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A Note on Oil Price Shocks and the Forecastability of Gold Realized Volatility

Author

Listed:
  • Riza Demirer

    () (Department of Economics and Finance, Southern Illinois University Edwardsville, Edwardsville, IL 62026-1102, USA)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Christian Pierdzioch

    () (Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008 Hamburg, Germany)

  • Syed Jawad Hussain Shahzad

    () (Montpellier Business School, Montpellier, France and South Ural State University, Chelyabinsk, Russian Federation)

Abstract

We examine the predictive power of disentangled oil price shocks over gold market volatility via the heterogeneous autoregressive realized volatility (HAR-RV) model. Our in- and out-of-sample tests show that combining the information from both oil supply and demand shocks with the innovations associated with financial market risks improves the forecast accuracy of realized volatility of gold. While financial risk shocks are important on their own, including oil price shocks in the model provides additional forecasting power in out-of-sample tests. Compared to the benchmark HAR-RV model, the extended model with all the three shocks included outperforms, in a statistically significant manner, all other variants of the HAR-RV framework for short-, medium, and long-run forecasting horizons. The findings highlight the predictive power of cross-market information in commodities and suggest that disentangling supply and demand related factors associated with price shocks could help improve the accuracy of forecasting models.

Suggested Citation

  • Riza Demirer & Rangan Gupta & Christian Pierdzioch & Syed Jawad Hussain Shahzad, 2020. "A Note on Oil Price Shocks and the Forecastability of Gold Realized Volatility," Working Papers 202010, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:202010
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Oil Shocks; Risk Shocks; Gold; Realized Volatility; Forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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