IDEAS home Printed from https://ideas.repec.org/p/pre/wpaper/201428.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alysha M De Livera & Rob J Hyndman, 2009. "Forecasting time series with complex seasonal patterns using exponential smoothing," Monash Econometrics and Business Statistics Working Papers 15/09, Monash University, Department of Econometrics and Business Statistics.
    2. 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.
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    4. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    5. Hossein Hassani & Saeed Heravi & Anatoly Zhigljavsky, 2013. "Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(5), pages 395-408, August.
    6. 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.
    7. Emmanuel Sirimal Silva & Hossein Hassani, 2015. "On the use of singular spectrum analysis for forecasting U.S. trade before, during and after the 2008 recession," International Economics, CEPII research center, issue 141, pages 34-49.
    8. repec:ipg:wpaper:2014-470 is not listed on IDEAS
    9. 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.
    10. repec:cii:cepiie:2015-q1-141-30 is not listed on IDEAS
    11. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    12. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    13. Hassani, Hossein & Heravi, Saeed & Zhigljavsky, Anatoly, 2009. "Forecasting European industrial production with singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 25(1), pages 103-118.
    14. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    15. Hossein Hassani & Abdol S. Soofi & Anatoly Zhigljavsky, 2013. "Predicting inflation dynamics with singular spectrum analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 743-760, June.
    16. Sims, Christopher A & Stock, James H & Watson, Mark W, 1990. "Inference in Linear Time Series Models with Some Unit Roots," Econometrica, Econometric Society, vol. 58(1), pages 113-144, January.
    17. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    18. repec:cii:cepiei:2015-q1-141-3 is not listed on IDEAS
    19. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    20. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    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. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
    23. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:ipg:wpaper:2014-480 is not listed on IDEAS
    2. 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.
    3. 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.
    4. Rangan Gupta & Alain Kabundi & Stephen Miller & Josine Uwilingiye, 2014. "Using large data sets to forecast sectoral employment," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(2), pages 229-264, June.
    5. Hossein Hassani & Zara Ghodsi & Rangan Gupta & Mawuli Segnon, 2017. "Forecasting Home Sales in the Four Census Regions and the Aggregate US Economy Using Singular Spectrum Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 49(1), pages 83-97, January.
    6. Silva, Emmanuel Sirimal & Ghodsi, Zara & Ghodsi, Mansi & Heravi, Saeed & Hassani, Hossein, 2017. "Cross country relations in European tourist arrivals," Annals of Tourism Research, Elsevier, vol. 63(C), pages 151-168.
    7. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    8. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    9. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    10. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    11. Das, Sonali & Gupta, Rangan & Kabundi, Alain, 2009. "Could we have predicted the recent downturn in the South African housing market?," Journal of Housing Economics, Elsevier, vol. 18(4), pages 325-335, December.
    12. Pami Dua & Nishita Raje & Satyananda Sahoo, 2008. "Forecasting Interest Rates in India," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 2(1), pages 1-41, March.
    13. Andrea Nobili, 2005. "Forecasting Output Growth And Inflation In The Euro Area: Are Financial Spreads Useful?," Temi di discussione (Economic working papers) 544, Bank of Italy, Economic Research and International Relations Area.
    14. repec:ipg:wpaper:2014-473 is not listed on IDEAS
    15. Plakandaras, Vasilios & Gupta, Rangan & Gogas, Periklis & Papadimitriou, Theophilos, 2015. "Forecasting the U.S. real house price index," Economic Modelling, Elsevier, vol. 45(C), pages 259-267.
    16. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, June.
    17. Hossein Hassani & Jan Coreman & Saeed Heravi & Joshy Easaw, 2018. "Forecasting Inflation Rate: Professional Against Academic, Which One is More Accurate," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(3), pages 631-646, September.
    18. Hossein Hassani & Emmanuel Sirimal Silva, 2015. "A Kolmogorov-Smirnov Based Test for Comparing the Predictive Accuracy of Two Sets of Forecasts," Econometrics, MDPI, vol. 3(3), pages 1-20, August.
    19. Huang, Xu & Hassani, Hossein & Ghodsi, Mansi & Mukherjee, Zinnia & Gupta, Rangan, 2017. "Do trend extraction approaches affect causality detection in climate change studies?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 604-624.
    20. Peter C.B. Phillips, 1992. "Bayes Methods for Trending Multiple Time Series with an Empirical Application to the US Economy," Cowles Foundation Discussion Papers 1025, Cowles Foundation for Research in Economics, Yale University.
    21. Colino, Evelyn V. & Irwin, Scott H. & Garcia, Philip, 2008. "How Much Can Outlook Forecasts be Improved? An Application to the U.S. Hog Market," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37620, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    22. Helmut Lütkepohl, 2013. "Vector autoregressive models," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 6, pages 139-164, Edward Elgar Publishing.

    More about this item

    Keywords

    ARIMA; ETS; TBATS; ARFIMA; AR; VAR; BAR; BVAR; Random Walk; Gold; Forecast; Multivariate; Univariate;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pre:wpaper:201428. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rangan Gupta (email available below). General contact details of provider: https://edirc.repec.org/data/decupza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.