Commodity Dynamics: A Sparse Multi-class Approach

TL;DR

This paper proposes a sparse multi-class VAR model to identify key commodity price effects across markets and portfolios, revealing significant cross-commodity influences and asymmetries, especially involving metals and energy.

Contribution

It introduces a novel sparse estimator for multi-class VAR models, enabling detection of common commodity price effects across multiple markets and portfolios.

Findings

Metal commodities have strong effects in Chinese and Indian markets.

Energy significantly influences agricultural commodities in investment portfolios.

Commodity effects are more similar across portfolios than across markets.

Abstract

The correct understanding of commodity price dynamics can bring relevant improvements in terms of policy formulation both for developing and developed countries. Agricultural, metal and energy commodity prices might depend on each other: although we expect few important effects among the total number of possible ones, some price effects among different commodities might still be substantial. Moreover, the increasing integration of the world economy suggests that these effects should be comparable for different markets. This paper introduces a sparse estimator of the Multi-class Vector AutoRegressive model to detect common price effects between a large number of commodities, for different markets or investment portfolios. In a first application, we consider agricultural, metal and energy commodities for three different markets. We show a large prevalence of effects involving metal commodities in the Chinese and Indian markets, and the existence of asymmetric price effects. In a second application, we analyze commodity prices for five different investment portfolios, and highlight the existence of important effects from energy to agricultural commodities. The relevance of biofuels is hereby confirmed. Overall, we find stronger similarities in commodity price effects among portfolios than among markets.