IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v48y2016icp282-291.html
   My bibliography  Save this article

A melting pot — Gold price forecasts under model and parameter uncertainty

Author

Listed:
  • Baur, Dirk G.
  • Beckmann, Joscha
  • Czudaj, Robert

Abstract

Gold is special as it is influenced by a wide range of factors such as commodity prices, interest rates, inflation expectations, exchange rate changes and stock market volatility. Hence, forecasting the price of gold is a difficult task and the main problem a researcher faces is to select the relevant regressors at each point in time. This model uncertainty in combination with parameter uncertainty is explicitly accounted for by Dynamic Model Averaging (DMA) which allows both the forecasting model and the coefficients to change over time. Based on this framework, we systematically evaluate a large set of possible gold price determinants and find that DMA (1) improves forecasts compared to other frameworks, (2) yields strong time-variation of gold price predictors and (3) favors parsimonious models. The results also show that typical in-sample features of gold such as its hedge property are weaker in an out-of-sample context.

Suggested Citation

  • Baur, Dirk G. & Beckmann, Joscha & Czudaj, Robert, 2016. "A melting pot — Gold price forecasts under model and parameter uncertainty," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 282-291.
    • Handle: RePEc:eee:finana:v:48:y:2016:i:c:p:282-291
    DOI: 10.1016/j.irfa.2016.10.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521916301612
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2016.10.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Blose, Laurence E., 2010. "Gold prices, cost of carry, and expected inflation," Journal of Economics and Business, Elsevier, vol. 62(1), pages 35-47, January.
    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. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    4. Barbara Rossi, 2005. "Testing Long-Horizon Predictive Ability With High Persistence, And The Meese-Rogoff Puzzle," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(1), pages 61-92, February.
    5. 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.
    6. Beckmann, Joscha & Czudaj, Robert, 2013. "Gold as an inflation hedge in a time-varying coefficient framework," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 208-222.
    7. Hossein Hassani & Emmanuel Sirimal Silva & Rangan Gupta & Mawuli K. Segnon, 2015. "Forecasting the price of gold," Applied Economics, Taylor & Francis Journals, vol. 47(39), pages 4141-4152, August.
    8. 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.
    9. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    10. 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.
    11. Mark, Nelson C, 1995. "Exchange Rates and Fundamentals: Evidence on Long-Horizon Predictability," American Economic Review, American Economic Association, vol. 85(1), pages 201-218, March.
    12. Thomas Deckers & Christoph Hanck, 2014. "Variable Selection in Cross-Section Regressions: Comparisons and Extensions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(6), pages 841-873, December.
    13. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    14. Pilbeam, Keith, 2001. "The profitability of central bank foreign exchange market intervention," Journal of Policy Modeling, Elsevier, vol. 23(5), pages 523-530, July.
    15. 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.
    16. Roy W. Jastram, 2009. "The Golden Constant," Books, Edward Elgar Publishing, number 12733.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bonato, Matteo & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2018. "Gold futures returns and realized moments: A forecasting experiment using a quantile-boosting approach," Resources Policy, Elsevier, vol. 57(C), pages 196-212.
    2. Dong, Xiyong & Yoon, Seong-Min, 2019. "What global economic factors drive emerging Asian stock market returns? Evidence from a dynamic model averaging approach," Economic Modelling, Elsevier, vol. 77(C), pages 204-215.
    3. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    4. Sui, Meng & Rengifo, Erick W. & Court, Eduardo, 2021. "Gold, inflation and exchange rate in dollarized economies – A comparative study of Turkey, Peru and the United States," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 82-99.
    5. Baur, Dirk G. & Beckmann, Joscha & Czudaj, Robert L., 2020. "The Relative Valuation Of Gold," Macroeconomic Dynamics, Cambridge University Press, vol. 24(6), pages 1346-1391, September.
    6. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
    7. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
    8. Pattnaik, Debidutta & Hassan, M. Kabir & DSouza, Arun & Ashraf, Ali, 2023. "Investment in gold: A bibliometric review and agenda for future research," Research in International Business and Finance, Elsevier, vol. 64(C).
    9. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    10. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.
    11. Dichtl, Hubert, 2020. "Forecasting excess returns of the gold market: Can we learn from stock market predictions?," Journal of Commodity Markets, Elsevier, vol. 19(C).
    12. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
    13. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    14. Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021. "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 5-28, January.
    15. Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices," Resources Policy, Elsevier, vol. 69(C).
    16. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.

    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. Baur, Dirk G. & Beckmann, Joscha & Czudaj, Robert, 2014. "Gold Price Forecasts in a Dynamic Model Averaging Framework – Have the Determinants Changed Over Time?," Ruhr Economic Papers 506, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    3. Baur, Dirk G. & Beckmann, Joscha & Czudaj, Robert L., 2020. "The Relative Valuation Of Gold," Macroeconomic Dynamics, Cambridge University Press, vol. 24(6), pages 1346-1391, September.
    4. Thi Hong Van Hoang & Amine Lahiani & David Heller, 2016. "Is gold a hedge against inflation? New evidence from a nonlinear ARDL approach," Post-Print hal-02012307, HAL.
    5. 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.
    6. Dirk G. Baur & Joscha Beckmann & Robert Czudaj, 2014. "Gold Price Forecasts in a Dynamic Model Averaging Framework – Have the Determinants Changed Over Time?," Ruhr Economic Papers 0506, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    7. repec:zbw:rwirep:0506 is not listed on IDEAS
    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. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    10. Domenico Ferraro & Kenneth S. Rogoff & Barbara Rossi, 2011. "Can oil prices forecast exchange rates?," Working Papers 11-34, Federal Reserve Bank of Philadelphia.
    11. Nguyen, Duc Binh Benno & Prokopczuk, Marcel & Wese Simen, Chardin, 2019. "The risk premium of gold," Journal of International Money and Finance, Elsevier, vol. 94(C), pages 140-159.
    12. Zhu, Yanhui & Fan, Jingwen & Tucker, Jon, 2018. "The impact of monetary policy on gold price dynamics," Research in International Business and Finance, Elsevier, vol. 44(C), pages 319-331.
    13. Engel, Charles & Wu, Steve Pak Yeung, 2023. "Forecasting the U.S. Dollar in the 21st Century," Journal of International Economics, Elsevier, vol. 141(C).
    14. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    15. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    16. Giannellis, Nikolaos & Koukouritakis, Minoas, 2019. "Gold price and exchange rates: A panel smooth transition regression model for the G7 countries," The North American Journal of Economics and Finance, Elsevier, vol. 49(C), pages 27-46.
    17. Fratzscher, Marcel & Rime, Dagfinn & Sarno, Lucio & Zinna, Gabriele, 2015. "The scapegoat theory of exchange rates: the first tests," Journal of Monetary Economics, Elsevier, vol. 70(C), pages 1-21.
    18. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    19. Nonejad, Nima, 2021. "Predicting equity premium using dynamic model averaging. Does the state–space representation matter?," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    20. Thomas Conlon & Brian M. Lucey & Gazi Salah Uddin, 2018. "Is gold a hedge against inflation? A wavelet time-scale perspective," Review of Quantitative Finance and Accounting, Springer, vol. 51(2), pages 317-345, August.
    21. Risse, Marian & Ohl, Ludwig, 2017. "Using dynamic model averaging in state space representation with dynamic Occam’s window and applications to the stock and gold market," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 158-176.

    More about this item

    Keywords

    Bayesian econometrics; Dynamic Model Averaging; Forecasting; Gold;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications

    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:eee:finana:v:48:y:2016:i:c:p:282-291. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.