Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning

James Ming Chen, Mobeen Ur Rehman and Xuan Vinh Vo

Resources Policy, 2021, vol. 73, issue C

Abstract: 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.

Keywords: Commodity markets; Precious metals; Energy markets; Agricultural markets; Machine learning; t-SNE (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721001768

DOI: 10.1016/j.resourpol.2021.102162

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