Clustering and Characterizing Intersectoral Relationships in the US Economy Through Complex Networks

Abstract

This paper seeks to characterize the relevance and cluster of the intersectoral relationships of the US economy through complex networks. To this end, 21 sectoral assets are considered and their volatility transmissions and receipts are estimated to generate complex network metrics with the Force Atlas 2 algorithm. The results indicate that the most influential assets in the network are the Wilshire 5000, the S&P 500, and the DJIA Industrial. In contrast, the least influential assets are the CRB commodities, WTI crude oil, and the 2-year note. Three clusters are identified, cluster 0 focuses on commodities, especially crude oil, and includes assets that are influenced by commodity markets and that exhibit similar behavioral patterns. Cluster 1 includes fixed-income instruments, such as government bonds with varying maturities, which respond to fluctuations in interest rates and macroeconomic factors. Cluster 2 is composed of assets characterized by greater volatility across several sectors, including assets in the industrial, materials, energy, technology, healthcare, and utilities sectors, highlighting their diversified portfolio and varied market exposures.