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新冠疫情下碳排放权期权定价及实证研究
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Abstract:
随着低碳经济时代的到来，不论是理论研究还是实际应用方面，碳排放权的定价问题越来越受到大家的关注，尤其是研究新冠疫情下碳排放权期权定价问题。本文借助于决策树梯度提升算法(LGBM)、自适应噪声的完全经验模态分解(CEEMDAN)及AE近似熵重构算法，集机制转换的跳–扩散模型(RSJM)、径向基神经网络模型(RBF)的优点，建立了一种不同特征维度下RSJM-RBF碳排放权期权定价模型；并利用新冠疫情下欧盟EUA碳排放权期权数据进行了实证分析。实证结果表明：与基于GARCH的分数布朗运动模型(FBM)相比，本文提出的RSJM-RBF碳排放权期权定价模型具有较高的预测精度；以皮尔逊相关系数为指标，对两种模型进行了鲁棒性分析，结果显示本文所建模型的稳健性更强。
With the advent of the era of low-carbon economy, the pricing of carbon emission rights has at-tracted more and more attention from both theoretical research and practical application, espe-cially the pricing of carbon emission rights options under the COVID-19. In this paper, with the help of the gradient lifting algorithm of decision tree (LGBM), complete empirical mode decomposition with adaptive noise (CEEMDAN) and the approximate entropy reconstruction (AE) algorithm, and the advantages of the jump-diffusion model of mechanism conversion (RSJM) and the radial basis function neural network model (RBF), a RSJM-RBF carbon emission option pricing model with dif-ferent feature dimensions is established. The empirical analysis is made by using the data of EU-EUA carbon emission option under the COVID-19. The empirical results show that compared with the fractional Brownian motion model (FBM) based on GARCH, the RSJM-RBF carbon emission option pricing model proposed in this paper has higher prediction accuracy. Taking Pearson corre-lation coefficient as the index, the robustness analysis of the two models shows that the model es-tablished in this paper is more robust.
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