Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility

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dc.contributor.authorWeylandt, Michaelen_US
dc.contributor.authorHan, Yuen_US
dc.contributor.authorEnsor, Katherine B.en_US
dc.date.accessioned2020-09-11T01:24:30Zen_US
dc.date.available2020-09-11T01:24:30Zen_US
dc.date.issued2019en_US
dc.description.abstractFinancial markets for Liquified Natural Gas (LNG) are an important and rapidly-growing segment of commodities markets. Like other commodities markets, there is an inherent spatial structure to LNG markets, with different price dynamics for different points of delivery hubs. Certain hubs support highly liquid markets, allowing efficient and robust price discovery, while others are highly illiquid, limiting the effectiveness of standard risk management techniques. We propose a joint modeling strategy, which uses high-frequency information from thickly-traded hubs to improve volatility estimation and risk management at thinly-traded hubs. The resulting model has superior in- and out-of-sample predictive performance, particularly for several commonly used risk management metrics, demonstrating that joint modeling is indeed possible and useful. To improve estimation, a Bayesian estimation strategy is employed and data-driven weakly informative priors are suggested. Our model is robust to sparse data and can be effectively used in any market with similar irregular patterns of data availability.en_US
dc.format.extent65 ppen_US
dc.identifier.citationWeylandt, Michael, Han, Yu and Ensor, Katherine B.. "Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility." (2019) SSRN: http://dx.doi.org/10.2139/ssrn.3425531.en_US
dc.identifier.doihttp://dx.doi.org/10.2139/ssrn.3425531en_US
dc.identifier.urihttps://hdl.handle.net/1911/109332en_US
dc.language.isoengen_US
dc.publisherSSRNen_US
dc.titleMultivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatilityen_US
dc.typepapers (documents)en_US
dc.type.dcmiTexten_US
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