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Gas storage valuation under multifactor Lévy processes

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dc.contributor.author Cummins, Mark
dc.contributor.author Kiely, Greg
dc.contributor.author Murphy, Bernard
dc.date.accessioned 2018-10-11T13:47:43Z
dc.date.issued 2018
dc.identifier.uri http://hdl.handle.net/10344/7218
dc.description peer-reviewed en_US
dc.description The full text of this article will not be available in ULIR until the embargo has expired on the 21/02/21
dc.description.abstract 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 Lévy 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 Lévy 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 Lévy model suite is better specified to more accurately capture extrinsic value. en_US
dc.language.iso eng en_US
dc.publisher Elsevier en_US
dc.relation.ispartofseries Journal of Banking and Finance;95, pp. 167-184
dc.relation.uri https://doi.org/10.1016/j.jbankfin.2018.02.012
dc.rights This is the author’s version of a work that was accepted for publication in Journal of Banking and Finance. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Banking and Finance, 2018, 95, pp. 167-184, en_US
dc.subject gas storage valuation en_US
dc.subject multifactor lévy processes en_US
dc.subject multifactor Lévy processes en_US
dc.subject mean reverting variance gamma processes en_US
dc.subject implied moments calibration en_US
dc.subject fast fourier transform en_US
dc.title Gas storage valuation under multifactor Lévy processes en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1016/j.jbankfin.2018.02.012
dc.date.embargoEndDate 2021-02-25
dc.embargo.terms 2021-02-21 en_US
dc.rights.accessrights info:eu-repo/semantics/embargoedAccess en_US


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