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金属期货量价关系的多重分形特征研究——基于MF-DCCA方法 被引量:26

Research on Multifractal Features of the Relation between Price and Volume in China Metal Futures Market: Based on MF-DCCA Approach
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摘要 近年来的实证研究表明,分形市场理论相比于传统有效市场假说能够更好地解释金融市场中存在的各种复杂现象和行为。因此,金融市场中分形特征的存在性检验以及分形特征的应用被更多学者所关注。然而,现有研究基本集中于对收益率或者交易量等单一序列的分形特征的实证研究上,而忽略了二者之间存在的相关性。因此,本文采用MF-DCCA方法,对我国金属期货市场量价关系的多重分形特征进行实证检验。实证结果表明我国金属期货量价关系存在着多重分形特征,而长期记忆性和厚尾分布是产生多重分形特征的主要原因。以上结论将有助于理解我国金属期货价格和交易量之间存在的非线性依赖关系和潜在的动力学机制。 Recently,more and more empirical studies have shown that the Fractal Market Theory can better explain the complex phenomena and behaviors in financial market than the traditional theory of Effective Market Hypothesis.As a result,more attention is paid to the study of the existence and application of Multifractal Features.However,most previous researches concentrate on studying multifractal features of returns or volume separately and ignore the relation between them.Therefore,this paper gives an empirical test on multifractal features of the relation between price and volume in China metal futures market by MF-DCCA approach.Empirical results show that the relationship between price and volume in China metal futures market is multifractal and the main reasons for the existence of multifractal are long-term memory and heavy-tailed distribution.These conclusions would help to understand the nonlinear dependency relationship and potential dynamics mechanism between price and volume in China metal futures market.
出处 《管理评论》 CSSCI 北大核心 2013年第4期77-85,共9页 Management Review
基金 国家自然科学基金项目(71073177) 教育部人文社科基金项目(10YJCZH123) 湖南省自然科学基金项目(12JJ4077) 湖南省软科学重点项目(2011ZK2043) 湖南省研究生创新项目(CX2012B107)
关键词 金属期货 量价关系 多重分形 MF-DCCA metal futures relation between price and volume multifractal MF-DCCA
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参考文献35

  • 1Panas, E. Long Memory and Chaotic Models of Prices on the London Metal Exchange[J]. Resources Policy, 2001,27(4):235-246.
  • 2Kenourgios, D., A. Samitas. Testing Efficiency of the Copper Futures Market: New Evidence from London Metal Exchange[C]. Global Business and Economics Review, 2004.
  • 3Mandlebrot, B. B. The Fractal Geometry of Nature[M]. San Francisco: Freeman, 1982.
  • 4Peters, E. E. Fractal Market Analysis: Applying Chaos Theory to Investment and Economics[M]. New York: John Wiley & Sons Press, 1994.
  • 5Lo, A. W. Long-Term Memory in Stock Market Prices[J]. Econometrica, 1991,59(5):1279-1314.
  • 6Hiemstra, C., J. D. Jones. Another Look at Long Memory in Common Stock Returns[J]. Journal of Empirical Finance, 1997,4(4): 371-401.
  • 7Granger, C. W. J. Long Memory Relationship and Aggregation of Dynamic Models[J]. Journal of Econometrics, 1980,14(2):227-238.
  • 8Baillie, R. T. Long Memory Processes and Fractional Integration in Econometrics[J]. Journal of Econometrics, 1996,73(1):5-59.
  • 9Alvarez-Ramirez, J., M. Cisneros, C. Ibarra-Valdez, et al. Muhifractal Hurst Analysis of Crude Oil Prices[J]. Physica A: Statistical Mechanics and its Applications, 2002,313(3-4):651-670.
  • 10Serletis, A., I. Andreadis. Random Fractal Structures in North American Energy Markets[J]. Energy Economics, 2004,26(3):389- 399.

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