Modeling and forecasting commodity market volatility with long-term economic and financial variables

Duc Khuong Nguyen and Thomas Walther

MPRA Paper from University Library of Munich, Germany

Abstract: This paper investigates the time-varying volatility patterns of some major commodities as well as the potential factors that drive their long-term volatility component. For this purpose, we make use of a recently proposed GARCH-MIDAS approach which typically allows us to examine the role of economic and financial variables of different frequencies. Using commodity futures for crude oil (WTI and Brent), gold, silver and platinum, our results show the necessity of disentangling the short- and long-term components in modeling and forecasting commodity volatility. They also indicate that the long-term volatility of most commodity futures is significantly driven by the level of the general real economic activity as well as the changes in consumer sentiment, industrial production, and economic policy uncertainty. However, the forecasting results are not alike across commodity futures as no single model fits all commodities.

Keywords: Commodity futures; GARCH; Long-term volatility; Macroeconomic effects; Mixed data sampling. (search for similar items in EconPapers)
JEL-codes: C52 C53 G17 (search for similar items in EconPapers)
Date: 2017-05, Revised 2018-01
New Economics Papers: this item is included in nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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https://mpra.ub.uni-muenchen.de/84464/1/MPRA_paper_84464.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/90348/9/MPRA_paper_90348.pdf revised version (application/pdf)

Related works:
Journal Article: Modeling and forecasting commodity market volatility with long‐term economic and financial variables (2020)
Working Paper: Modeling and Forecasting Commodity Market Volatility with Long-term Economic and Financial Variables (2018)
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