 Financial risk management innovation in energy market: Evidence from a machine learning hybrid modelZepei Li, Feng Ma and Xinjie Lu Energy Economics, 2025, vol. 144, issue C Abstract: This study employs a novel hybrid machine learning model that combines principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) methods. It explored the relationships between 19 kinds of commodities and 14 international stock market indices, taking the volatility of international stock market indices as a predictive factor. We discover that the LASSO-PCA model has significant predictive power for the energy market. Furthermore, through the analysis of different special periods (such as periods of high and low volatility, the COVID-19 pandemic, and the Russia-Ukraine conflict), it is verified that the model can still stably predict the energy market in various market environments. This research result showcases the application value of machine learning methods in analyzing the energy market, which is of great significance for financial risk management innovation and investor decision-making in the energy market. Keywords: Risk management; Financial innovation; Energy market; Machine learning; LASSO (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001847 DOI: 10.1016/j.eneco.2025.108360 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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