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Forecasting the Price of Gold

Author

Listed:
  • Hossein Hassani

    () (The Statistical Research Centre, Bournemouth University, UK)

  • Emmanuel Sirimal Silva

    (The Statistical Research Centre, Bournemouth University, UK)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Mawuli K. Segnon

    () (Christian-Albrechts-University Kiel, Department of Economics, 24098, Kiel, Germany)

Abstract

This paper seeks to evaluate the appropriateness of a variety of existing forecasting techniques (17 methods) at providing accurate, and statistically significant forecasts for gold price. We report the results from the 9 most competitive techniques. Special consideration is given to the ability of these techniques at providing forecasts which outperforms the random walk as we noticed that certain multivariate models (which included prices of silver, platinum, palladium and rhodium, besides gold) were also unable to outperform the random walk in this case. Interestingly, the results show that none of the forecasting techniques are able to outperform the random walk at horizons of 1 and 9 steps ahead, and on average the Exponential Smoothing model is seen providing the best forecasts in terms of the lowest root mean squared error over the 24 months forecasting horizons. Moreover, we find that the univariate models used in this paper are able to outperform the Bayesian autoregression, and Bayesian vector autoregressive models, with exponential smoothing (ETS) reporting statistically significant results in comparison to the former models, and classical autoregressive and the vector autoregressive models in most cases.

Suggested Citation

  • Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2014. "Forecasting the Price of Gold," Working Papers 201428, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201428
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    References listed on IDEAS

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    1. repec:eee:phsmap:v:509:y:2018:i:c:p:121-139 is not listed on IDEAS
    2. Sahay, Arvind & Jaikumar, Saravana, 2016. "Does Pharmaceutical Price Regulation Result in Greater Access to Essential Medicines? Study of the impact of drug price control order on sales volume of drugs in India," IIMA Working Papers WP2016-02-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    3. Hoang, Thi Hong Van & Lahiani, Amine & Heller, David, 2016. "Is gold a hedge against inflation? New evidence from a nonlinear ARDL approach," Economic Modelling, Elsevier, vol. 54(C), pages 54-66.
    4. Hassani, Hossein & Silva, Emmanuel Sirimal & Gupta, Rangan & Das, Sonali, 2018. "Predicting global temperature anomaly: A definitive investigation using an ensemble of twelve competing forecasting models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 121-139.
    5. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, Open Access Journal, vol. 3(3), pages 1-20, August.
    6. Hassani, Hossein & Silva, Emmanuel Sirimal & Antonakakis, Nikolaos & Filis, George & Gupta, Rangan, 2017. "Forecasting accuracy evaluation of tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 112-127.
    7. Nikolaos Giannellis & Minoas Koukouritakis, 2018. "Gold Price and Exchange Rates: A Panel Smooth Transition Regression Model for the G7 Countries," Working Papers 1806, University of Crete, Department of Economics.
    8. O'Connor, Fergal A. & Lucey, Brian M. & Batten, Jonathan A. & Baur, Dirk G., 2015. "The financial economics of gold — A survey," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 186-205.
    9. Cristiana Tudor, 2016. "Predicting the Evolution of CO 2 Emissions in Bahrain with Automated Forecasting Methods," Sustainability, MDPI, Open Access Journal, vol. 8(9), pages 1-10, September.

    More about this item

    Keywords

    ARIMA; ETS; TBATS; ARFIMA; AR; VAR; BAR; BVAR; Random Walk; Gold; Forecast; Multivariate; Univariate;

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