碳市场复杂系统价格波动机制与风险管理研究
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摘要
碳交易市场是应对气候变化成本有效手段,随着应对气候变化行动和国际碳市场迅速发展,碳市场及其金融属性已成为能源经济领域研究热点之一。碳市场价格波动涉及到多方面因素的综合影响,受气候变化谈判、金融危机等影响,具有非线性、不确定性、综合性和动态性等复杂系统的典型特征,碳价波动频繁,市场风险凸显。但是,现有相关文献对碳市场价格波动机制和风险的定量研究不够,研究方法局限于正态分布、线性回归等传统市场研究思路,在碳市场研究中反映了一定的局限性。
本文基于碳市场复杂系统的背景,围绕碳市场价格波动机制和风险管理两个主要问题,采用计量经济学、统计学和金融学等领域的理论方法和模型,定量描述了碳价的波动特征,分析了碳价的主要风险因素及其影响机制,系统地揭示了碳市场复杂系统风险管理的主要特征,并对碳市场进行风险管理的技术手段和成本进行了分析。
本文的主要研究工作和创新点包括以下几个方面：
(1)基于碳价波动的复杂情况,引入分解序列能够很好地反映原价格特征的总体经验模态分解模型将碳价进行分解,从市场机制、异质性环境、温度三个角度分析碳价变化。研究表明碳价受到市场影响频率高,持续的时间较短,一般在20周以内,幅度一般在3欧元以内。异质性环境影响碳价频率低,持续时间较长,35周甚至更长,幅度较大,一般在6欧元甚至更高。同时,碳市场是温度敏感性市场,受季节性影响,呈现一种振幅式的运动。将碳价分解为低频数据和高频数据,这一思路能够较好的分析碳价的复杂走势。
(2)为了进一步分析碳价的波动规律,从非线性动力学观点出发，采用序列相关和方差比率分析碳价的随即游走性；采用R/S、修正R/S分析历史碳价对(?)的记忆性；采用混沌理论分析碳市场的内部机制对碳价波动的影响。从市场内部机制、市场的正负反馈机制、异制性环境三者的互动关系分析碳价波动的本因。研究发现,碳价基本不是随机游走性的,当前碳价没有完全包含着历史价格信息。碳价(Dec08)表现出短期记忆性,对未来价格的走势影响时间短。碳价波动内生的复杂性非线性动力学现象不明显,有部分混沌特性,具有部分市场特征和分形市场特征。价格波动一方面是市场机制的作用,而大幅度波动主要是异质性环境冲击的影响。
(3)引入市场投资者的预期收益和投资尺度，通过Zipf技术研究不同预期收益和投资时间尺度下碳价波动行为。研究发现,碳价的上涨和下跌概率是不对称的,在高预期收益率和较长投资时间内,下跌概率略高于上涨概率。不同预期收益率的投资者对于价格的认知不同,在较低的预期收益率下,碳价的涨跌受到了市场机制、季节性、异质性事件的影响,碳价的变化也是较为清晰的,而在高预期收益率下,投资者对碳价变动认知较不稳定,风险较大。
(4)针对碳市场现货和期货价格相依性,盟DCC-GARCH对碳市场现货和期货相依性进行动态分析，并采用结构性断点检测模型分析碳市场主要重大事件，运用COPULA函数分系重大事件前后市场现货和期货相依性变化情况。研究结果表明现货和期货相依性很强,期货价格波动对现货价格的解释能力在50%以上。重大事件对现货和期货相依性有较明显的改变,较大的影响了现货和期货相依性,在第一阶段初期到末期过程中,相依性呈现增强到减弱的趋势性特征；在第二阶段金融危机发生后,相依性呈现较大的减弱趋势。表明了碳市场的发展受到特殊性事件影响较大。
(5)针对碳市场风险问题,将在险值引入到碳市场的风险度量中，突出了有效买哦书碳市场风险的手段；运用极值得理论对碳价分先暴露程度进行分析，度量碳市场静态VaR,运GARCH模型对现货市场和期货市场碳价收益率波动进行建模，计算动态VaR,同时比较了EVT理论和传统VaR对于碳市场风险度量的有效性。研究发现,碳市场的下跌风险高于上涨风险。现货市场和期货市场第一阶段上涨和下跌风险高于第二阶段。同一阶段内现货和期货上涨和下跌风险较为相近。研究结果显示,本文所采用的EVT处理后的静态VaR和动态VaR,均要比银行界普遍使用的传统方法更为有效,可以有效降低碳市场参与者所面临的风险。GARCH-EVT-VaR可以有效的描述EU ETS的市场风险。
(6)针对碳市场的流动性风险问题,本文首次用交易价差、交易量、换三率等量纲构建了碳市场流动性指标，分析了碳市场流动性等险。运用广义帕累托分布和Copula函数分析市场风险和流动性风险的相依性以及风险集成值。研究发现,碳市场的市场风险和流动性风险相依性较弱。受市场规模限制,碳市场目前的市场流动性风险较小,当前阶段,碳市场面临的主要是市场风险。但忽视市场流动性会低估碳市场面临风险,每份合约在一日内面临的流动性风险会达到市场风险的9%,而持有合约数较多的投资者,面临的流动性风险会大大增加。随着碳市场的容量增大,将有助于降低流动性风险,但随着交易者增加,换手率变小,流动性风险仍不可忽视。
(7)根据碳市场参与主体的多样性,套期保值者的行为特征决定着套期保值的目标函数。从行为金融学角度度出发,运用失望压恶非期望效用模型研究失望厌恶和风险厌恶对期货最优套期保值策略的影响，并进一步模拟分析了碳市场进行套期保值成本。结果表明以方差最小化的套期保值在第一阶段作用微弱,套期保值比在0.1左右。第二阶段套期保值比在0.4左右。碳市场最优套期保值比低于一般市场。碳市场上,失望厌恶和风险厌恶系数极大或者极低时,对市场的判断容易产生偏差。失望和风险厌恶变大时,投资收益参考点会下降。同时,碳市场价格趋势影响投资者收益的心理预期,调整好投资心态有利于市场操作。失望厌恶与风险厌恶之间替代效应不明显。随着风险厌恶的减小,失望厌恶不一定会增大。失望厌恶和风险厌恶会改变投资者的最优投资策略。模拟表明碳市场上期货与现货的收益率序列并不存在线性相关性。目前期货市场尚不能很好的为现货市场提供套期保值的功能。
通过以上研究,有助于进一步加深对碳市场复杂系统价格波动机制和风险管理的认识和理解,为碳价预测、碳市场风险管理提供信息支持,为市场监管者、投资者等有关机构的决策提供参考依据和政策支持。
The carbon market is cost-effective for addressing climate change, with the response to the rapid development of the Climate Change Action and the international carbon market, carbon market and its financial property has become one of the hotspot fields of energy economics research. Carbon market especially the carbon price volatility is universally acknowledged to be commonly influenced by numerous factors and appears a typical complex system, with nonlinear, nondeterministic, comprehensive and dynamic features etc. During recent years, the confluence of many contributors, such as international negotiations, the financial crises, important notices, has led to great volatility and complex changes. Carbon market risk has been brought into focus. However, existing related literature appear to be scanty to quantify the carbon market risk; as for the research methods, they tend to use some traditional paths, such as the normal distribution, linear regression and ordinary least squares (OLS) etc., which seem to be quite limited and insufficient to well define the complexity of carbon market risk management.
Motivated by several scientific topics within carbon price volatility and market risk management, this thesis singles out some empirical study methodologies, including econometric models, statistical approaches and financial market risk management theories etc., so as to model the features of carbon price volatility, discuss the directions of carbon market risk information transfer, and analyze the primary factors of oil price changes and their influencing mechanism. A relatively systematic recognition for the main characteristics of oil market risk management will come into being and the carbon market risk management techniques and cost will be analyzed.
In brief, the highlights of our research in this thesis can be summarized as follows.
(1) By proposing the hypotheses for carbon price volatility, variance ratio and Ensemble Empirical Mode Decomposition (EEMD) will be used to analyze the carbon price. Results show that carbon market is temperature-sensitive, affected by seasonal changes, which presents a style of movement amplitude; carbon price is affected by the market mechanism at a high frequency, with the duration being less than20weeks and amplitudes less than3euros; heterogeneity environment has an impact on carbon price at a low frequency, the duration lasting more than35weeks or even more and amplitudes more than6euros or higher. Historical carbon price change shows the long-term trend declines gradually since2005from18to16euros per ton. The continuing declining trend agrees with special events by time. Analyzing the composition of carbon price from low frequencies and high frequencies helps understand the underlying rules of carbon price reality
(2) Based on the research, we analyze carbon price volatility from a nonlinear dynamics point of view. First, we use a random walk model, including serial correlation and variance ratio tests, to determine whether carbon price history information is fully reflected in current carbon price. The empirical research results show that carbon price is not a random walk:the price history information is not fully reflected in current carbon price. Second, use R/S, modified R/S and ARFIMA to analyse the memory of carbon price history. For the period April2005-December2008, the modified Hurst index of the carbon price is0.4859and the d value of ARFIMA is0.1191, indicating short-term memory of the carbon price. Third, we use chaos theory to analyse the influence of the carbon market internal mechanism on carbon price, i.e., the market's positive and negative feedback mechanism and the heterogeneous environment. Chaos theory proves that the correlation dimension of carbon price increases. The maximal Lyapunov exponent is positive and large. There is no obvious complex endogenous phenomenon of nonlinear dynamics the carbon price fluctuation. The carbon market is mildly chaotic, showing both market and fractal market characteristics. Price fluctuation is not only influenced by the internal market mechanism, but is also impacted by the heterogeneous environment.
(3) Zipf analysis technology is used to assess carbon price volatility under different expectations of returns and time scales. The results show the sensitivity of the futures returns to the market's returns is lower in the second phase than the first phase. At longer time scales, the probability of prices declining becomes greater than the probability of prices increasing. Traders with different expectations of returns have different price perceptions. For traders with low expectations of returns, carbon prices are affected by market mechanisms, seasonal weather variations and other heterogeneous events, and carbon price fluctuations are relatively well perceived. Carbon prices are more volatile and higher risks and uncertainties are more characteristic for high expectations of returns.
(4) For the spot and futures prices dependencies carbon market, the chapter uses ①CC-GARCH to analyze the dynamic correlation between spot and futures price,②tructural breakpoint detection model to analyze the major events of carbon market,③copula function to analyze the dependencies change before and after the major events in the spot and futures. The results show that there is a strong correlation between spot and futures. The explanatory power of spot price volatility by the futures price is more than50%. A major special event had a visible change on correlation between spot and futures, whose influence is large. The correlation presents enhancements to the weakening trend characteristics in the first phase and the correlation showed a weakening trend after the financial crisis in the second phase, which show that the carbon market development is influenced by special events.
(5) Carbon market risk directly affects the investor confidence and emission reduction results. In the present study, extreme value theory (EVT) is used to analyze risk exposure for carbon price and to measure the Value at Risk (VaR) for the carbon market. GARCH models are applied to establish a model of price volatility for the spot market and the futures market and to calculate dynamic VaR. Traditional VaR and VaR based on EVT are also compared. The results show that the downside risk is higher than the upside risk for the carbon market. Upside and downside risks are higher in the first phase (Jun2005-Dec2007) than in the second phase (Feb2008-Dec2009) for both the spot and futures markets. Upside and downside risks are similar for the spot and futures markets during the same phase. The results also show that the EVT VaR is more effective than the traditional method, which can reduce the risks for market participants. Dynamic VaR based on GARCH and EVT can effectively measure the EU ETS market risk.
(6) For liquidity risk in carbon markets, the chapter builds carbon market liquidity indicators from trading spreads, trading volume, turnover rate for the first time, analyzing the liquidity risk in carbon markets. The Generalized Pareto Distribution and the Copula function is used to analyze dependencies and integration of market risk and liquidity risk market risk. The Empirical study found that the dependency of market risk and liquidity risk in carbon markets is weak. Subject to market size restrictions, the market liquidity risk in carbon market is small, but ignoring market liquidity will underestimate carbon market risk, liquidity risk reach9%of the market risk in one day for each contract, which shows liquidity risk increasing greatly for investors holding lots of contracts. Carbon market development can help to reduce liquidity risk, but liquidity risk still cannot be ignored with the turnover rate becoming smaller.
(7) The behavioral characteristics of hedgers determine the hedging objective function in carbon market. Starting from a behavioral finance point of view, the chapter uses non-expected utility model of disappointment aversion to①analyze the optimal hedging strategy influence by disappointment aversion and risk aversion;②simulate hedge costs of the carbon market. The results show that the hedge ratio is small according to the minimum variance, which is about0.1in the first phase of and0.4in the second phase. The optimal hedge ratio is smaller than the general market. In carbon market, the market judgment is prone to bias when disappointment aversion coefficient and risk aversion coefficient is high or very low. When the disappointment and the risk aversion are big, the reference point of investment income will decline in the carbon market. Investors'expectation psychological is influenced by carbon price trends. Adjusting investment mentality is conducive to market operations. The substitution effect between risk aversion and disappointment aversion is not obvious, the disappointment aversion effect maybe not increase when the risk aversion decreases. Disappointment aversion and risk aversion will change investors'optimal investment strategy. The simulation shows that the return of the futures and spot carbon market has not a linear correlation. The hedging feature is not good in carbon market.
To sum up, based on the research above, this thesis is aimed to strengthen therecognition of carbon market complex system in terms of price volatility and risk management, and proposesome scientific information support for carbon price forecast, macroeconomic research, investment decision-making and market regulating of related institutions.
本文基于碳市场复杂系统的背景,围绕碳市场价格波动机制和风险管理两个主要问题,采用计量经济学、统计学和金融学等领域的理论方法和模型,定量描述了碳价的波动特征,分析了碳价的主要风险因素及其影响机制,系统地揭示了碳市场复杂系统风险管理的主要特征,并对碳市场进行风险管理的技术手段和成本进行了分析。
本文的主要研究工作和创新点包括以下几个方面：
(1)基于碳价波动的复杂情况,引入分解序列能够很好地反映原价格特征的总体经验模态分解模型将碳价进行分解,从市场机制、异质性环境、温度三个角度分析碳价变化。研究表明碳价受到市场影响频率高,持续的时间较短,一般在20周以内,幅度一般在3欧元以内。异质性环境影响碳价频率低,持续时间较长,35周甚至更长,幅度较大,一般在6欧元甚至更高。同时,碳市场是温度敏感性市场,受季节性影响,呈现一种振幅式的运动。将碳价分解为低频数据和高频数据,这一思路能够较好的分析碳价的复杂走势。
(2)为了进一步分析碳价的波动规律,从非线性动力学观点出发，采用序列相关和方差比率分析碳价的随即游走性；采用R/S、修正R/S分析历史碳价对(?)的记忆性；采用混沌理论分析碳市场的内部机制对碳价波动的影响。从市场内部机制、市场的正负反馈机制、异制性环境三者的互动关系分析碳价波动的本因。研究发现,碳价基本不是随机游走性的,当前碳价没有完全包含着历史价格信息。碳价(Dec08)表现出短期记忆性,对未来价格的走势影响时间短。碳价波动内生的复杂性非线性动力学现象不明显,有部分混沌特性,具有部分市场特征和分形市场特征。价格波动一方面是市场机制的作用,而大幅度波动主要是异质性环境冲击的影响。
(3)引入市场投资者的预期收益和投资尺度，通过Zipf技术研究不同预期收益和投资时间尺度下碳价波动行为。研究发现,碳价的上涨和下跌概率是不对称的,在高预期收益率和较长投资时间内,下跌概率略高于上涨概率。不同预期收益率的投资者对于价格的认知不同,在较低的预期收益率下,碳价的涨跌受到了市场机制、季节性、异质性事件的影响,碳价的变化也是较为清晰的,而在高预期收益率下,投资者对碳价变动认知较不稳定,风险较大。
(4)针对碳市场现货和期货价格相依性,盟DCC-GARCH对碳市场现货和期货相依性进行动态分析，并采用结构性断点检测模型分析碳市场主要重大事件，运用COPULA函数分系重大事件前后市场现货和期货相依性变化情况。研究结果表明现货和期货相依性很强,期货价格波动对现货价格的解释能力在50%以上。重大事件对现货和期货相依性有较明显的改变,较大的影响了现货和期货相依性,在第一阶段初期到末期过程中,相依性呈现增强到减弱的趋势性特征；在第二阶段金融危机发生后,相依性呈现较大的减弱趋势。表明了碳市场的发展受到特殊性事件影响较大。
(5)针对碳市场风险问题,将在险值引入到碳市场的风险度量中，突出了有效买哦书碳市场风险的手段；运用极值得理论对碳价分先暴露程度进行分析，度量碳市场静态VaR,运GARCH模型对现货市场和期货市场碳价收益率波动进行建模，计算动态VaR,同时比较了EVT理论和传统VaR对于碳市场风险度量的有效性。研究发现,碳市场的下跌风险高于上涨风险。现货市场和期货市场第一阶段上涨和下跌风险高于第二阶段。同一阶段内现货和期货上涨和下跌风险较为相近。研究结果显示,本文所采用的EVT处理后的静态VaR和动态VaR,均要比银行界普遍使用的传统方法更为有效,可以有效降低碳市场参与者所面临的风险。GARCH-EVT-VaR可以有效的描述EU ETS的市场风险。
(6)针对碳市场的流动性风险问题,本文首次用交易价差、交易量、换三率等量纲构建了碳市场流动性指标，分析了碳市场流动性等险。运用广义帕累托分布和Copula函数分析市场风险和流动性风险的相依性以及风险集成值。研究发现,碳市场的市场风险和流动性风险相依性较弱。受市场规模限制,碳市场目前的市场流动性风险较小,当前阶段,碳市场面临的主要是市场风险。但忽视市场流动性会低估碳市场面临风险,每份合约在一日内面临的流动性风险会达到市场风险的9%,而持有合约数较多的投资者,面临的流动性风险会大大增加。随着碳市场的容量增大,将有助于降低流动性风险,但随着交易者增加,换手率变小,流动性风险仍不可忽视。
(7)根据碳市场参与主体的多样性,套期保值者的行为特征决定着套期保值的目标函数。从行为金融学角度度出发,运用失望压恶非期望效用模型研究失望厌恶和风险厌恶对期货最优套期保值策略的影响，并进一步模拟分析了碳市场进行套期保值成本。结果表明以方差最小化的套期保值在第一阶段作用微弱,套期保值比在0.1左右。第二阶段套期保值比在0.4左右。碳市场最优套期保值比低于一般市场。碳市场上,失望厌恶和风险厌恶系数极大或者极低时,对市场的判断容易产生偏差。失望和风险厌恶变大时,投资收益参考点会下降。同时,碳市场价格趋势影响投资者收益的心理预期,调整好投资心态有利于市场操作。失望厌恶与风险厌恶之间替代效应不明显。随着风险厌恶的减小,失望厌恶不一定会增大。失望厌恶和风险厌恶会改变投资者的最优投资策略。模拟表明碳市场上期货与现货的收益率序列并不存在线性相关性。目前期货市场尚不能很好的为现货市场提供套期保值的功能。
通过以上研究,有助于进一步加深对碳市场复杂系统价格波动机制和风险管理的认识和理解,为碳价预测、碳市场风险管理提供信息支持,为市场监管者、投资者等有关机构的决策提供参考依据和政策支持。
The carbon market is cost-effective for addressing climate change, with the response to the rapid development of the Climate Change Action and the international carbon market, carbon market and its financial property has become one of the hotspot fields of energy economics research. Carbon market especially the carbon price volatility is universally acknowledged to be commonly influenced by numerous factors and appears a typical complex system, with nonlinear, nondeterministic, comprehensive and dynamic features etc. During recent years, the confluence of many contributors, such as international negotiations, the financial crises, important notices, has led to great volatility and complex changes. Carbon market risk has been brought into focus. However, existing related literature appear to be scanty to quantify the carbon market risk; as for the research methods, they tend to use some traditional paths, such as the normal distribution, linear regression and ordinary least squares (OLS) etc., which seem to be quite limited and insufficient to well define the complexity of carbon market risk management.
Motivated by several scientific topics within carbon price volatility and market risk management, this thesis singles out some empirical study methodologies, including econometric models, statistical approaches and financial market risk management theories etc., so as to model the features of carbon price volatility, discuss the directions of carbon market risk information transfer, and analyze the primary factors of oil price changes and their influencing mechanism. A relatively systematic recognition for the main characteristics of oil market risk management will come into being and the carbon market risk management techniques and cost will be analyzed.
In brief, the highlights of our research in this thesis can be summarized as follows.
(1) By proposing the hypotheses for carbon price volatility, variance ratio and Ensemble Empirical Mode Decomposition (EEMD) will be used to analyze the carbon price. Results show that carbon market is temperature-sensitive, affected by seasonal changes, which presents a style of movement amplitude; carbon price is affected by the market mechanism at a high frequency, with the duration being less than20weeks and amplitudes less than3euros; heterogeneity environment has an impact on carbon price at a low frequency, the duration lasting more than35weeks or even more and amplitudes more than6euros or higher. Historical carbon price change shows the long-term trend declines gradually since2005from18to16euros per ton. The continuing declining trend agrees with special events by time. Analyzing the composition of carbon price from low frequencies and high frequencies helps understand the underlying rules of carbon price reality
(2) Based on the research, we analyze carbon price volatility from a nonlinear dynamics point of view. First, we use a random walk model, including serial correlation and variance ratio tests, to determine whether carbon price history information is fully reflected in current carbon price. The empirical research results show that carbon price is not a random walk:the price history information is not fully reflected in current carbon price. Second, use R/S, modified R/S and ARFIMA to analyse the memory of carbon price history. For the period April2005-December2008, the modified Hurst index of the carbon price is0.4859and the d value of ARFIMA is0.1191, indicating short-term memory of the carbon price. Third, we use chaos theory to analyse the influence of the carbon market internal mechanism on carbon price, i.e., the market's positive and negative feedback mechanism and the heterogeneous environment. Chaos theory proves that the correlation dimension of carbon price increases. The maximal Lyapunov exponent is positive and large. There is no obvious complex endogenous phenomenon of nonlinear dynamics the carbon price fluctuation. The carbon market is mildly chaotic, showing both market and fractal market characteristics. Price fluctuation is not only influenced by the internal market mechanism, but is also impacted by the heterogeneous environment.
(3) Zipf analysis technology is used to assess carbon price volatility under different expectations of returns and time scales. The results show the sensitivity of the futures returns to the market's returns is lower in the second phase than the first phase. At longer time scales, the probability of prices declining becomes greater than the probability of prices increasing. Traders with different expectations of returns have different price perceptions. For traders with low expectations of returns, carbon prices are affected by market mechanisms, seasonal weather variations and other heterogeneous events, and carbon price fluctuations are relatively well perceived. Carbon prices are more volatile and higher risks and uncertainties are more characteristic for high expectations of returns.
(4) For the spot and futures prices dependencies carbon market, the chapter uses ①CC-GARCH to analyze the dynamic correlation between spot and futures price,②tructural breakpoint detection model to analyze the major events of carbon market,③copula function to analyze the dependencies change before and after the major events in the spot and futures. The results show that there is a strong correlation between spot and futures. The explanatory power of spot price volatility by the futures price is more than50%. A major special event had a visible change on correlation between spot and futures, whose influence is large. The correlation presents enhancements to the weakening trend characteristics in the first phase and the correlation showed a weakening trend after the financial crisis in the second phase, which show that the carbon market development is influenced by special events.
(5) Carbon market risk directly affects the investor confidence and emission reduction results. In the present study, extreme value theory (EVT) is used to analyze risk exposure for carbon price and to measure the Value at Risk (VaR) for the carbon market. GARCH models are applied to establish a model of price volatility for the spot market and the futures market and to calculate dynamic VaR. Traditional VaR and VaR based on EVT are also compared. The results show that the downside risk is higher than the upside risk for the carbon market. Upside and downside risks are higher in the first phase (Jun2005-Dec2007) than in the second phase (Feb2008-Dec2009) for both the spot and futures markets. Upside and downside risks are similar for the spot and futures markets during the same phase. The results also show that the EVT VaR is more effective than the traditional method, which can reduce the risks for market participants. Dynamic VaR based on GARCH and EVT can effectively measure the EU ETS market risk.
(6) For liquidity risk in carbon markets, the chapter builds carbon market liquidity indicators from trading spreads, trading volume, turnover rate for the first time, analyzing the liquidity risk in carbon markets. The Generalized Pareto Distribution and the Copula function is used to analyze dependencies and integration of market risk and liquidity risk market risk. The Empirical study found that the dependency of market risk and liquidity risk in carbon markets is weak. Subject to market size restrictions, the market liquidity risk in carbon market is small, but ignoring market liquidity will underestimate carbon market risk, liquidity risk reach9%of the market risk in one day for each contract, which shows liquidity risk increasing greatly for investors holding lots of contracts. Carbon market development can help to reduce liquidity risk, but liquidity risk still cannot be ignored with the turnover rate becoming smaller.
(7) The behavioral characteristics of hedgers determine the hedging objective function in carbon market. Starting from a behavioral finance point of view, the chapter uses non-expected utility model of disappointment aversion to①analyze the optimal hedging strategy influence by disappointment aversion and risk aversion;②simulate hedge costs of the carbon market. The results show that the hedge ratio is small according to the minimum variance, which is about0.1in the first phase of and0.4in the second phase. The optimal hedge ratio is smaller than the general market. In carbon market, the market judgment is prone to bias when disappointment aversion coefficient and risk aversion coefficient is high or very low. When the disappointment and the risk aversion are big, the reference point of investment income will decline in the carbon market. Investors'expectation psychological is influenced by carbon price trends. Adjusting investment mentality is conducive to market operations. The substitution effect between risk aversion and disappointment aversion is not obvious, the disappointment aversion effect maybe not increase when the risk aversion decreases. Disappointment aversion and risk aversion will change investors'optimal investment strategy. The simulation shows that the return of the futures and spot carbon market has not a linear correlation. The hedging feature is not good in carbon market.
To sum up, based on the research above, this thesis is aimed to strengthen therecognition of carbon market complex system in terms of price volatility and risk management, and proposesome scientific information support for carbon price forecast, macroeconomic research, investment decision-making and market regulating of related institutions.
引文
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