Centrality of Copper Prices Revealed by Network Analysis Based on the Return Characteristics of Various Commodity Futures Prices

In this research, I conducted a network analysis to elucidate the relationship between the return characteristics of commodity futures prices and macroeconomic trends. First, I examined the time series characteristics of 19 types of commodity futures prices and classified them into four clusters using a time series clustering method based on TimeSeriesKMeans. Furthermore, I performed a network analysis using a correlation matrix based on the weekly returns of each commodity to identify commodities exhibiting central behaviors. By constructing a network and analyzing it using centrality measures (degree centrality, eigenvector centrality, betweenness centrality, and closeness centrality), I found that copper exhibited the highest centrality across all metrics. Additionally, this research showed a correlation analysis between copper futures prices and the Composite Leading Indicators (CLI) of various OECD countries. The results indicated that copper futures prices had a higher correlation with the CLI compared to other commodity prices, particularly showing significant correlation with the CLI of countries such as the United States and Germany. Considering the extensive industrial applications of copper in production activities, it can be inferred that macroeconomic conditions, including consumption activities and demand forecasts, are reflected in the futures prices of commodities centered around copper.