|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|104032||2018||85 صفحه PDF||سفارش دهید||20179 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Banking & Finance, Available online 22 February 2018
A practical problem for energy companies is instituting a consistent framework across its supply and trading activities to deliver on all-important P&L and at-Risk reporting requirements. With a focus on storage assets and wider natural gas market exposures, we present a gas storage valuation methodology, which uniquely uses a flexible multifactor LÃ©vy process setting that allows for consistent valuation and risk management reporting across a general derivative book. Our approach is capable of replicating the complex covariance structure of the natural gas forward curve and capturing time spread volatility, a key driver of extrinsic storage value, while being simultaneously capable of accurately calibrating to market traded options. We begin by extending a single factor Mean Reverting Variance Gamma process to an arbitrary number of dimensions and, by way of specific examples, show how the traditional Principal Component Analysis based view of gas forward curve dynamics can be incorporated into a primarily market based valuation. We develop in the process an innovative implied moments based calibration technique, which allows for efficient calibration of general multifactor forward curve models to delivery period options common in energy and commodity markets. Furthermore, to accommodate the forward curve and traded options market consistency, we propose an appropriate joint market based calibration and historical estimation methodology. Through a formal model specification analysis, we provide evidence that the multifactor LÃ©vy models we propose provide a better joint fit to NBP natural gas options-forward market data, relative to comparative benchmark models. Finally, we develop a novel multidimensional fast Fourier transform based storage valuation algorithm and provide empirical evidence that the multifactor LÃ©vy model suite is better specified to more accurately capture extrinsic value.