Abstract
To identify the jump forms of carbon asset price under the shocks of significant information, this paper focuses on two aspects: different jump directions and jump time points. Therefore, a method for simultaneously optimizing time window size and threshold of the LM jump test is proposed. This optimized method has reduced the omission of jump points and improves the effectiveness of the model. Results show that the shocks of significant information cause large magnitude and asymmetric successive jumps of carbon asset price. A downward high-frequency jump indicates that the downward risk of carbon market is significant, and policy events have the biggest impact on the carbon market, which leads to the biggest jump magnitude. This research helps improve the efficiency of carbon pricing, provides practical guidance for decision process of enterprises, and prevents risk in a timely manner.
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