Multivariate models of commodity futures markets: a dynamic copula approach

Sihong Chen (), Qi Li (), Qiaoyu Wang () and Yu Yvette Zhang ()
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Sihong Chen: Amazon.com Inc
Qi Li: Texas A &M University
Qiaoyu Wang: Capital University of Economics and Business
Yu Yvette Zhang: Texas A &M University

Empirical Economics, 2023, vol. 64, issue 6, No 21, 3037-3057

Abstract: Abstract We apply flexible multivariate dynamic models to capture the dependence structure of various US commodity futures across different sectors between 2004 and 2022; particular attention is paid to the 2008 financial crisis and the COVID-19 pandemic. Our copula-based models allow for time-varying nonlinear and asymmetric dependence by integrating elliptical and skewed copulas with dynamic conditional correlation (DCC) and block dynamic equicorrelation (Block DECO). Flexible copula models that allow for multivariate asymmetry and tail dependence are found to provide the best performance in characterizing co-movements of commodity returns. We also find that the connectedness between commodities has dramatically increased during the financial distress and the COVID-19 pandemic. The impacts of the financial crisis appear to be more persistent than those of the pandemic. We apply our models to some risk management tasks in the commodity markets. Our results suggest that optimal portfolio weights based on dynamic copulas have persistently outperformed the equal-weighted portfolio, demonstrating the practicality and usefulness of our proposed models.

Keywords: Copula; Commodity futures; Dynamic correlation; Diversification benefit; Tail dependence; Financial crisis; Pandemic (search for similar items in EconPapers)
JEL-codes: G01 G10 Q02 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00181-023-02373-2

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