Abstract

Because of insufficient liquidity, prices in the carbon market are more vulnerable to unexpected events, for which the impact duration lasts longer than that of the general market. The root reason for this phenomenon lies in the irrationality of quota distribution. The existing quota adjustment schemes and policies, e.g., the market stability reserve (MSR) and some recent adjustment measures, have poor timeliness and effectiveness, which has increased the risk of market crashes. Using the Hidden Markov Model (HMM), this paper develops a new dynamic quota adjustment scheme that can rapidly reduce the risk of quota supply by bridging quota price and quantity with price feedback as a response signal. To achieve this, we integrated the HMM algorithm and a two-step quota adjustment model by setting price thresholds and then connected the quota adjustment transition matrix and historical quota price. By comparing the MSR from 2013 to 2018, our scheme will help mitigate risks in quota price because the HMM can show the actual impact of price feedback on quota adjustment with merits of steady quota price and timely supply optimization. Moreover, our scheme, which recalculates the transition matrix, can be applied in other mature carbon markets.

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