国际碳市场价量关系的多重分形特征研究
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摘要
文章在验证欧盟排放配额(European Union allowance,EUA)市场价量之间交互相关关系存在性的基础上,使用多重分形去趋势交叉相关分析(multifractal detrended cross-correlation analysis,MF-DCCA)法研究发现,由于长记忆性和厚尾分布的存在,价量关系存在多重分形特征。研究价量关系在欧盟碳排放体系的实施过程的3个阶段(2005-2007年、2008-2012年和2013-2020年)中的差异性,发现不同阶段价量关系的多重分形特征不同,第2、3阶段的市场风险明显低于第1阶段,有效性有所提高;进一步使用非对称MF-DCCA(MF-ADCCA)法研究不同市场状态下价量关系的非对称性发现,对于每一阶段,价格或者交易量序列趋势改变时,多重分形特征和市场风险也有所不同。文章通过分析碳市场价量关系的多重分形特征探究了市场的有效性和风险状态及其变化,为监管者和投资者深入认识市场提供了参考。
This paper verifies the existence of the cross-correlation between price and volume in European Union allowance(EUA) market firstly. And then it is found that the relationship between price and volume is multifractal because of long-range correlations and fat-tail distributions with multifractal detrended cross-correlation analysis(MF-DCCA). At the same time, the differences of the relationship between the price and volume in the three periods(2005-2007, 2008-2012 and 2013-2020) of European Union carbon emissions trading system are studied. It is revealed that the multifractal characteristics of the relationship between the price and volume are different in each period of the market, and the risks of second and third periods are obviously lower than that of the first period while the market effectiveness is improved. Furthermore, the asymmetry of the relationship under different market situations is also examined with multifractal asymmetric detrended cross-correlation analysis(MF-ADCCA). It is discovered that the multifractality and the risk of market are different when the trend of price or volume changes in three periods. By analyzing the multifractal characteristics of the relationship between the price and volume in carbon market, the market effectiveness and risk status and their changes are explored. The results contribute to providing a theoretical reference for regulators and investors to understand the carbon market fully.
This paper verifies the existence of the cross-correlation between price and volume in European Union allowance(EUA) market firstly. And then it is found that the relationship between price and volume is multifractal because of long-range correlations and fat-tail distributions with multifractal detrended cross-correlation analysis(MF-DCCA). At the same time, the differences of the relationship between the price and volume in the three periods(2005-2007, 2008-2012 and 2013-2020) of European Union carbon emissions trading system are studied. It is revealed that the multifractal characteristics of the relationship between the price and volume are different in each period of the market, and the risks of second and third periods are obviously lower than that of the first period while the market effectiveness is improved. Furthermore, the asymmetry of the relationship under different market situations is also examined with multifractal asymmetric detrended cross-correlation analysis(MF-ADCCA). It is discovered that the multifractality and the risk of market are different when the trend of price or volume changes in three periods. By analyzing the multifractal characteristics of the relationship between the price and volume in carbon market, the market effectiveness and risk status and their changes are explored. The results contribute to providing a theoretical reference for regulators and investors to understand the carbon market fully.
引文
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[12] 汪冬华,索园园.我国沪深300股指期货和现货市场的交叉相关性及其风险[J].系统工程理论与实践,2014,34(3):631-639.
[13] 黄健柏,程慧,郭尧琦,等.金属期货量价关系的多重分形特征研究:基于MF-DCCA方法[J].管理评论,2013,25(4):77-85.
[14] CAO G,CAO J,XU L,et al.Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market[J].Physica A:Statistical Mechanics and its Applications,2014,393(1):460-469.
[15] ZHANG C,NI Z,NI L.Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors[J].Physica A:Statistical Mechanics and its Applications,2015,438:114-123.
[16] WEI X U,CAO G.Asymmetric-structure analysis of carbon and energy markets[J].Fractals-complex Geometry Patterns & Scaling in Nature & Society,2016,24(1):1650011-15.
[17] YUAN Y,ZHUANG X T,JIN X.Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis[J].Physica A:Statistical Mechanics and its Applications,2009,388(11):2189-2197.
[18] SHADKHOO S,JAFARI G R.Multifractal detrended cross-correlation analysis of temporal and spatial seismic data[J].The European Physical Journal B,2009,72(4):679-683.
[19] KANTELHARDT J W,ZSCHIEGNER S A,KOSCIELNY B E,et al.Multifractal detrended fluctuation analysis of nonstationary time series[J].Physica A:Statistical Mechanics and its Applications,2002,316(1/2/3/4):87-114.
[20] THEILER J,EUBANK S,LONGTIN A,et al.Testing for nonlinearity in time series:the method of surrogate data[J].Physica D Nonlinear Phenomena,1992,58(92):77-94.
[2] GALLANT A R,TAUCHEN G.Stock prices and volume[J].Review of Financial Studies,1992,5(2):199-242.
[3] 汪文隽,汤丽娟,陈玲玲.欧盟和湖北碳市场量价关系的多重分形特征研究[J].云南师范大学学报(哲学社会科学版),2017,49(1):107-114.
[4] 张晨,彭婷,刘宇佳.基于GARCH-分形布朗运动模型的碳期权定价研究[J].合肥工业大学学报(自然科学版),2015,38(11):1553-1558.
[5] 王扬雷,杜莉.我国碳金融交易市场的有效性研究:基于北京碳交易市场的分形理论分析[J].管理世界,2015(12):174-175.
[6] PODOBNIK B,STANLEY H E.Detrended cross-correlation analysis:a new method for analyzing two nonstationary time series[J].Physical Review Letters,2008,100(8):084102.
[7] 苑莹,庄新田,金秀.中国股市的日内效应、长记忆性及多重分形性:基于价量交叉相关性视角[J].系统管理学报,2016,25(1):28-35.
[8] 王宏勇,陈晓娜.国内外原油市场的交互相关性分析:基于多重分形的统计测度[J].统计与信息论坛,2015,12:42-47.
[9] MANDELBROT B B.A multifractal walk down wall street[J].Scientific American,1999,280(2):70-73.
[10] ZHOU W X.Multifractal detrended cross-correlation analysis for two nonstationary signals[J].Physical Review E Statistical Nonlinear & Soft Matter Physics,2008,77(6):066211.
[11] 汪冬华,索园园.金融危机前后中国股票市场和外汇市场的交叉相关性:基于多重分形理论的视角[J].系统管理学报,2013,22(3):394-401.
[12] 汪冬华,索园园.我国沪深300股指期货和现货市场的交叉相关性及其风险[J].系统工程理论与实践,2014,34(3):631-639.
[13] 黄健柏,程慧,郭尧琦,等.金属期货量价关系的多重分形特征研究:基于MF-DCCA方法[J].管理评论,2013,25(4):77-85.
[14] CAO G,CAO J,XU L,et al.Detrended cross-correlation analysis approach for assessing asymmetric multifractal detrended cross-correlations and their application to the Chinese financial market[J].Physica A:Statistical Mechanics and its Applications,2014,393(1):460-469.
[15] ZHANG C,NI Z,NI L.Multifractal detrended cross-correlation analysis between PM2.5 and meteorological factors[J].Physica A:Statistical Mechanics and its Applications,2015,438:114-123.
[16] WEI X U,CAO G.Asymmetric-structure analysis of carbon and energy markets[J].Fractals-complex Geometry Patterns & Scaling in Nature & Society,2016,24(1):1650011-15.
[17] YUAN Y,ZHUANG X T,JIN X.Measuring multifractality of stock price fluctuation using multifractal detrended fluctuation analysis[J].Physica A:Statistical Mechanics and its Applications,2009,388(11):2189-2197.
[18] SHADKHOO S,JAFARI G R.Multifractal detrended cross-correlation analysis of temporal and spatial seismic data[J].The European Physical Journal B,2009,72(4):679-683.
[19] KANTELHARDT J W,ZSCHIEGNER S A,KOSCIELNY B E,et al.Multifractal detrended fluctuation analysis of nonstationary time series[J].Physica A:Statistical Mechanics and its Applications,2002,316(1/2/3/4):87-114.
[20] THEILER J,EUBANK S,LONGTIN A,et al.Testing for nonlinearity in time series:the method of surrogate data[J].Physica D Nonlinear Phenomena,1992,58(92):77-94.