Forecasting the price of gold using dynamic model averaging

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dc.contributor.author Aye, Goodness Chioma
dc.contributor.author Gupta, Rangan
dc.contributor.author Hammoudeh, Shawkat
dc.contributor.author Kim, Won Joong
dc.date.accessioned 2015-06-11T06:57:33Z
dc.date.available 2015-06-11T06:57:33Z
dc.date.issued 2015-10
dc.description.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) extracted from a recursive principal component analysis (PCA) 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. Our result is also robust to a version of the PCA that uses all the 28 potential predictors instead of categorizing them, as well as, alternative out-of-sample period. 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. en_ZA
dc.description.embargo 2016-10-30
dc.description.librarian hb2015 en_ZA
dc.description.uri http://www.sciencedirect.com en_ZA
dc.identifier.citation Aye, G, Gupta, R, Hammoudeh, S & Kim, WJ 2015, 'Forecasting the price of gold using dynamic model averaging', International Review of Financial Analysis, vol. 41, pp. 257-266. en_ZA
dc.identifier.issn 1057-5219 (print)
dc.identifier.issn 1873-8079 (online)
dc.identifier.other 10.1016/j.irfa.2015.03.010
dc.identifier.uri http://hdl.handle.net/2263/45456
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2015 Elsevier Inc. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in International Review of Financial Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Review of Financial Analysis, vol. 41, pp. 257-266, 2015. doi : 10.1016/j.irfa.2015.03.010. en_ZA
dc.subject Bayesian en_ZA
dc.subject State space models en_ZA
dc.subject Gold en_ZA
dc.subject Macroeconomic fundamentals en_ZA
dc.subject Forecasting en_ZA
dc.title Forecasting the price of gold using dynamic model averaging en_ZA
dc.type Postprint Article en_ZA


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