Abstract

In this study, we analyze the risk of extreme value dependence in Chinese regional carbon emission markets. After filtering the daily return data of six carbon markets in China using a generalized autoregressive conditional heteroscedasticity (GARCH) model, we obtain the standardized residual series. Next, the dependence structures in the markets are captured by the Copula function and the Extreme Value theory (EVT). We report high peaks, heavy tails and fluctuation aggregation in the logarithm return series of the markets, as well as significant dependent structures. There are significant extreme value risks in Chinese regional carbon markets, but the risks can be mitigated through appropriate portfolio diversification.

Highlights

  • To deal with global climate change and comply with the call for international emission reduction, China established its carbon emission market at the end of 2011

  • We focus on the price risk of China’s carbon emission markets, which can be classified as a component of market operation risk

  • In this study, we analyze the risk of extreme value dependence in Chinese regional carbon emission markets

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Summary

Introduction

To deal with global climate change and comply with the call for international emission reduction, China established its carbon emission market at the end of 2011. Located in the major cities and areas across China, including Beijing, Shanghai, Tianjin, Hubei, Guangdong, Shenzhen, Chongqing, and Fujian, the markets have developed rapidly and exhibit important characteristics similar to the European Union’s Emissions-Trading Market. These regional markets have made great progress toward coverage, transaction quota and amount, and performance of participating enterprises, etc. Our paper focuses on the risk of extreme value dependence in Chinese regional carbon emission markets using a GARCH-Copula model. An effective system to monitor and manage the dependence risk of extreme value is critically important for market participants, and it helps policy makers to better improve market efficiency. A global and healthy development of the carbon emission market will help to mitigate global climate change, develop a low-carbon economy, and achieve sustainable development

Literature Review
AR-GARCH Model
Copula Function
Data and Empirical Analysis
Autocorrelation Analysis
Evaluation of EVT Model
VaR Calculation
Discussion

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