Working Paper: NBER ID: w23685
Authors: Francis X. Diebold; Laura Liu; Kamil Yilmaz
Abstract: We use variance decompositions from high-dimensional vector autoregressions to characterize connectedness in 19 key commodity return volatilities, 2011-2016. We study both static (full-sample) and dynamic (rolling-sample) connectedness. We summarize and visualize the results using tools from network analysis. The results reveal clear clustering of commodities into groups that match traditional industry groupings, but with some notable differences. The energy sector is most important in terms of sending shocks to others, and energy, industrial metals, and precious metals are themselves tightly connected.
Keywords: Commodity markets; Connectedness; Risk management; Emerging economies
JEL Codes: G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| fluctuations in commodity prices (Q02) | common business cycle fluctuations (E32) |
| shocks in one commodity (Q02) | volatilities of other commodities (Q02) |
| energy price volatility (Q47) | volatilities of industrial metals (L61) |
| energy price volatility (Q47) | volatilities of precious metals (G13) |
| degree of connectedness among commodities varies (Q02) | time (C41) |
| external economic factors (F69) | degree of connectedness among commodities (Q02) |