本文利用Bluenext和ECX的经验数据，对欧盟碳排放权市场（EU-ETS）的价格行为特征进行了分析.研究表明：1）对EU-ETS市场收益率序列基本统计特征检验显示：碳收益率序列不服从正态分布，具有明显的有偏、尖峰、肥尾的非正态性和非线性的统计分布特征； 2）对市场分形性的研究表明：该市场具有显著的统计自相似性，阶段性的、而非全过程的长期记忆性特征； 3）对市场混沌性的研究表明：从关联维数看，EU-ETS并不存在低维混沌性，但结合最大Lyapunov指数检验及邻近返回检验证明欧盟碳排放权市场存在非收敛饱和混沌性.由此得出结论：欧盟碳排放权市场是一个具有分形与混沌特征的非线性动力系统，它不符合有效市场假说，因此不能用线性范式来研究市场价格行为、交易机制及政策制定.
Abstract
In this paper, the characteristics of the price behavior of the EU carbon emissions market (EU-ETS) are analyzed by using the empirical data of Bluenext and ECX. The research shows that:1) the statistical characteristics of the sequence of the EU-ETS market returns show that the carbon yield sequence is not from normal distribution, which has obvious characteristics of the statistical distribution of the partial, spike and fat tail; 2) the research on the fractal property of the market shows that:the market has the obvious self similarity, the staged, and not the whole process of long-term memory; 3) the study on the chaos of the market shows that:from the correlation dimension, EU-ETS does not exist in the low dimensional chaos, but the combination of the maximum Lyapunov index test and close return test proves that the EU carbon market has a non convergence saturation chaos. From this, we can get the conclusion:The EU carbon emissions market is a nonlinear dynamic system with fractal and chaotic characteristics, which does not conform to the effective market hypothesis, and the linear paradigm can't be used to study the market price behavior, trading mechanism and policy formulation.
关键词
碳排放权市场 /
分形与混沌 /
非线性动力系统
{{custom_keyword}} /
Key words
carbon emissions market /
fractal and chaotic /
nonlinear dynamical systems
{{custom_keyword}} /
中图分类号：
F831.5
{{custom_clc.code}}
({{custom_clc.text}})
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 埃德加·E·彼得斯. 分形市场分析-将混沌理论应用到投资与经济理论上[M]. 储海林,殷勤,译.北京:经济科学出版社, 2002.
[2] 李安楠, 邓修权, 赵秋红. 分形视角下的非常规突发事件应急组织动态重构-以8.12天津港爆炸事件为例[J]. 管理评论, 2016, 28(8):193-206.Li A N, Deng X Q, Zhao Q H. Unconventional emergency organizational dynamic reconstruction based on fractal-8.12 Tianjin explosions case study[J]. Management Review, 2016, 28(8):193-206.
[3] 张琛, 倪志伟, 姜婷. 基于WPTMM的PM2.5与气象条件关系的联合多重分形分析[J]. 系统工程理论与实践, 2015, 35(8):2166-2176.Zhang C, Ni Z W, Jiang T. Joint multifractal analysis of relationship of PM2.5 and meteorological condition based on WPTMM[J]. Systems Engineering-Theory & Practice, 2015, 35(8):2166-2176.
[4] George D, Raphael N M. Are the European carbon markets efficient?[J]. Review of Futures Markets, 2008, 17(2):103-128.
[5] Zhen H F, Zou L L, Wei Y M. Carbon price volatility:Evidence from EU ETS[J]. Applied Energy, 2011, 88(1):590-598.
[6] Fan X H, Li S S, Tian L. Chaotic characteristic identification for carbon price and an multilayer perceptron network prediction model[J]. Expert Systems with Applications, 2015, 42(1):3945-3952.
[7] 杨星, 冯晓莹. 欧盟碳排放权交易市场弱式有效性分析与检验[R]. 工作论文, 2014.Yang X, Feng X Y. Analysis and test of the weak form efficiency of the EU carbon emissions market[R]. Working Paper, 2014.
[8] 庄新田, 张鼎, 苑莹, 等. 中国股市复杂网络中的分形特征[J]. 系统工程理论与实践, 2015, 35(2):273-282.Zhuang X T, Zhang D, Yuan Y, et al. Fractal characteristic of the Chinese stock market complex network[J]. Systems Engineering-Theory & Practice, 2015, 35(2):273-282.
[9] 王鹏, 袁小丽. 金融资产收益非对称性的多标度分形测度及其在VaR计算中的应用[J]. 中国管理科学, 2015, 23(3):13-23.Wang P, Yuan X L. A VaR model based on multifractal asymmetry measurement[J]. Chinese Journal of Management Science, 2015, 23(3):13-23.
[10] Chakrabotry A, Ashir T, Suganuma G. Self-similar and fractal nature of internet traffic[J]. International Journal of Network Management, 2004, 14(1):1-11.
[11] Hurst H E. Long term storage capacity of reservoirs[J]. Transactions of the American Society of Civil Engineers, 1951, 116(1):770-799.
[12] 陈梦根. 中国股市长期记忆效应的实证研究[J]. 经济研究, 2003, 3(1):70-78.Chen M G. Empirical study on the long-memory effect in Chinese stock market[J]. Economic Research Journal, 2003, 3(1):70-78.
[13] Lo A W. Long-term memory in stock market prices[J]. Econometrica, 1991, 59(5):1279-1313.
[14] Giraitis L, Kokoszkab P S, Leipusc R, et al. Rescaled variance and related tests for long memory in volatility and levels[J]. Journal of Econometrics, 2003, 112(1):265-294.
[15] Andrews D W K. Heteroscedasticity and autocorrelation consistent covariance estimation[J]. Econnometrica, 1991, 59(1):817-858.
[16] 谢朝华, 李忠, 郑咏梅,等.中国股票市场分形与混沌特征:1994-2008[J]. 系统工程, 2010, 28(6):30-35.Xie C H, Li Z, Zheng Y M, at el. The fractal and chaos characteristics of Chinese stock markets:1994-2008[J]. Systems Engineering, 2010, 28(6):30-35.
[17] Grassberger P, Procaccia J. Characterization of strange attractors[J]. Physical Review Letters, 1983, 50(5):346-349.
[18] 王卫宁, 汪秉宏, 史晓平. 股票价格波动的混沌行为分析[J]. 数量经济技术经济研究, 2004, 23(4):141-147.Wang W N, Wang B H, Shi X P. Chaos behavior analysis of stock price fluctuating[J]. The Journal of Quantitative & Technical Economic, 2004, 23(4):141-147.
[19] Wolf A, Swift J B, Swinney H L, et al. Determining Lyapunov exponents from a time series[J]. Physica, 1985, 16(3):285-317.
[20] Gilmore C G. A new test for chaos[J]. Journal of Economic Behaviour and Organisation, 1993, 22(1):209-237.
[21] McKenzie M D. Chaotic behavior in the national stock market indices:New evidence from the close returns test[J]. Global Finance Journal, 2001, 12(2):35-53.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
基金
国家社科基金重点项目（15AGJ009）
{{custom_fund}}
PDF(1213 KB)
