针对国际碳市场价格预测LSSVM建模输入节点和模型参数难以确定的问题, 建立了基于数据分组处理方法(GMDH)-粒子群算法(PSO)-最小二乘支持向量机(LSSVM)的国际碳市场价格预测模型. 首先利用GMDH算法获得LSSVM建模中的输入变量; 其次应用PSO算法对LSSVM建模中的参数进行优化, 进而使用训练好的LSSVM模型对测试样本进行预测; 最后采用该模型对欧盟排放交易体系(EU ETS)两个不同到期时间的碳期货价格(DEC 10和DEC 12)进行实证分析, 取得了令人满意的效果.
Abstract
Aiming at the problems of determining the inputs and parameters for least squares support vector machines (LSSVM) modeling, this paper presents an integrated model of group method of data handling (GMDH), particle swarm optimization (PSO) and LSSVM, i.e., GMDH-PSO-LSSVM, for international carbon price prediction. First, GMDH is used to make the selection of input-layer units easily. Next, PSO is used to train LSSVM model with the training samples and obtain the optimal parameters. Then, the trained LSSVM is used to forecast carbon price of the testing samples. Finally, taking two carbon futures prices with different maturity called DEC 10 and DEC 12 of European Union emissions trading scheme (EU ETS) as samples, empirical results show that the proposed model is an effective way to improve forecasting accuracy.
关键词
碳价预测 /
欧盟排放交易体系 /
数据分组处理方法 /
粒子群算法 /
最小二乘支持向量机
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Key words
carbon price prediction /
EU ETS /
group method of data handling /
particle swarm optimization /
least squares support vector machines
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中图分类号:
TP18
F830
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参考文献
[1] 魏一鸣, 王恺, 凤振华, 等. 碳金融与碳市场: 方法与实证[M]. 北京: 科学出版社, 2010. Wei Y M, Wang K, Feng Z H, et al. Carbon Finance and Carbon Market: Models and Empirical Analysis[M]. Beijing: Science Press, 2010.
[2] Alberola E, Chevallier J, Cheze B. Price drivers and structural breaks in European carbon prices 2005-2007[J]. Energy Policy, 2008, 36(2): 787-797.
[3] Benz E, Truck S. Modeling the price dynamics of CO2 emission allowances[J]. Energy Economics, 2009, 31(1): 4-15.
[4] Chevallier J. Volatility forecasting of carbon prices using factor models[J]. Economics Bulletin, 2010, 30(2): 1642-1660.
[5] Daskalakis G, Psychoyios D, Markellos R N. Modeling CO2 emission allowance prices and derivatives: Evidence from the European trading scheme[J]. Journal of Banking & Finance, 2009, 33(7): 1230-1241.
[6] Hintermann B. Allowance price drivers in the first phase of the EU ETS[J]. Journal of Environmental Economics and Management, 2010, 59: 43-56.
[7] Zhang Y J, Wei Y M. An overview of current research on EU ETS: Evidence from its operating mechanism and economic effect[J]. Applied Energy, 2010, 87(6): 1804-1814.
[8] http://discover.news.163.com/10/0201/10/5UE9C2P300 0125LI. html.
[9] Yu L, Wang S Y, Lai K K. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates[J]. Computer & Operation Research, 2005, 32: 2523-2541.
[10] Vapnik V N. Statistical Learning Theory[M]. NewYork: Wiley, 1998.
[11] Suykenns J A K, Vandewalle J. Least squares support vector machine[J]. Neural Processing Letter, 1999, 9(3): 293-300.
[12] Lemke F, Muller J A. Self-organizing data mining[J]. Systems Analysis Modeling Simulation, 2003, 43: 231-240.
[13] 邹昊飞, 夏国平, 杨方廷. 基于两阶段优化算法的神经网络预测模型[J]. 管理科学学报, 2006, 9(5): 28-35. Zou H F, Xia G P, Yang F T. Neural network forecasting model using multi-stage optimization approach based on GMDH and genetic algorithm[J]. Journal of Management Sciences in China, 2006, 9(5): 28-35.
[14] Kennedy J, Eberhart R C. Particle swarm optimization[C]// Proc IEEE Conf on Neural Networks, Perth: Piscataway, 1995(4): 1942-1948.
[15] 张跃军, 魏一鸣. 化石能源市场对国际碳市场的动态影响实证研究[J]. 管理评论, 2010, 21(6): 34-41. Zhang Y J, Wei Y M. Interpreting the complex impact of fossil fuel markets on the EU ETS futures markets: An empirical evidence[J]. Management Review, 2010, 21(6): 34-41.
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脚注
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基金
国家自然科学基金(71020107026, 70733005); 国家博士后科学基金(201104057); 国家教育部人文社会科学青年基金(11YJC630304)
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