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Does oil predict gold? A nonparametric causality-in-quantiles approach

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  • Shahbaz, Muhammad
  • Balcilar, Mehmet
  • Abidin Ozdemir, Zeynel

Abstract

This paper examines the predictive power of oil price for gold price using the novel nonparametric causality-in-quantiles testing approach. The study uses weekly data over the April 1983-August 2016 period for both the spot and 1-month to 12-month futures markets. The new approach, the causality-in-quantile, allows one to test for causality-in-mean and causality-in-variance when there may be no causality in the first moment but higher order interdependencies may exist. The tests are preferred over the linear Granger causality test that might be subject to misleading results due to misspecification. Contrary to no predictability results obtained under misspecified linear structure, the nonparametric causality-in-quantiles test shows that oil price has a weak predictive power for the gold price. Moreover, the causality-in-variance tests obtain strong support for the predictive capacity of oil for gold market volatility. The results underline the importance of accounting for nonlinearity in the analysis of causality from oil to gold.

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  • Shahbaz, Muhammad & Balcilar, Mehmet & Abidin Ozdemir, Zeynel, 2017. "Does oil predict gold? A nonparametric causality-in-quantiles approach," Resources Policy, Elsevier, vol. 52(C), pages 257-265.
    • Handle: RePEc:eee:jrpoli:v:52:y:2017:i:c:p:257-265
    DOI: 10.1016/j.resourpol.2017.03.004
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    More about this item

    Keywords

    Gold; oil; spot and futures markets; Quantile causality; C22; G15;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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