碳交易价格预测研究——以深圳市为例
|
摘要
碳市场作为国内新兴市场,受到国家减排政策市场机制、国际市场等诸多因素的影响,而碳价格作为碳市场是否成熟的风向标,挖掘其规律特征尤为重要。本文将碳市场无法获得、不能度量的系统信息视作灰色系统,使用分数阶灰色预测模型预测碳价格的变化规律。分数阶灰色预测模型通过使平均相对误差最小,基于PSO粒子群算法理论,找出准确反映数据变化特征的模型,从而达到精准预测的效果。本文以深圳市为例,用分数阶灰色预测模型预测了平均碳价的变化趋势。研究表明当模型的阶数为1.76时为最优模型,通过该模型预测深圳市2018-2021年碳平均价格发现,深圳市碳价将稳定在30元/吨至38元/吨的区间中。这说明在目前监管形势下,碳价格基本平稳运行,不会出现碳价格的大涨大跌。
As a domestic emerging market, carbon market is affected by many factors such as the market mechanism of national emission reduction policy, international market and many other factors, it is particularly important to explore the characteristicsof the carbon price sincecarbon price isthe indicatorof carbon market. In this paper, the information that cannot be obtained and measured in carbon market is regarded as a grey system, and the fractional grey prediction model is employed to predict the carbon price,by minimizing the average relative error and based on the particle swarm optimization(PSO) theory, the fractional grey prediction model can reflect the characteristics of dataso as to achieve the accurateprediction.Shenzhen city is taken as an example to demonstrate the effectiveness of the fractional grey prediction mode, and the trend of average carbon price is predicted by fractional grey prediction model. The study shows thatthe optimal fractional grey model is the model when the order is 1.76,and by this model the carbon price of Shenzhen cityis forecasted in 2018-2021, the prediction indicates that the price will be stable in the range of30 yuan/ton to 38 yuan/ton. This study shows that under the current regulatory situation, the carbon price is running smoothly and there will be no sharp rise or fall in the carbon price.
As a domestic emerging market, carbon market is affected by many factors such as the market mechanism of national emission reduction policy, international market and many other factors, it is particularly important to explore the characteristicsof the carbon price sincecarbon price isthe indicatorof carbon market. In this paper, the information that cannot be obtained and measured in carbon market is regarded as a grey system, and the fractional grey prediction model is employed to predict the carbon price,by minimizing the average relative error and based on the particle swarm optimization(PSO) theory, the fractional grey prediction model can reflect the characteristics of dataso as to achieve the accurateprediction.Shenzhen city is taken as an example to demonstrate the effectiveness of the fractional grey prediction mode, and the trend of average carbon price is predicted by fractional grey prediction model. The study shows thatthe optimal fractional grey model is the model when the order is 1.76,and by this model the carbon price of Shenzhen cityis forecasted in 2018-2021, the prediction indicates that the price will be stable in the range of30 yuan/ton to 38 yuan/ton. This study shows that under the current regulatory situation, the carbon price is running smoothly and there will be no sharp rise or fall in the carbon price.
引文
[1]Feng Z,Liu C,Wei Y.How does carbon price change?Evidences from EU ETS[J].CEEP-BIT Working Paper 11,2010.
[2]Zhang Y, Wei Y. An overview of current research on EUETS:evidence from its operating mechanism and economic effect[J]. Applied Energy, 2010(6).
[3]陈晓红、王陟昀.碳排放权交易价格影响因素实证研究—以欧盟排放交易体系(EUETS)为例[J].系统工程, 2012(2).
[4]Byun S J, Cho H J. Forecasting carbon futures volatility using GARCH models withenergy volatilities. Energy Economics, 2013(40).
[5]郑爽.七省市碳交易试点调研报告[J].中国能源, 2014(2).
[6]Fan X,Li S,Tian L.Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model[J].Expert Systems with Applications,2015(8).
[7]Li W, Lu C.The research on setting a unified interval of carbon price benchmarkin the national carbon trading market of China[J]. Applied Energy, 2015.
[8]孟伟、刘思峰、方志耕、曾波.基于互逆分数阶算子的GM(1,1)阶数优化模型[J].控制与决策,2016(4).
[9]赵立祥、胡灿.我国碳排放权交易价格影响因素研究——基于结构方程模型的实证分析[J].价格理论与实践,2016(7).
[10]王娜.基于大数据的碳价预测[J].统计研究,2016(11).
[11]易兰、李朝鹏、杨历、刘杰.欧盟碳市场价格走势的情景模拟分析及对中国的启示[J].环境经济研究,2017(3).
[12]王倩、路京京.人民币汇率对中国碳价的冲击效应—基于区域差异的视角[J].武汉大学学报(哲学社会科学版), 2018(2).
[13]蒋锋,彭紫君.基于混沌PSO优化BP神经网络的碳价预测[J].统计与信息论坛,2018(5).
[14]李旸、陈浩苗.环保政策对我国碳排放权交易价格的影响研究——以湖北、广东和深圳碳排放权交易试点为例[J].国土资源科技管理,2018(4).
[15]杜子平、刘富存.我国区域碳排放权价格及其影响因素研究——基于GA-BP-MIV模型的实证分析[J].价格理论与实践,2018(6).
[16]Guo J F, Su B,Yang G, Feng LY, Liu YP, Gu F. How do verified emissions announcements affect the comoves between trading behaviors and carbon prices? Evidence from EUETS. 2018(9).
[17]郑祖婷、沈菲、郎鹏.我国碳交易价格波动风险预警研究———基于深圳市碳交易市场试点数据的实证检验[J].价格理论与实践,2018(10).
[18]魏琦、金卓然.化石能源价格变动对中国碳交易价格的影响研究[J].价格理论与实践,2018(11).
[2]Zhang Y, Wei Y. An overview of current research on EUETS:evidence from its operating mechanism and economic effect[J]. Applied Energy, 2010(6).
[3]陈晓红、王陟昀.碳排放权交易价格影响因素实证研究—以欧盟排放交易体系(EUETS)为例[J].系统工程, 2012(2).
[4]Byun S J, Cho H J. Forecasting carbon futures volatility using GARCH models withenergy volatilities. Energy Economics, 2013(40).
[5]郑爽.七省市碳交易试点调研报告[J].中国能源, 2014(2).
[6]Fan X,Li S,Tian L.Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model[J].Expert Systems with Applications,2015(8).
[7]Li W, Lu C.The research on setting a unified interval of carbon price benchmarkin the national carbon trading market of China[J]. Applied Energy, 2015.
[8]孟伟、刘思峰、方志耕、曾波.基于互逆分数阶算子的GM(1,1)阶数优化模型[J].控制与决策,2016(4).
[9]赵立祥、胡灿.我国碳排放权交易价格影响因素研究——基于结构方程模型的实证分析[J].价格理论与实践,2016(7).
[10]王娜.基于大数据的碳价预测[J].统计研究,2016(11).
[11]易兰、李朝鹏、杨历、刘杰.欧盟碳市场价格走势的情景模拟分析及对中国的启示[J].环境经济研究,2017(3).
[12]王倩、路京京.人民币汇率对中国碳价的冲击效应—基于区域差异的视角[J].武汉大学学报(哲学社会科学版), 2018(2).
[13]蒋锋,彭紫君.基于混沌PSO优化BP神经网络的碳价预测[J].统计与信息论坛,2018(5).
[14]李旸、陈浩苗.环保政策对我国碳排放权交易价格的影响研究——以湖北、广东和深圳碳排放权交易试点为例[J].国土资源科技管理,2018(4).
[15]杜子平、刘富存.我国区域碳排放权价格及其影响因素研究——基于GA-BP-MIV模型的实证分析[J].价格理论与实践,2018(6).
[16]Guo J F, Su B,Yang G, Feng LY, Liu YP, Gu F. How do verified emissions announcements affect the comoves between trading behaviors and carbon prices? Evidence from EUETS. 2018(9).
[17]郑祖婷、沈菲、郎鹏.我国碳交易价格波动风险预警研究———基于深圳市碳交易市场试点数据的实证检验[J].价格理论与实践,2018(10).
[18]魏琦、金卓然.化石能源价格变动对中国碳交易价格的影响研究[J].价格理论与实践,2018(11).