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

Simple item page
dc.contributor.authorWeylandt, Michael
dc.contributor.authorHan, Yu
dc.contributor.authorEnsor, Katherine B.
dc.date.accessioned2020-09-11T01:24:30Z
dc.date.available2020-09-11T01:24:30Z
dc.date.issued2019
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.
dc.format.extent65 pp
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.
dc.identifier.doihttp://dx.doi.org/10.2139/ssrn.3425531
dc.identifier.urihttps://hdl.handle.net/1911/109332
dc.language.isoeng
dc.publisherSSRN
dc.titleMultivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility
dc.typepapers (documents)
dc.type.dcmiText
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
SSRN-id3425531.pdf
Size:
5.01 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Center for Computational Finance and Economic Systems (CoFES)