The Roles of Global Supply Chain Pressure and Economic Conditions in Forecasting the VaR of Commodity Markets: A Quantile GARCH-MIDAS Approach

Zhangying Li (), O-Chia Chuang (), Rangan Gupta () and Elie Bouri ()
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Zhangying Li: Economics and Management School, Wuhan University
O-Chia Chuang: School of Digital Economics, Hubei University of Economics
Rangan Gupta: Department of Economics, University of Pretoria
Elie Bouri: School of Business, Lebanese American University, Lebanon

No 202528, Working Papers from University of Pretoria, Department of Economics

Abstract: Accurately predicting the Value-at-Risk (VaR) in commodity markets is crucial for risk management, yet the volatility and cyclicality of commodity prices pose significant challenges. This paper innovatively incorporates the information content of the Global Supply Chain Pressure Index (GSCPI) and the Global Economic Conditions Index (GECON) into the quantile Genaralized Autoregressive Conditional Heteroskedasticty-Mixed Data Sampling (GARCH-MIDAS) framework to address the issue of mismatched data frequencies, and explores the impact of these monthly indicators on daily commodity returns volatility. We find that the MIDAS framework significantly outperforms the conditional autoregressive VaR by regression quantiles (CAViaR) model, with asymmetric models showing superior performance. Both GSCPI and GECON exhibit strong explanatory power for VaR forecasting, highlighting the important influence of global supply and demand conditions on returns volatility of the overall commodity market, as well as its various sub-sectors.

Keywords: VaR predictions; Quantiles-based mixed-frequency models; Commodity market (search for similar items in EconPapers)
JEL-codes: C32 C53 E23 E32 Q02 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2025-08
New Economics Papers: this item is included in nep-for and nep-rmg
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