Drilling Deeper: Non-Linear, Non-Parametric Natural Gas Price and Volatility Forecasting

Dusan Bajatovic, Deniz Erdemlioglu and Nikola Gradojevic

The Energy Journal, 2024, vol. 45, issue 4, 1-25

Abstract: This paper studies the forecast accuracy and explainability of a battery of dayahead (Henry Hub and Title Transfer Facility (TTF)) natural gas price and volatility models. The results demonstrate the dominance of non-linear, non-parametric models with deep structure relative to various competing model specifications. By employing the explainable artificial intelligence (XAI) approach, we document that the price of natural gas is formed strategically based on crude oil and electricity prices. While the conditional volatility of natural gas returns is driven by long-memory dynamics and crude oil volatility, the informativeness of the electricity predictor has improved over the most recent volatile time period. Although we reveal that predictive non-linear relationships are inherently complex and time-varying, our findings in general support the notion that natural gas, crude oil and electricity are interconnected. Focusing on the periods when markets experienced sharp structural breaks and extreme volatility (e.g., the COVID-19 pandemic and the Russia-Ukraine conflict), we show that deep learning models provide better adaptability and lead to significantly more accurate forecast performance.

Keywords: Natural gas; Forecasting; Volatility; Energy commodities; Neural networks; Deep learning; Machine learning (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/01956574241277302 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:enejou:v:45:y:2024:i:4:p:1-25

DOI: 10.1177/01956574241277302

Access Statistics for this article

More articles in The Energy Journal
Bibliographic data for series maintained by SAGE Publications ().