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On the Risk Comovements between the Crude Oil Market and the U.S. Dollar Exchange Rates

Author

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  • Gilles de Truchis

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)

  • Benjamin Keddad

    (Aix-Marseille University (Aix-Marseille School of Economics), CNRS & EHESS)

Abstract

This article examines the volatility dependence between the crude oil price and four US dollar exchange rates using both fractional cointegration and copula techniques. The former exploits the long memory behavior of the volatility processes to investigate whether they are tied through a common long-run equilibrium. The latter is complementary as it allows to explore whether the volatility of the markets are linked in the short run. The cointegration results conclude in favor of long-run independence for the Canadian and Japan exchange rates while few evidence of long-run dependence are found for the European and British exchange rates. Concerning the copula analysis, we conclude in favor of weak dependence when we consider static copulas. Considering time-varying copulas, it appears that dependence is sensitive to market conditions as we found increasing linkages just before the 2008 market collapse and more recently, in the aftermath of the European debt crisis.

Suggested Citation

  • Gilles de Truchis & Benjamin Keddad, 2014. "On the Risk Comovements between the Crude Oil Market and the U.S. Dollar Exchange Rates," AMSE Working Papers 1421, Aix-Marseille School of Economics, France, revised May 2014.
  • Handle: RePEc:aim:wpaimx:1421
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    More about this item

    Keywords

    comovement; volatility linkage; Fractional cointegration; copula; oil market; exchange rate;
    All these keywords.

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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