Modeling and Forecasting Commodity Market Volatility with Long-term Economic and Financial Variables
Thomas Walther and
Duc Khuong Nguyen
No 1824, Working Papers on Finance from University of St. Gallen, School of Finance
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 as well as a commodity index, our results show the necessity of disentangling the short-term 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 global 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: C58 G17 Q02 (search for similar items in EconPapers)
Pages: 41 pages
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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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Persistent link: https://EconPapers.repec.org/RePEc:usg:sfwpfi:2018:24
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