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Gold Price Forecasts in a Dynamic Model Averaging Framework – Have the Determinants Changed Over Time?

Author

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  • Baur, Dirk G.
  • Beckmann, Joscha
  • Czudaj, Robert

Abstract

The price of gold is influenced by a wide range of local and global factors such as commodity prices, interest rates, inflation expectations, exchange rate changes and stock market volatility among others. Hence, forecasting the price of gold is a notoriously difficult task and the main problem a researcher faces is to select the relevant regressors at each point in time. This combination of model and parameter uncertainty is explicitly accounted for by Dynamic Model Averaging which allows both the forecasting model and the coefficients to change over time. Based on this framework, we systematically evaluate a large set of possible gold price determinants and use both the predictive likelihood and the mean squared error as a measure of the forecasting performance. We carefully assess which predictors are relevant for forecasting at different points in time through the posterior probability. Our findings show that (1) DMA improves forecasts compared to other frameworks and (2) provides clear evidence for the time-variation of gold price predictors.

Suggested Citation

  • Baur, Dirk G. & Beckmann, Joscha & Czudaj, Robert, 2014. "Gold Price Forecasts in a Dynamic Model Averaging Framework – Have the Determinants Changed Over Time?," Ruhr Economic Papers 506, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:506
    DOI: 10.4419/86788581
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    References listed on IDEAS

    as
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    4. Beckmann, Joscha & Czudaj, Robert, 2013. "Gold as an inflation hedge in a time-varying coefficient framework," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 208-222.
    5. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
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    8. Capie, Forrest & Mills, Terence C. & Wood, Geoffrey, 2005. "Gold as a hedge against the dollar," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(4), pages 343-352, October.
    9. Roy W. Jastram, 2009. "The Golden Constant," Books, Edward Elgar Publishing, number 12733.
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    Citations

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    Cited by:

    1. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
    2. Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    3. O'Connor, Fergal A. & Lucey, Brian M. & Batten, Jonathan A. & Baur, Dirk G., 2015. "The financial economics of gold — A survey," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 186-205.
    4. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "A real-time quantile-regression approach to forecasting gold returns under asymmetric loss," Resources Policy, Elsevier, vol. 45(C), pages 299-306.
    5. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    6. Baur, Dirk G. & Beckmann, Joscha & Czudaj, Robert L., 2020. "The Relative Valuation Of Gold," Macroeconomic Dynamics, Cambridge University Press, vol. 24(6), pages 1346-1391, September.
    7. Salisu, Afees A. & Ndako, Umar B. & Oloko, Tirimisiyu F., 2019. "Assessing the inflation hedging of gold and palladium in OECD countries," Resources Policy, Elsevier, vol. 62(C), pages 357-377.
    8. Arnold, Stephan & Auer, Benjamin R., 2015. "What do scientists know about inflation hedging?," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 187-214.
    9. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.

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    More about this item

    Keywords

    Bayesian econometrics; dynamic model averaging; forecasting; gold;
    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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

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