The Driving Relationship of China Carbon Price Based on Different Market Volatility States

The China carbon market is a market-oriented designation for addressing climate issues. Price mechanism is the core of carbon market, studying on the price formation can promote the emission reduction targets. This paper conducts an autoregressive adjusted Markov model to divide the carbon price state, and designs a multiple regression model to test the driving mechanism. The results show the 2 order autoregressive Markov model of MS (2) - AR (2) model can classify the carbon price into high and low volatility states. Furthermore, in high volatility state, carbon price is only significantly positively correlated with macroeconomic factors of China Securities Index 300 (CSI300) and European carbon price of European emission allowance future contract (EUAF), while in low volatility state, carbon price is significantly positively influenced by energy market products JM future (JMF) and Oil, macroeconomic factor of CSI300, and European carbon price of EUAF. Furthermore, the impact strength is weaker than the whole sample regression results. The results provide reference for investors to judge carbon price and reveal price trends.

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Publication Details

Journal
Asian Academy of Management Journal of Accounting and Finance
Published
2024-12-27
DOI
https://doi.org/10.21315/aamjaf2024.20.2.7
Primary Topic
Energy, Environment, and Transportation Policies
Type
article
Field-Weighted Citation Impact
0.00

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article

The Driving Relationship of China Carbon Price Based on Different Market Volatility States

Li Ni, Venus Khim‐Sen Liew
Asian Academy of Management Journal of Accounting and Finance
Energy, Environment, and Transportation Policies
article

The Driving Relationship of China Carbon Price Based on Different Market Volatility States

Li Ni, Venus Khim‐Sen Liew
article en

Abstract

The China carbon market is a market-oriented designation for addressing climate issues. Price mechanism is the core of carbon market, studying on the price formation can promote the emission reduction targets. This paper conducts an autoregressive adjusted Markov model to divide the carbon price state, and designs a multiple regression model to test the driving mechanism. The results show the 2 order autoregressive Markov model of MS (2) - AR (2) model can classify the carbon price into high and low volatility states. Furthermore, in high volatility state, carbon price is only significantly positively correlated with macroeconomic factors of China Securities Index 300 (CSI300) and European carbon price of European emission allowance future contract (EUAF), while in low volatility state, carbon price is significantly positively influenced by energy market products JM future (JMF) and Oil, macroeconomic factor of CSI300, and European carbon price of EUAF. Furthermore, the impact strength is weaker than the whole sample regression results. The results provide reference for investors to judge carbon price and reveal price trends.

Asian Academy of Management Journal of Accounting and FinanceVol. 20(2)
Universiti Malaysia Sarawak (MY)
Universiti Malaysia Sarawak
Climate action
Openalex Percentile: Top 85%
Energy, Environment, and Transportation Policies
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The Driving Relationship of China Carbon Price Based on Different Market Volatility States — Li Ni, Venus Khim‐Sen Liew · Asian Academy of Management Journal of Accounting and Finance (2024) | TGRS Research Map | TGRS