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Forecasting the Price of Gold Using Dynamic Model Averaging

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Author Info

  • Goodness C. Aye

    ()
    (Department of Economics, University of Pretoria)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Shawkat Hammoudeh

    ()
    (Lebow College of Business, Drexel University, Philadelphia, USA)

  • Won Joong Kim

    ()
    (Department of Economics, Konkuk University, Seoul, Korea)

Abstract

We develop models for examining possible predictors of the return on gold that embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price factors) and two uncertainty indices (the Kansas City Fed’s financial stress index and the U.S. Economic uncertainty index). Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only a linear model (such as random walk) but also the Bayesian model averaging (BMA) model for examining possible predictors of the return of gold. The DMS is the best overall across all forecast horizons. Generally, all the predictors show strong predictive power at one time or another though at varying magnitudes, while the exchange rate factor and the Kansas City Fed’s financial stress index appear to be strong at almost all horizons and sub-periods. However, the forecasting prowess of the exchange rate is supreme.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201415.

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Length: 31 pages
Date of creation: Apr 2014
Date of revision:
Handle: RePEc:pre:wpaper:201415

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Keywords: Bayesian; state space models; macroeconomic fundamentals; forecasting;

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References

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  1. Koop, Gary & Korobilis, Dimitris, 2011. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," SIRE Discussion Papers 2011-39, Scottish Institute for Research in Economics (SIRE).
  2. Baur, Dirk G. & Glover, Kristoffer J., 2014. "Heterogeneous expectations in the gold market: Specification and estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 116-133.
  3. Sjaastad, Larry A. & Scacciavillani, Fabio, 1996. "The price of gold and the exchange rate," Journal of International Money and Finance, Elsevier, vol. 15(6), pages 879-897, December.
  4. Ciner, Cetin & Gurdgiev, Constantin & Lucey, Brian M., 2013. "Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 202-211.
  5. Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong & Simo-Kengne, Beatrice D., 2014. "Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 170-189.
  6. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-89, December.
  7. 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, 08.
  8. Geweke, John & Amisano, Gianni, 2007. "Hierarchical Markov normal mixture models with applications to financial asset returns," Working Paper Series 0831, European Central Bank.
  9. Sjaastad, Larry A., 2008. "The price of gold and the exchange rates: Once again," Resources Policy, Elsevier, vol. 33(2), pages 118-124, June.
  10. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, School of Economics and Management, University of Aarhus.
  11. Apergis, Nicholas, 2014. "Can gold prices forecast the Australian dollar movements?," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 75-82.
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Cited by:
  1. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta, 2014. "Forecasting the Price of Gold," Working Papers 201428, University of Pretoria, Department of Economics.

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