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

## TL;DR

This paper introduces a joint modeling approach using high-frequency data and Bayesian estimation to improve volatility prediction and risk management in LNG spot markets, especially for less liquid hubs.

## Contribution

It develops a novel multivariate model that leverages liquid hub data to enhance volatility estimates for illiquid hubs, with robust Bayesian estimation techniques.

## Key findings

- Model outperforms traditional methods in predictive accuracy.
- Effective in markets with sparse and irregular data.
- Applicable to other commodities with similar data patterns.

## 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.