Forecasting the price of gold using dynamic model averaging

Goodness Aye, Rangan Gupta, Shawkat Hammoudeh and Won Joong Kim

International Review of Financial Analysis, 2015, vol. 41, issue C, 257-266

Abstract: 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.

Keywords: Bayesian; State space models; Gold; Macroeconomic fundamentals; Forecasting (search for similar items in EconPapers)
JEL-codes: C11 C53 F37 F47 Q02 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (71)

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Working Paper: Forecasting the Price of Gold Using Dynamic Model Averaging (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:41:y:2015:i:c:p:257-266

DOI: 10.1016/j.irfa.2015.03.010

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