Forecasting the term structure of commodities future prices using machine learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42521-022-00069-3.pdf
Reference49 articles.
1. Aiube, F. A. L. (2013). Modelos Quantitativos em Finanças Com Enfoque em Commodities. Bookman Editora.
2. Aiube, F. A. L., Baidya, T. K. N., & Tito, E. A. H. (2008). Analysis of commodity prices with the particle filter. Energy Economics, 30(2), 597–605. https://doi.org/10.1016/j.eneco.2006.06.006
3. Aiube, F. A. L., & Samanez, C. P. (2014). On the comparison of Schwartz and Smith’s two- and three-factor models on commodity prices. Applied Economics, 46(30), 3736–3749. https://doi.org/10.1080/00036846.2014.939409
4. Baruník, J., & Malinská, B. (2016). Forecasting the term structure of crude oil futures prices with neural networks. Applied Energy, 164, 366–379. https://doi.org/10.1016/j.apenergy.2015.11.051
5. Bhar, R., & Lee, D. (2011). Time-varying market price of risk in the crude oil futures market. Journal of Futures Markets, 31(8), 779–807. https://doi.org/10.1002/fut.20493
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