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

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  • Nguyen, Duc Khuong
  • Walther, Thomas

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.

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  • Nguyen, Duc Khuong & Walther, Thomas, 2017. "Modeling and forecasting commodity market volatility with long-term economic and financial variables," MPRA Paper 84464, University Library of Munich, Germany, revised Jan 2018.
  • Handle: RePEc:pra:mprapa:84464
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    More about this item

    Keywords

    Commodity futures; GARCH; Long-term volatility; Macroeconomic effects; Mixed data sampling.;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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