  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.
