基于混合深度学习的碳排放权购买策略研究--基于上海试点市场
A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market
摘要：
碳排放权价格在碳交易市场中起着至关重要的作用。因此，对价格的准确预测至关重要。以上海试点市场为例，尝试为企业设计碳排放权购买策略，并建立碳排放权价格预测模型，帮助企业降低购买成本。为了使预测更精确，我们通过将广义自回归条件异方差（GARCH）嵌入到门递归单元（GRU）模型中来构建混合深度学习模型，并将其性能与其他模型进行比较。在此基础上，基于冰山秩序理论和预测价格，提出了碳排放权的购买策略。结果表明，GARCH-GRU模型的预测误差在所有6个模型中最小，滑动时间窗为5天。在仿真中，基于GARCH-GRU模型的采购策略也以最小的成本被执行。采用混合深度学习方法构建的碳排放权购买策略能够准确发出时机信号，帮助企业降低碳排放权购买成本。
英文摘要：
The price of carbon emission rights play a crucial role in carbon trading markets. Therefore, accurate prediction of the price is critical. Taking the Shanghai pilot market as an example, this paper attempted to design a carbon emission purchasing strategy for enterprises, and establish a carbon emission price prediction model to help them reduce the purchasing cost. To make predictions more precise, we built a hybrid deep learning model by embedding Generalized Autoregressive Conditional Heteroskedastic (GARCH) into the Gate Recurrent Unit (GRU) model, and compared the performance with those of other models. Then, based on the Iceberg Order Theory and the predicted price, we proposed the purchasing strategy of carbon emission rights. As a result, the prediction errors of the GARCH-GRU model with a 5-day sliding time window were the minimum values of all six models. And in the simulation, the purchasing strategy based on the GARCH-GRU model was executed with the least cost as well. The carbon emission purchasing strategy constructed by the hybrid deep learning method can accurately send out timing signals, and help enterprises reduce the purchasing cost of carbon emission permits.
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