Journal
RESOURCES POLICY
Volume 73, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.resourpol.2021.102162
Keywords
Commodity markets; Precious metals; Energy markets; Agricultural markets; Machine learning; t-SNE
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Funding
- University of Economics Ho Chi Minh City, Vietnam
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This article applies unsupervised machine learning to visualize and interpret logarithmic returns and conditional volatility in commodity markets. Results show that returns-based clustering conforms closely to traditional boundaries between different types of commodities, while volatility-based clustering successfully identifies extreme market distress periods.
Unsupervised machine learning can interpret logarithmic returns and conditional volatility in commodity markets. This article applies machine learning in order to visualize and interpret log returns and conditional volatility in commodities trading. We emphasize two classes of unsupervised learning methods: clustering and manifold learning for the reduction of dimensionality. We source daily prices from September 18, 2000 through July 31, 2020, for precious metals, base metals), energy commodities and agricultural commodities. Our results highlight that at the very least, returns-based clusters conform more closely to traditional boundaries between precious metals, base metals, fuels, temperate-climate agricultural commodities, and tropical agricultural commodities. On the other hand, volatility-based clustering succeeds in identifying periods of extreme market distress, such as the global financial crisis of 2008-09 and the Covid-19 pandemic.
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