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Forecasting the Price of Gold Using Dynamic Model Averaging

  • 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)

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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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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Web page: http://www.up.ac.za/economics

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