IDEAS home Printed from https://ideas.repec.org/a/wsi/ijitdm/v16y2017i01ns0219622016500504.html
   My bibliography  Save this article

Multi-Scale Volatility Feature Analysis and Prediction of Gold Price

Author

Listed:
  • Fenghua Wen

    (Business School of Central South University, Changsha 410081, P. R. China†Center for Computational Finance and Economic Agents, University of Essex, Colchester CO4 3SQ, UK‡Institute of Financial, WenZhou University, Wenzhou 325035, P. R. China)

  • Xin Yang

    (Business School of Central South University, Changsha 410081, P. R. China)

  • Xu Gong

    (Business School of Central South University, Changsha 410081, P. R. China)

  • Kin Keung Lai

    (#xA7;International Business School, Shaanxi Normal University, Xian, P. R. China¶Department of Management Sciences, City University of Hong Kong, Kowloon, Hong Kong)

Abstract

Volatility of gold price is of great significance for avoiding the risk of gold investment. It is necessary to understand the effect of external events and intrinsic regularities to make accurate price predictions. This paper first compared EMD with CEEMD algorithm, and the results find that CEEMD algorithm performance is better than that of EMD in analysis gold price volatility. Then this paper uses the complementary ensemble empirical mode decomposition (CEEMD) to decompose the historical price of international gold into price components at different frequencies, and extracts a short-term fluctuation, a shock from significant events and a long-term price. In addition, this paper combines the Iterative cumulative sum of squares (ICSS) with Chow test to test the three event prices for structural breaks, and analyzes the effect of external events on volatility of gold price by comparing the external events with the test results for structural breaks. Finally, this paper constructs support vector machine (SVM) models and artificial neural network (ANN) on three series for prediction, and finds that the SVM performed better in gold price prediction in one-step-ahead and five-step-ahead, and when we combine the SVMs and ANNs with price components to make predictions, the error of the combined prediction is smaller than SVMs and ANNs with separate terms of series extracted.

Suggested Citation

  • Fenghua Wen & Xin Yang & Xu Gong & Kin Keung Lai, 2017. "Multi-Scale Volatility Feature Analysis and Prediction of Gold Price," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 205-223, January.
    • Handle: RePEc:wsi:ijitdm:v:16:y:2017:i:01:n:s0219622016500504
    DOI: 10.1142/S0219622016500504
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219622016500504
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219622016500504?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. 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. 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.
    3. Zhang, Xun & Lai, K.K. & Wang, Shou-Yang, 2008. "A new approach for crude oil price analysis based on Empirical Mode Decomposition," Energy Economics, Elsevier, vol. 30(3), pages 905-918, May.
    4. Gang Kou & Yanqun Lu & Yi Peng & Yong Shi, 2012. "Evaluation Of Classification Algorithms Using Mcdm And Rank Correlation," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 197-225.
    5. Iwamura, Mitsuru & Kitamura, Yukinobu & 北村, 行伸 & Matsumoto, Tsutomu & Saito, Kenji, 2019. "Can We Stabilize the Price of a Cryptocurrency?: Understanding the Design of Bitcoin and Its Potential to Compete with Central Bank Money," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 60(1), pages 41-60, June.
    6. Giovannini, Alberto, 1988. "How Do Fixed-Exchange-Rates Regimes Work? The Evidence from the Gold Standard, Bretton Woods and the EMS," CEPR Discussion Papers 282, C.E.P.R. Discussion Papers.
    7. Parisi, Antonino & Parisi, Franco & Díaz, David, 2008. "Forecasting gold price changes: Rolling and recursive neural network models," Journal of Multinational Financial Management, Elsevier, vol. 18(5), pages 477-487, December.
    8. Peng, Yi & Kou, Gang & Wang, Guoxun & Shi, Yong, 2011. "FAMCDM: A fusion approach of MCDM methods to rank multiclass classification algorithms," Omega, Elsevier, vol. 39(6), pages 677-689, December.
    9. Tully, Edel & Lucey, Brian M., 2007. "A power GARCH examination of the gold market," Research in International Business and Finance, Elsevier, vol. 21(2), pages 316-325, June.
    10. Alberto Giovannini, 1988. "How Do Fixed-Exchange-Rates Regimes Work: The Evidence From The Gold Standard, Bretton Woods and The EMS," NBER Working Papers 2766, National Bureau of Economic Research, Inc.
    11. Yi Peng & Gang Kou & Yong Shi & Zhengxin Chen, 2008. "A Descriptive Framework For The Field Of Data Mining And Knowledge Discovery," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 639-682.
    12. 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.
    13. Zhang, Yue-Jun & Wei, Yi-Ming, 2010. "The crude oil market and the gold market: Evidence for cointegration, causality and price discovery," Resources Policy, Elsevier, vol. 35(3), pages 168-177, September.
    14. Bhar, Ramaprasad & Malliaris, A.G., 2011. "Oil prices and the impact of the financial crisis of 2007–2009," Energy Economics, Elsevier, vol. 33(6), pages 1049-1054.
    15. 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.
    16. Sari, Ramazan & Hammoudeh, Shawkat & Soytas, Ugur, 2010. "Dynamics of oil price, precious metal prices, and exchange rate," Energy Economics, Elsevier, vol. 32(2), pages 351-362, March.
    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. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Lu, Haiyan & Zhang, Linyue, 2022. "A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory," Resources Policy, Elsevier, vol. 79(C).
    2. Wang, Yan & Wang, Yue & Li, Ming-Xia, 2019. "Regional characteristics of sports industry profitability: Evidence from China’s province level data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 946-955.
    3. Silvia Muzzioli & Luca Gambarelli & Bernard De Baets, 2018. "Indices for Financial Market Volatility Obtained Through Fuzzy Regression," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1659-1691, November.
    4. Xu, Qingqing & Meng, Tianci & Sha, Yue & Jiang, Xia, 2022. "Volatility in metallic resources prices in COVID-19 and financial Crises-2008: Evidence from global market," Resources Policy, Elsevier, vol. 78(C).
    5. 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).
    6. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
    7. Yuze Li & Shangrong Jiang & Yunjie Wei & Shouyang Wang, 2021. "Take Bitcoin into your portfolio: a novel ensemble portfolio optimization framework for broad commodity assets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-26, December.
    8. Aggarwal, Divya & Chandrasekaran, Shabana & Annamalai, Balamurugan, 2020. "A complete empirical ensemble mode decomposition and support vector machine-based approach to predict Bitcoin prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    9. Du, Pei & Wang, Jianzhou & Yang, Wendong & Niu, Tong, 2020. "Point and interval forecasting for metal prices based on variational mode decomposition and an optimized outlier-robust extreme learning machine," Resources Policy, Elsevier, vol. 69(C).
    10. Li, Yuze & Jiang, Shangrong & Li, Xuerong & Wang, Shouyang, 2021. "The role of news sentiment in oil futures returns and volatility forecasting: Data-decomposition based deep learning approach," Energy Economics, Elsevier, vol. 95(C).
    11. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    12. Du, Pei & Guo, Ju’e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2021. "Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm," Resources Policy, Elsevier, vol. 74(C).
    13. Wang, Jianzhou & Niu, Xinsong & Zhang, Linyue & Lv, Mengzheng, 2021. "Point and interval prediction for non-ferrous metals based on a hybrid prediction framework," Resources Policy, Elsevier, vol. 73(C).
    14. 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).
    15. Yuze Li & Shangrong Jiang & Xuerong Li & Shouyang Wang, 2022. "Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-24, December.
    16. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
    17. Gong, Xu & Lin, Boqiang, 2018. "Structural changes and out-of-sample prediction of realized range-based variance in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 27-39.
    18. Srivastava, Mrinalini & Rao, Amar & Parihar, Jaya Singh & Chavriya, Shubham & Singh, Surendar, 2023. "What do the AI methods tell us about predicting price volatility of key natural resources: Evidence from hyperparameter tuning," Resources Policy, Elsevier, vol. 80(C).

    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. Kanjilal, Kakali & Ghosh, Sajal, 2017. "Dynamics of crude oil and gold price post 2008 global financial crisis – New evidence from threshold vector error-correction model," Resources Policy, Elsevier, vol. 52(C), pages 358-365.
    2. Ntim, Collins G. & English, John & Nwachukwu, Jacinta & Wang, Yan, 2015. "On the efficiency of the global gold markets," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 218-236.
    3. Tiwari, Aviral Kumar & Aye, Goodness C. & Gupta, Rangan & Gkillas, Konstantinos, 2020. "Gold-oil dependence dynamics and the role of geopolitical risks: Evidence from a Markov-switching time-varying copula model," Energy Economics, Elsevier, vol. 88(C).
    4. Lin, Fu-Lai & Chen, Yu-Fen & Yang, Sheng-Yung, 2016. "Does the value of US dollar matter with the price of oil and gold? A dynamic analysis from time–frequency space," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 59-71.
    5. 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).
    6. Reboredo, Juan C., 2013. "Is gold a hedge or safe haven against oil price movements?," Resources Policy, Elsevier, vol. 38(2), pages 130-137.
    7. 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.
    8. Gaye Hatice Gencer & Zafer Musoglu, 2014. "Volatility Modeling and Forecasting of Istanbul Gold Exchange (IGE)," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 5(2), pages 87-101, April.
    9. Golitsis, Petros & Gkasis, Pavlos & Bellos, Sotirios K., 2022. "Dynamic spillovers and linkages between gold, crude oil, S&P 500, and other economic and financial variables. Evidence from the USA," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    10. Awartani, Basel & Aktham, Maghyereh & Cherif, Guermat, 2016. "The connectedness between crude oil and financial markets: Evidence from implied volatility indices," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 56-69.
    11. 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).
    12. Ming, Lei & Yang, Shenggang & Cheng, Cheng, 2016. "The double nature of the price of gold—A quantitative analysis based on Ensemble Empirical Mode Decomposition," Resources Policy, Elsevier, vol. 47(C), pages 125-131.
    13. Shahzad, Syed Jawad Hussain & Rahman, Md Lutfur & Lucey, Brian M. & Uddin, Gazi Salah, 2021. "Re-examining the real option characteristics of gold for gold mining companies," Resources Policy, Elsevier, vol. 70(C).
    14. Hatice Gaye Gencer & Zafer Musoglu, 2014. "Volatility Transmission and Spillovers among Gold, Bonds and Stocks: An Empirical Evidence from Turkey," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 705-713.
    15. Białkowski, Jędrzej & Bohl, Martin T. & Stephan, Patrick M. & Wisniewski, Tomasz P., 2015. "The gold price in times of crisis," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 329-339.
    16. Liu, Min & Lee, Chien-Chiang, 2022. "Is gold a long-run hedge, diversifier, or safe haven for oil? Empirical evidence based on DCC-MIDAS," Resources Policy, Elsevier, vol. 76(C).
    17. Gomis-Porqueras, Pedro & Shi, Shuping & Tan, David, 2022. "Gold as a financial instrument," Journal of Commodity Markets, Elsevier, vol. 27(C).
    18. Ftiti, Zied & Fatnassi, Ibrahim & Tiwari, Aviral Kumar, 2016. "Neoclassical finance, behavioral finance and noise traders: Assessment of gold–oil markets," Finance Research Letters, Elsevier, vol. 17(C), pages 33-40.
    19. 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.
    20. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Pérez-Pico, Ada María & Ribeiro-Navarrete, Belén, 2018. "Does social network sentiment influence the relationship between the S&P 500 and gold returns?," International Review of Financial Analysis, Elsevier, vol. 57(C), pages 57-64.

    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:wsi:ijitdm:v:16:y:2017:i:01:n:s0219622016500504. 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijitdm/ijitdm.shtml .

    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.