Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty
The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses a...
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MDPI AG
2025-04-01
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| author | Lyubov Doroshenko Loretta Mastroeni Alessandro Mazzoccoli |
| author_facet | Lyubov Doroshenko Loretta Mastroeni Alessandro Mazzoccoli |
| author_sort | Lyubov Doroshenko |
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| description | The analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses and wavelet energy-based measures to investigate the relationship between these indices and commodity prices across multiple time scales. The wavelet approach captures complex, time-varying dependencies, offering a more nuanced understanding of how uncertainty indices influence commodity price fluctuations. By integrating this analysis with predictability measures, we assess how uncertainty indices enhance forecasting accuracy. We further incorporate deep learning models capable of capturing sequential patterns in financial time series into our analysis to better evaluate their predictive potential. Our findings highlight the varying impact of financial and economic uncertainty on the predictability of commodity prices, showing that while some indices offer valuable forecasting information, others display strong correlations without significant predictive power. These results underscore the need for tailored predictive models, as different commodities react differently to the same financial conditions. By combining wavelet-based measures with machine learning techniques, this study presents a comprehensive framework for evaluating the role of uncertainty in commodity markets. The insights gained can support investors, policymakers, and market analysts in making more informed decisions. |
| format | Article |
| id | doaj-art-1981fb87b0d6410b9c0b8b7f18af8a32 |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-1981fb87b0d6410b9c0b8b7f18af8a322025-08-20T02:28:24ZengMDPI AGMathematics2227-73902025-04-01138134610.3390/math13081346Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial UncertaintyLyubov Doroshenko0Loretta Mastroeni1Alessandro Mazzoccoli2Department of Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, ItalyDepartment of Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, ItalyDepartment of Economics, Roma Tre University, Via Silvio D’Amico 77, 00145 Rome, ItalyThe analysis of commodity markets—particularly in the energy and metals sectors—is essential for understanding economic dynamics and guiding decision-making. Financial and economic uncertainty indices provide valuable insights that help reduce price uncertainty. This study employs wavelet analyses and wavelet energy-based measures to investigate the relationship between these indices and commodity prices across multiple time scales. The wavelet approach captures complex, time-varying dependencies, offering a more nuanced understanding of how uncertainty indices influence commodity price fluctuations. By integrating this analysis with predictability measures, we assess how uncertainty indices enhance forecasting accuracy. We further incorporate deep learning models capable of capturing sequential patterns in financial time series into our analysis to better evaluate their predictive potential. Our findings highlight the varying impact of financial and economic uncertainty on the predictability of commodity prices, showing that while some indices offer valuable forecasting information, others display strong correlations without significant predictive power. These results underscore the need for tailored predictive models, as different commodities react differently to the same financial conditions. By combining wavelet-based measures with machine learning techniques, this study presents a comprehensive framework for evaluating the role of uncertainty in commodity markets. The insights gained can support investors, policymakers, and market analysts in making more informed decisions.https://www.mdpi.com/2227-7390/13/8/1346wavelet analysispredictabilityenergy-based measurecommoditiesdeep learning |
| spellingShingle | Lyubov Doroshenko Loretta Mastroeni Alessandro Mazzoccoli Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty Mathematics wavelet analysis predictability energy-based measure commodities deep learning |
| title | Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty |
| title_full | Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty |
| title_fullStr | Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty |
| title_full_unstemmed | Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty |
| title_short | Wavelet and Deep Learning Framework for Predicting Commodity Prices Under Economic and Financial Uncertainty |
| title_sort | wavelet and deep learning framework for predicting commodity prices under economic and financial uncertainty |
| topic | wavelet analysis predictability energy-based measure commodities deep learning |
| url | https://www.mdpi.com/2227-7390/13/8/1346 |
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