Analysis on Volatility of Copper and Aluminum Futures Market of China

  • Wang Shu-pingEmail author
  • Wang Zhen-wei
  • Wu Zhen-xin
Conference paper
Part of the Computational Risk Management book series (Comp. Risk Mgmt)


The metal futures market is a typical nonlinear dynamic system. Using R/S method and FIEGARCH model, the paper study nonlinear characteristics and long-term memory of copper and aluminum futures market of China. The empirical results show that: the return series and volatility series of copper and aluminum futures have significant long-term memory, and the volatility leverage effect of copper futures is more obvious than aluminum futures. Furthermore, the copper futures prices respond vehemently to bad news. Testing find that FIEGARCH model is more suitable for the volatility analysis on copper and aluminum futures market of China.


FIEGARCH model Leverage effect Long-term memory R/S method Risk 



This research is supported by the Humanities and Social Sciences Research Youth Project of Ministry of Education (08JC790004), and the Special Fund of Subject and Graduate Education of Beijing Municipal Education Commission (PXM2010_014212_093659).


  1. Baillie RT, Bollerslev T, Millelsen HO (1996) Fractionally integrated generalized autoregressive conditional heteroscedasticity. J Econometrics 74:3–30CrossRefGoogle Scholar
  2. Bollerslev T, Mikkelsen H (1996) Modeling and pricing long memory in stock market volatility. J Econometrics 73:151–184CrossRefGoogle Scholar
  3. Helms BP, Kaen FR, Rosenman RE (1984) Memory in commodity futures contracts. J Futures Mark 10:559–567CrossRefGoogle Scholar
  4. Ji Guangpo, Yang Junhong (2004) An empirical study on autoregressive conditional heteroscedasticity effect in China’s futures market. Econ Rev 5:100–103 (in Chinese)Google Scholar
  5. Li Jiang, Zou Kai (2007) The empirical study on fractal structure of China’s futures market. Zhejiang Finance 8:38–39 (in Chinese)Google Scholar
  6. Li Yan, Qi Zhongying, Niu Hongyuan (2005) R/S analysis of time series of copper futures prices of Shanghai futures exchange. J Manage Sci 18:87–92 (in Chinese)Google Scholar
  7. Mandelbrot BB (1963) The variation of certain speculative prices. J Business 36:394–419CrossRefGoogle Scholar
  8. Panas E (2001) Long memory and chaotic models of prices on the London Metal Exchange. Resour Policy 27:23–246CrossRefGoogle Scholar
  9. Peters EE (1999) Chaos and order in the capital markets. Economic Science Press, BeijingGoogle Scholar
  10. Tang Yanwei, Chen Gang, Zhang Chenhong (2005) An empirical research on the long-term correlation of the price volatility of the agricultural products futures markets. Syst Eng 23:79–84 (in Chinese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.School of Economics and ManagementNorth China University of TechnologyBeijingP.R. China

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