upon knowledge of the conditional characteristic function of the underlying Lévy driven OU-process. Analytical solutions for such characteristic functions are generally not known and to date the only non-trivial example in the literature is the jump diffusion of Deng (2000). We derive a solution for the mean-reverting Variance Gamma process and demonstrate its effectiveness in both modeling the implied volatility smile and term structure present in the natural gas options market. One of the main benefits of choosing this process as the source of randomness in our modeling framework is parsimony. The model contains just a single parameter more than the more common mean-reverting diffusion model, and this parameter is entirely responsible for matching the implied volatility smile. We next move on to extending these results to a multidimensional setting in order to further capture the rich dynamics of the underlying forward curve whilst still maintaining consistency with the options market. The proposed framework is thus a specific case of the general Cheyette, Cheyette (2001), model class uniquely specified to meet the needs of a storage trader. Whereas the single factor modelling framework relies entirely upon the implied volatility surface to determine the level of extrinsic value accruing to a storage position, this more general family of models allows one to utilize other sources of information to estimate the value. We demonstrate how a traditional PCA based analysis can be used in informing model specification and provide several examples of such. We extend the Fourier based pricing and calibration methods developed for the single factor models to a multidimensional setting, and for the latter derive an efficient implied moment based calibration routine which is independent of the number of factors in the underlying model. We go on to provide numerical examples of storage valuations under a range of multifactor model specifications and also, analysis on how the model implied forward curve dynamics impact the value of storage. We finish by providing a storage model risk framework, with an emphasis on parameter risk, which will aid in analyzing the risk inherent in adopting this innovative market consistent modeling framework. The proposed approach extends the current model risk literature by providing a methodology for incorporating both market based model calibration and statistical parameter estimation in a consistent manner. The potential benefit of such a framework will impact equally trading, risk and regulatory stakeholders within a storage business, from model validation through to deriving appropriate bid-offer levels. We provide detailed numerical examples, based upon the models specified previously, demonstrating how model prices can be adjusted to incorporate model risk and also how different models can be ranked depending upon the model risk implicit in their estimation.