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Forecasting the price of gold using dynamic model averaging
Journal article   Open access   Peer reviewed

Forecasting the price of gold using dynamic model averaging

Goodness Aye, Rangan Gupta, Shawkat Hammoudeh and Won Joong Kim
International review of financial analysis, v 41, pp 257-266
Oct 2015
url
http://hdl.handle.net/2263/45456View

Abstract

Bayesian Forecasting Gold Macroeconomic fundamentals State space models
We develop several models to examine possible predictors of the return of gold, which embrace six global factors (business cycle, nominal, interest rate, commodity, exchange rate and stock price) extracted from a recursive principal component analysis (PCA) and two uncertainty and stress indices (the Kansas City Fed's financial stress index and the U.S. economic policy uncertainty index). Specifically, by comparing alternative predictive models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform linear models (such as the random walk) as well as the Bayesian model averaging (BMA) model. The DMS is the best predictive model 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. •This paper develops models for examining predictors of gold return that embrace six global factors and two uncertain indices.•Global factors include business cycle, nominal, interest rate, commodity, exchange rate and stock price factors.•Uncertain indices include the Kansas City Fed's financial stress index and the U.S. Economic uncertainty index.•Results show that the dynamic model selection (DMS) is the best overall across all forecast horizons.•The forecasting prowess of the exchange rate is supreme.

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102 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Business, Finance
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