Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty DOI Creative Commons
Lyubov Doroshenko, Loretta Mastroeni, Alessandro Mazzoccoli

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(8), P. 1346 - 1346

Published: April 20, 2025

The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics guiding decision-making. Financial uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses energy-based measures to investigate relationship between these prices across multiple time scales. approach captures complex, time-varying dependencies, offering a more nuanced how influence fluctuations. By integrating this with predictability measures, we assess enhance forecasting accuracy. We further incorporate deep learning models capable capturing sequential patterns financial series into our better evaluate their predictive potential. Our findings highlight varying impact on prices, showing while some offer information, others display strong correlations without significant power. These results underscore need tailored models, as different commodities react differently same conditions. combining wavelet-based machine techniques, presents comprehensive framework evaluating role markets. gained can support investors, policymakers, market analysts making informed decisions.

Language: Английский

Wavelet Entropy for Efficiency Assessment of Price, Return, and Volatility of Brent and WTI During Extreme Events DOI Creative Commons
Salim Lahmiri

Commodities, Journal Year: 2025, Volume and Issue: 4(2), P. 4 - 4

Published: March 21, 2025

This study analyzes the market efficiency of crude oil markets, namely Brent and West Texas Intermediate (WTI), during three different periods: pre-COVID-19, COVID-19 pandemic, ongoing Russia–Ukraine military conflict. To evaluate wavelet entropy is computed from price, return, volatility series. Our empirical results show that WTI prices are predictable conflict, but difficult to predict same period. The were pandemic. Returns in more conflict than they Finally, carried information pandemic compared Also, series for These findings offer insightful investors, traders, policy makers relation energy under various extreme conditions.

Language: Английский

Citations

0
Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty DOI Creative Commons
Lyubov Doroshenko, Loretta Mastroeni, Alessandro Mazzoccoli

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(8), P. 1346 - 1346

Published: April 20, 2025

The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics guiding decision-making. Financial uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses energy-based measures to investigate relationship between these prices across multiple time scales. approach captures complex, time-varying dependencies, offering a more nuanced how influence fluctuations. By integrating this with predictability measures, we assess enhance forecasting accuracy. We further incorporate deep learning models capable capturing sequential patterns financial series into our better evaluate their predictive potential. Our findings highlight varying impact on prices, showing while some offer information, others display strong correlations without significant power. These results underscore need tailored models, as different commodities react differently same conditions. combining wavelet-based machine techniques, presents comprehensive framework evaluating role markets. gained can support investors, policymakers, market analysts making informed decisions.

Language: Английский

Citations

0