IDEAS home Printed from https://ideas.repec.org/r/eee/finana/v41y2015icp257-266.html
   My bibliography  Save this item

Forecasting the price of gold using dynamic model averaging

Citations

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


Cited by:

  1. 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.
  2. Robert A. Hill & Paulo M. M. Rodrigues, 2022. "Forgetting approaches to improve forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1356-1371, November.
  3. Behnamian, Mehdi & Shojaee, Abdul Nasser & Haji, Gholamali, 2021. "Investigating the Effective Factors in the Growth of Private Sector Investment in Iran," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 7(4), pages 84-57, February.
  4. 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.
  5. Han, Liyan & Xu, Yang & Yin, Libo, 2017. "Does investor attention matter? The attention-return relation in gold futures market," Economics Discussion Papers 2017-37, Kiel Institute for the World Economy (IfW Kiel).
  6. 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.
  7. Jan Prüser, 2019. "Adaptive learning from model space," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 29-38, January.
  8. repec:ipg:wpaper:2014-546 is not listed on IDEAS
  9. 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.
  10. Chen, Yi-Ting & Liu, Chu-An, 2023. "Model averaging for asymptotically optimal combined forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 592-607.
  11. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A quantile-boosting approach to forecasting gold returns," The North American Journal of Economics and Finance, Elsevier, vol. 35(C), pages 38-55.
  12. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
  13. Yu Zhao & Xi Zhang & Zhongshun Shi & Lei He, 2017. "Grain Price Forecasting Using a Hybrid Stochastic Method," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(05), pages 1-24, October.
  14. Madziwa, Lawrence & Pillalamarry, Mallikarjun & Chatterjee, Snehamoy, 2022. "Gold price forecasting using multivariate stochastic model," Resources Policy, Elsevier, vol. 76(C).
  15. Gupta, Rangan & Majumdar, Anandamayee & Pierdzioch, Christian & Wohar, Mark E., 2017. "Do terror attacks predict gold returns? Evidence from a quantile-predictive-regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 276-284.
  16. Yang Zhao & Charalampos Stasinakis & Georgios Sermpinis & Filipa Da Silva Fernandes, 2019. "Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(4), pages 1443-1463, October.
  17. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, vol. 11(5), pages 1-24, May.
  18. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
  19. 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.
  20. Zhang, Hongwei & Demirer, Riza & Huang, Jianbai & Huang, Wanjun & Tahir Suleman, Muhammad, 2021. "Economic policy uncertainty and gold return dynamics: Evidence from high-frequency data," Resources Policy, Elsevier, vol. 72(C).
  21. Semeyutin, Artur & Downing, Gareth, 2022. "Co-jumps in the U.S. interest rates and precious metals markets and their implications for investors," International Review of Financial Analysis, Elsevier, vol. 81(C).
  22. 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.
  23. Mohammad Reza Yeganegi & Hossein Hassani & Rangan Gupta, 2023. "The ENSO cycle and forecastability of global inflation and output growth: Evidence from standard and mixed‐frequency multivariate singular spectrum analyses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1690-1707, November.
  24. Perry Sadorsky, 2021. "Predicting Gold and Silver Price Direction Using Tree-Based Classifiers," JRFM, MDPI, vol. 14(5), pages 1-21, April.
  25. 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.
  26. Cheng, Xian & Wu, Peng & Liao, Stephen Shaoyi & Wang, Xuelian, 2023. "An integrated model for crude oil forecasting: Causality assessment and technical efficiency," Energy Economics, Elsevier, vol. 117(C).
  27. Donghua Wang & Yang Xin & Xiaohui Chang & Xingze Su, 2021. "Realized volatility forecasting and volatility spillovers: Evidence from Chinese non‐ferrous metals futures," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2713-2731, April.
  28. Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2024. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 487-513, July.
  29. Liu, Jing & Wei, Yu & Ma, Feng & Wahab, M.I.M., 2017. "Forecasting the realized range-based volatility using dynamic model averaging approach," Economic Modelling, Elsevier, vol. 61(C), pages 12-26.
  30. Ender Demir & Giray Gozgor, 2016. "The Impact Of Economic Policy Uncertainty On The Vehicle Miles Traveled (Vmt) In The U.S," Eurasian Journal of Business and Management, Eurasian Publications, vol. 4(3), pages 39-48.
  31. 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.
  32. repec:ipg:wpaper:2014-516 is not listed on IDEAS
  33. 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.
  34. Apergis, Nicholas & Eleftheriou, Sofia, 2016. "Gold returns: Do business cycle asymmetries matter? Evidence from an international country sample," Economic Modelling, Elsevier, vol. 57(C), pages 164-170.
  35. Dibooglu, Sel & Cevik, Emrah I. & Gillman, Max, 2022. "Gold, silver, and the US dollar as harbingers of financial calm and distress," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 200-210.
  36. Taicir Mezghani & Mouna Boujelbène Abbes, 2023. "Forecast the Role of GCC Financial Stress on Oil Market and GCC Financial Markets Using Convolutional Neural Networks," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 30(3), pages 505-530, September.
  37. Dong, Xiyong & Song, Li & Yoon, Seong-Min, 2021. "How have the dependence structures between stock markets and economic factors changed during the COVID-19 pandemic?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  38. Drachal, Krzysztof, 2019. "Forecasting prices of selected metals with Bayesian data-rich models," Resources Policy, Elsevier, vol. 64(C).
  39. Fang, Libing & Yu, Honghai & Xiao, Wen, 2018. "Forecasting gold futures market volatility using macroeconomic variables in the United States," Economic Modelling, Elsevier, vol. 72(C), pages 249-259.
  40. Wang, TianTian & Zhang, Dayong & Clive Broadstock, David, 2019. "Financialization, fundamentals, and the time-varying determinants of US natural gas prices," Energy Economics, Elsevier, vol. 80(C), pages 707-719.
  41. Plakandaras, Vasilios & Ji, Qiang, 2022. "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, vol. 28(C).
  42. repec:ipg:wpaper:2014-545 is not listed on IDEAS
  43. David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
  44. repec:ipg:wpaper:2014-561 is not listed on IDEAS
  45. Hossein Hassani & Mahdi Kalantari & Zara Ghodsi, 2019. "Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data," Stats, MDPI, vol. 2(4), pages 1-11, December.
  46. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2023. "Gold risk premium estimation with machine learning methods," Journal of Commodity Markets, Elsevier, vol. 31(C).
  47. 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).
  48. Qian, Yao & Ralescu, Dan A. & Zhang, Bo, 2019. "The analysis of factors affecting global gold price," Resources Policy, Elsevier, vol. 64(C).
  49. Edson VENGESAI & Adefemi A. OBALADE & Paul-Francois MUZINDUTSI, 2021. "Country Risk Dynamics and Stock Market Volatility: Evidence from the JSE Cross-Sector Analysis," Journal of Economics and Financial Analysis, Tripal Publishing House, vol. 5(2), pages 63-84.
  50. Mahdi Kalantari & Hossein Hassani, 2019. "Automatic Grouping in Singular Spectrum Analysis," Forecasting, MDPI, vol. 1(1), pages 1-16, October.
  51. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2015. "A real-time quantile-regression approach to forecasting gold returns under asymmetric loss," Resources Policy, Elsevier, vol. 45(C), pages 299-306.
  52. Wang, Xinya & Lucey, Brian & Huang, Shupei, 2022. "Can gold hedge against oil price movements: Evidence from GARCH-EVT wavelet modeling," Journal of Commodity Markets, Elsevier, vol. 27(C).
  53. Rehman, Mobeen Ur & Owusu Junior, Peterson & Ahmad, Nasir & Vo, Xuan Vinh, 2022. "Time-varying risk analysis for commodity futures," Resources Policy, Elsevier, vol. 78(C).
  54. repec:ipg:wpaper:2014-480 is not listed on IDEAS
  55. 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).
  56. repec:ipg:wpaper:2014-495 is not listed on IDEAS
  57. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
  58. Chen, Jinyu & Wang, Yilin & Ren, Xiaohang, 2023. "Asymmetric effect of financial stress on China’s precious metals market: Evidence from a quantile-on-quantile regression," Research in International Business and Finance, Elsevier, vol. 64(C).
  59. Liu, Weiping & Wang, Chengzhu & Li, Yonggang & Liu, Yishun & Huang, Keke, 2021. "Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
  60. repec:ipg:wpaper:2014-481 is not listed on IDEAS
  61. Zhongxin Ni & Xing Lu & Wenjun Xue, 2021. "Does the belt and road initiative resolve the steel overcapacity in China? Evidence from a dynamic model averaging approach," Empirical Economics, Springer, vol. 61(1), pages 279-307, July.
  62. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
  63. repec:ipg:wpaper:2014-548 is not listed on IDEAS
  64. Andrew Urquhart, 2017. "How predictable are precious metal returns?," The European Journal of Finance, Taylor & Francis Journals, vol. 23(14), pages 1390-1413, November.
  65. 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.
  66. 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).
  67. Zhang, Zhikai & Wang, Yudong & Zhang, Yaojie & Wang, Qunwei, 2024. "Forecasting carbon prices under diversified attention: A dynamic model averaging approach with common factors," Energy Economics, Elsevier, vol. 133(C).
  68. 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.
  69. Shrestha, Keshab & Naysary, Babak, 2023. "ESG and economic policy uncertainty: A wavelet application," Finance Research Letters, Elsevier, vol. 58(PD).
  70. Krzysztof Drachal, 2018. "Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices," Sustainability, MDPI, vol. 10(8), pages 1-27, August.
  71. Shang, Yue & Wei, Yu & Chen, Yongfei, 2022. "Cryptocurrency policy uncertainty and gold return forecasting: A dynamic Occam's window approach," Finance Research Letters, Elsevier, vol. 50(C).
  72. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
  73. Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier, 2021. "Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 1070-1085, September.
  74. 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.
  75. Salisu, Afees A. & Gupta, Rangan & Nel, Jacobus & Bouri, Elie, 2022. "The (Asymmetric) effect of El Niño and La Niña on gold and silver prices in a GVAR model," Resources Policy, Elsevier, vol. 78(C).
  76. repec:ipg:wpaper:2014-486 is not listed on IDEAS
  77. Christian Pierdzioch & Marian Risse, 2020. "Forecasting precious metal returns with multivariate random forests," Empirical Economics, Springer, vol. 58(3), pages 1167-1184, March.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.