An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms
作者: Xiaojia LiuHaizhong AnLijun WangXiaoliang Jia
作者单位: 1School of Humanities and Economic Management, China University of Geosciences, Beijing 100083, China
2Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Land and Resources, Beijing 100083, China
3Lab of Resources and Environmental Management, China University of Geosciences, Beijing 100083, China
刊名: Applied Energy, 2016
来源数据库: Elsevier Journal
DOI: 10.1016/j.apenergy.2016.01.045
关键词: Carbon emission tradingEUA futures marketMoving average trading rulesParticle swarm optimizationGenetic algorithms
英文摘要: Abstract(#br)Climate change is a big challenge facing global community in 21st century. The carbon emission futures markets has been treated as a key tool to combat climate change cost-effectively. Making profits from futures trading is the fundamental incentive mechanism to keep this market run sustainably and effectively, while few technique analysis research on this topic has been done in the energy finance field. This paper contributes to the literature by proposing an integrated moving average rule for the European Union Allowance (EUA) futures market and designing an approach to optimize the weights of rules based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs). The similarity of trading rules designed here is used to select base rules. An integrated approach based...
全文获取路径: Elsevier  (合作)
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影响因子:4.781 (2012)

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