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Forecasting the price of gold using dynamic model averaging

Author

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

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.

Suggested Citation

  • Aye, Goodness & Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong, 2015. "Forecasting the price of gold using dynamic model averaging," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 257-266.
  • Handle: RePEc:eee:finana:v:41:y:2015:i:c:p:257-266
    DOI: 10.1016/j.irfa.2015.03.010
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    References listed on IDEAS

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    1. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    2. Charles Engel & Kenneth D. West, 2005. "Exchange Rates and Fundamentals," Journal of Political Economy, University of Chicago Press, vol. 113(3), pages 485-517, June.
    3. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, August.
    4. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
    5. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    6. Koop, Gary & Korobilis, Dimitris, 2011. "UK macroeconomic forecasting with many predictors: Which models forecast best and when do they do so?," Economic Modelling, Elsevier, vol. 28(5), pages 2307-2318, September.
    7. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2014. "The international business cycle and gold-price fluctuations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 292-305.
    8. Sjaastad, Larry A. & Scacciavillani, Fabio, 1996. "The price of gold and the exchange rate," Journal of International Money and Finance, Elsevier, vol. 15(6), pages 879-897, December.
    9. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    10. Gupta, Rangan & Hammoudeh, Shawkat & Kim, Won Joong & Simo-Kengne, Beatrice D., 2014. "Forecasting China's foreign exchange reserves using dynamic model averaging: The roles of macroeconomic fundamentals, financial stress and economic uncertainty," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 170-189.
    11. Dirk G. Baur & Thomas K.J. McDermott, 2011. "Safe Haven Assets and Investor Behaviour Under Uncertainty," The Institute for International Integration Studies Discussion Paper Series iiisdp392, IIIS, revised Feb 2012.
    12. Wright, Jonathan H., 2008. "Bayesian Model Averaging and exchange rate forecasts," Journal of Econometrics, Elsevier, vol. 146(2), pages 329-341, October.
    13. Shafiee, Shahriar & Topal, Erkan, 2009. "When will fossil fuel reserves be diminished?," Energy Policy, Elsevier, vol. 37(1), pages 181-189, January.
    14. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
    15. Philippe Bacchetta & Eric Van Wincoop, 2006. "Can Information Heterogeneity Explain the Exchange Rate Determination Puzzle?," American Economic Review, American Economic Association, vol. 96(3), pages 552-576, June.
    16. Pindyck, Robert S & Rotemberg, Julio J, 1990. "The Excess Co-movement of Commodity Prices," Economic Journal, Royal Economic Society, vol. 100(403), pages 1173-1189, December.
    17. Sjaastad, Larry A., 2008. "The price of gold and the exchange rates: Once again," Resources Policy, Elsevier, vol. 33(2), pages 118-124, June.
    18. Apergis, Nicholas, 2014. "Can gold prices forecast the Australian dollar movements?," International Review of Economics & Finance, Elsevier, vol. 29(C), pages 75-82.
    19. Ciner, Cetin & Gurdgiev, Constantin & Lucey, Brian M., 2013. "Hedges and safe havens: An examination of stocks, bonds, gold, oil and exchange rates," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 202-211.
    20. Capie, Forrest & Mills, Terence C. & Wood, Geoffrey, 2005. "Gold as a hedge against the dollar," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(4), pages 343-352, October.
    21. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    22. Baur, Dirk G. & Glover, Kristoffer J., 2014. "Heterogeneous expectations in the gold market: Specification and estimation," Journal of Economic Dynamics and Control, Elsevier, vol. 40(C), pages 116-133.
    23. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M, 2014. "On the economic determinants of the gold–inflation relation," Resources Policy, Elsevier, vol. 41(C), pages 101-108.
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    More about this item

    Keywords

    Bayesian; State space models; Gold; Macroeconomic fundamentals; Forecasting;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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