Nonlinear Risk Spillover Path Between China’s Carbon Market, China’s New Energy Market, and the International Crude Oil Futures Market

Author:

Yao Yanyun1,Tang Zifeng2,Niu Guiqian1,Cai Shangzhen3

Affiliation:

1. College of Finance & Information, Ningbo University of Finance & Economics, 899 Xueyuan Road, Haishu District, Ningbo, Zhejiang 315175, China

2. Department of Mathematics, Shaoxing University, 900 Chengnan Avenue, Yuecheng District, Shaoxing, Zhejiang 312000, China

3. College of Digital Technology and Engineering, Ningbo University of Finance & Economics, 899 Xueyuan Road, Haishu District, Ningbo, Zhejiang 315175, China

Abstract

The carbon market was established to reduce carbon dioxide emissions. The traditional fossil energy market, new energy market, and carbon market have interrelated effects such as substitution, demand, and production inhibition, which can potentially lead to risk transmission. This study examines the nonlinear volatility correlation between China’s carbon market, China’s new energy market, and the international crude oil futures market. Seven submarkets within these three markets are selected for analysis. By measuring volatility risk through the conditional heteroscedasticity of returns, the analysis of nonlinear Granger causality networks reveals that, from a nonlinear perspective, risk primarily spills over through the paths of “International crude oil futures market → China’s carbon market” and “International crude oil futures market → China’s new energy market → China’s carbon market.” China’s carbon market serves as a recipient of risk, with minimal spillover effects. Therefore, further optimization is needed for the framework of China’s carbon market to enhance its asset allocation function and promote its spillover influence. Investors in China’s carbon market should consider both linear and nonlinear risks from China’s new energy market and the international crude oil futures market, and take appropriate measures to facilitate the sustainable growth of Chinese enterprises.

Funder

Statistical Research Project of Zhejiang Province

Project of China Business Statistics Society

National Social Science Fund of China

Zhejiang Province Quantitative Economics Society Project

Publisher

Fuji Technology Press Ltd.

Reference34 articles.

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