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Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies

Author

Listed:
  • Shawkat M. Hammoudeh

    (Lebow College of Business, Drexel University)

  • Yuan Yuan

    (Lebow College of Business, Drexel University)

  • Michael McAleer

    (Erasmus School of Economics, Erasmus University Rotterdam and Tinbergen Institute and Department of Economics and Finance, University of Canterbury)

Abstract

This paper examines the inclusion of the dollar/euro exchange rate together with four important and highly traded commodities - aluminum, copper, gold and oil- in symmetric and asymmetric multivariate GARCH and DCC models. The inclusion of exchange rate increases the significant direct and indirect past shock and volatility effects on future volatility between the commodities in all the models. Model 2, which includes the business cycle industrial metal copper and not aluminum, displays more direct and indirect transmissions than does Model 3, which replaces the business cycle-sensitive copper with the highly energy-intensive aluminum. The asymmetric effects are the greatest in Model 3 because of the high interactions between oil and aluminum. Optimal portfolios should have more euro currency than commodities, and more copper and gold than oil.

Suggested Citation

  • Shawkat M. Hammoudeh & Yuan Yuan & Michael McAleer, 2010. "Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies," CIRJE F-Series CIRJE-F-741, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2010cf741
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    References listed on IDEAS

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    Cited by:

    1. Mohamad, Sharifah Fairuz Syed & Masih, Mansur, 2013. "An application of MGARCH-DCC analysis on selected currencies in terms of gold Price," MPRA Paper 62349, University Library of Munich, Germany.
    2. Mohamad, Sharifah Fairuz Syed & Masih, Mansur, 2013. "Gold price movements in selected currencies: wavelet approach," MPRA Paper 62347, University Library of Munich, Germany.
    3. Ahmadi, Maryam & Bashiri Behmiri, Niaz & Manera, Matteo, 2016. "How is volatility in commodity markets linked to oil price shocks?," Energy Economics, Elsevier, vol. 59(C), pages 11-23.
    4. Halova Wolfe, Marketa & Rosenman, Robert, 2014. "Bidirectional causality in oil and gas markets," Energy Economics, Elsevier, vol. 42(C), pages 325-331.
    5. Andi Duqi & Leonardo Franci & Giuseppe Torluccio, 2014. "The Black-Litterman model: the definition of views based on volatility forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 24(19), pages 1285-1296, October.

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    More about this item

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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