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

65 Pages Posted: 7 Aug 2019

Date Written: July 25, 2019

Abstract

Financial 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.

Suggested Citation

Weylandt, Michael and Han, Yu and Ensor, Katherine B., Multivariate Modeling of Natural Gas Spot Trading Hubs Incorporating Futures Market Realized Volatility (July 25, 2019). Available at SSRN: https://ssrn.com/abstract=3425531 or http://dx.doi.org/10.2139/ssrn.3425531

Michael Weylandt (Contact Author)

Rice University ( email )

6100 South Main Street
Houston, TX 77005-1892
United States

Yu Han

Rice University ( email )

6100 South Main Street
Houston, TX 77005-1892
United States

Katherine B. Ensor

Rice University ( email )

Department of Statistics, MS 138
Rice University
Houston, TX 77251-1892
United States
7133484687 (Phone)

HOME PAGE: http://www.stat.rice.edu/~kathy/

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