Forecasting the Price of Gold Using Dynamic Model Averaging

Goodness Aye (), Rangan Gupta, Shawkat Hammoudeh and Won Joong Kim ()
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Goodness Aye: Department of Economics, University of Pretoria
Won Joong Kim: Department of Economics, Konkuk University, Seoul, Korea

No 201415, Working Papers from University of Pretoria, Department of Economics

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.

Keywords: Bayesian; state space models; macroeconomic fundamentals; forecasting (search for similar items in EconPapers)
JEL-codes: C11 C53 F37 F47 Q02 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2014-04
New Economics Papers: this item is included in nep-for
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Citations: View citations in EconPapers (10)

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Journal Article: Forecasting the price of gold using dynamic model averaging (2015)
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