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Gold price analysis based on ensemble empirical model decomposition and independent component analysis

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  • Xian, Lu
  • He, Kaijian
  • Lai, Kin Keung

Abstract

In recent years, the increasing level of volatility of the gold price has received the increasing level of attention from the academia and industry alike. Due to the complexity and significant fluctuations observed in the gold market, however, most of current approaches have failed to produce robust and consistent modeling and forecasting results. Ensemble Empirical Model Decomposition (EEMD) and Independent Component Analysis (ICA) are novel data analysis methods that can deal with nonlinear and non-stationary time series. This study introduces a new methodology which combines the two methods and applies it to gold price analysis. This includes three steps: firstly, the original gold price series is decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. Secondly, IMFs are further processed with unimportant ones re-grouped. Then a new set of data called Virtual Intrinsic Mode Functions (VIMFs) is reconstructed. Finally, ICA is used to decompose VIMFs into statistically Independent Components (ICs). The decomposition results reveal that the gold price series can be represented by the linear combination of ICs. Furthermore, the economic meanings of ICs are analyzed and discussed in detail, according to the change trend and ICs’ transformation coefficients. The analyses not only explain the inner driving factors and their impacts but also conduct in-depth analysis on how these factors affect gold price. At the same time, regression analysis has been conducted to verify our analysis. Results from the empirical studies in the gold markets show that the EEMD–ICA serve as an effective technique for gold price analysis from a new perspective.

Suggested Citation

  • Xian, Lu & He, Kaijian & Lai, Kin Keung, 2016. "Gold price analysis based on ensemble empirical model decomposition and independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 454(C), pages 11-23.
    • Handle: RePEc:eee:phsmap:v:454:y:2016:i:c:p:11-23
    DOI: 10.1016/j.physa.2016.02.055
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    1. Phillip Cagan, 1971. "Changes in the Cyclical Behavior of Interest Rates," NBER Chapters, in: Essays on Interest Rates, Volume 2, pages 3-34, National Bureau of Economic Research, Inc.
    2. 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.
    3. Uribe, Martin & Yue, Vivian Z., 2006. "Country spreads and emerging countries: Who drives whom?," Journal of International Economics, Elsevier, vol. 69(1), pages 6-36, June.
    4. Neumeyer, Pablo A. & Perri, Fabrizio, 2005. "Business cycles in emerging economies: the role of interest rates," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 345-380, March.
    5. Jonathan Andrew Batten & Brian M. Lucey, 2010. "Volatility in the gold futures market," Applied Economics Letters, Taylor & Francis Journals, vol. 17(2), pages 187-190, January.
    6. Sjaastad, Larry A., 2008. "The price of gold and the exchange rates: Once again," Resources Policy, Elsevier, vol. 33(2), pages 118-124, June.
    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. Tschoegl, Adrian E., 1980. "Efficiency in the gold market -- a note," Journal of Banking & Finance, Elsevier, vol. 4(4), pages 371-379, December.
    9. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    10. Mills, Terence C., 2004. "Statistical analysis of daily gold price data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 559-566.
    11. Cheung, Yin-Wong & Lai, Kon S, 1993. "Do Gold Market Returns Have Long Memory?," The Financial Review, Eastern Finance Association, vol. 28(2), pages 181-202, May.
    12. Keith Sill, 1996. "The cyclical volatility of interest rates," Business Review, Federal Reserve Bank of Philadelphia, issue Jan, pages 15-29.
    13. Cendejas, José Luis & Castañeda, Juan E. & Muñoz, Félix-Fernando, 2014. "Business cycle, interest rate and money in the euro area: A common factor model," Economic Modelling, Elsevier, vol. 43(C), pages 136-141.
    14. Solt, Michael E & Swanson, Paul J, 1981. "On the Efficiency of the Markets for Gold and Silver," The Journal of Business, University of Chicago Press, vol. 54(3), pages 453-478, July.
    15. Benhmad, François, 2013. "Dynamic cyclical comovements between oil prices and US GDP: A wavelet perspective," Energy Policy, Elsevier, vol. 57(C), pages 141-151.
    16. 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.
    17. 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.
    18. 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)

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