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基于ceemdan和优化lstm模型的碳价波动率预测研究
首发时间：2024-02-06
摘要：二化碳过度排放已成为当前社会面临的严峻挑战，建立碳排放交易市场可以有效降低社会碳排放量，促进经济绿色转型。中国政府在2020年提出双碳目标，国内碳市场建设正处于全国统一碳市场发展阶段，丰富碳价格波动率研究对于建立合理定价机制和碳配额分配制度有着重要意义。本文以北京碳配额交易价格实际波动率为研究对象，构建以自适应噪声完备集合经验模态分解和长短期记忆网络为基础的混合预测模型，以粒子群算法优化模型结构参数。实验结果证明：该模型具备提取多尺度复杂时间序列波动趋势和有效处理金融时间序列的优点，粒子群算法对预测模型结构参数的优化避免了因参数选取不当导致的拟合问题，该模型在碳价波动率预测方面具备显著的准确性和稳定性。
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research on carbon price volatility prediction based on ceemdan and optimized lstm model
abstract：excessive carbon dioxide emissions have become a serious challenge facing the current society. establishing a carbon emissions trading market can effectively reduce social carbon emissions and promote green economic transformation. the chinese government proposed the goals of carbon peaking and carbon neutrality in 2020, and the construction of the domestic carbon market is currently in the stage of developing a unified national carbon market. enriching research on carbon price volatility is of great significance for establishing a reasonable pricing mechanism and carbon quota allocation system. this paper takes the realized volatility of beijing carbon emission allowance trading prices as the research object, constructs a hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise and long short-term memory network, and optimizes the model structural parameters through particle swarm optimization algorithm. the experimental results demonstrate that the model has the advantages of extracting multi-scale complex time series volatility trends and effectively processing financial time series. the particle swarm optimization algorithm optimizes the structural parameters of the forecasting model to avoid fitting problems caused by improper parameter selection. the model has significant accuracy and stability in forecasting carbon price volatility.
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基于ceemdan和优化lstm模型的碳价波动率预测研究
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