Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning
Resources Policy
; 73:102162, 2021.
Article
in English
| ScienceDirect | ID: covidwho-1272699
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.
Full text:
Available
Collection:
Databases of international organizations
Database:
ScienceDirect
Language:
English
Journal:
Resources Policy
Year:
2021
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS