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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 University Rotterdam, Tinbergen Institute, The Netherlands, and Institute of Economic Research, Kyoto University)

Abstract

This paper examines volatility, volatility spillovers, optimal portfolio weights and hedging for systems that include the dollar/euro exchange rate together with four important and highly traded commodities - aluminum, copper, gold and oil - by utilizing four 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. The model that includes copper displays more direct and indirect transmissions than the one that includes aluminum which displays the high interactions with oil. Optimal portfolio weights suggest that investors should hold more of aluminum, copper and gold and less of oil in those portfolios. Hedging ratios indicate that the most effective way of hedging long commodity and euro positions is shorting them with oil positions.

Suggested Citation

  • Shawkat M. Hammoudeh & Yuan Yuan & Michael McAleer, 2010. "Exchange Rate and Industrial Commodity Volatility Transmissions, Asymmetries and Hedging Strategies," KIER Working Papers 751, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:751
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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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    Keywords

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    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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