Asymmetric risk spillover between financial market uncertainty and the carbon market: A GAS–DCS–copula approach

Yuan, Nannan; Yang, Lu

doi:10.1016/j.jclepro.2020.120750

Cited by 63 publications

(21 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The characteristics are similar to those of the European market. In Europe, the uncertainty of the crude oil market has greater influence on the carbon market than stock market uncertainty does, and its upper tail risk spillover is greater than the lower tail risk spillover in terms of absolute value (Yuan & Yang, 2020 ). Wen et al ( 2020a , b ), Wen & Hu et al ( 2020 )found significantly negative long- and short-term asymmetric relationships between the carbon emission trading market and stock market in China, but no significant effects passing from the stock index to the carbon emission trading price.…”

Section: Discussionmentioning

confidence: 99%

High-dimensional nonlinear dependence and risk spillovers analysis between China’s carbon market and its major influence factors

Zhang

Tian

et al. 2022

Ann Oper Res

View full text Add to dashboard Cite

In July 2021, China began its national emissions trading scheme, marking a new stage of development for the country’s carbon market. This study analyzes the multidimensional correlation between carbon prices in the Guangdong pilot market and eight influencing factors from three perspectives (the international carbon market, energy prices, and China’s economic situation), using the ARMA-GARCH-vine copula model. The CoVaR between the carbon price and each factor is then calculated using copula-CoVaR. The results show that the crude oil market plays the primary role in the vine structure, and that the carbon market is not strongly correlated with other markets. China’s carbon market is still a regional market driven by government policy, and the international carbon and energy markets (especially the crude oil market) have upward risk spillover effects upon it. This indicates an asymmetric risk spillover between influencing factors and the carbon market. The findings of this study will help market participants prepare risk management strategies and make related investment decisions, and provide a reference for policy makers to formulate national emission trading scheme policies. Graphical abstract

show abstract

Section: Discussionmentioning

confidence: 99%

High-dimensional nonlinear dependence and risk spillovers analysis between China’s carbon market and its major influence factors

Zhang

Tian

et al. 2022

Ann Oper Res

View full text Add to dashboard Cite

show abstract

“…Furthermore, we employed the GAS model proposed by Creal et al [16] and Yuan and Yang [32] to update the current value of the variable from its lagged information. We considered three dynamic models for the comparison.…”

Section: Semiparametric Modelsmentioning

confidence: 99%

Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach

Yang

Hamori

2020

Energies

Self Cite

View full text Add to dashboard Cite

We propose the use of wavelet-based semiparametric models for forecasting the value-at-risk (VaR) and expected shortfall (ES) in the crude oil market. We compared the forecast outcomes across different time scales for three semiparametric models, three nonparametric, distribution-based, generalized, autoregressive, conditional, heteroskedasticity (GARCH) models, and three rolling-window models. We found that the GARCH model estimated by the Fissler and Ziegel (FZ) zero loss minimization (GARCH-FZ) model performs the best at forecasting the VaR and ES in the short term, whereas the hybrid model performs the best for mid- and long-term time scales. Thus, long-term investors should consider the hybrid model and short-term investors should employ the GARCH-FZ model in their risk management processes. Overall, our proposed wavelet-based semiparametric models outperform the other models tested for all time scales and market conditions. As such, we suggest that these models are considered for the management of crude oil price risk and in the development of energy policy.

show abstract

“…Based on the “Kyoto Protocol” signed in 2005 and the “Paris Agreement” passed in 2015, the carbon allowance assets traded in the carbon market have commodity and financial attributes, and there exists three exclusive characteristics that cannot to be ignored compared with other capital markets. The first is the asymmetric distribution of market returns, the tail distribution has the characteristics of left deviation, and the skewness is negative [ 3 , 4 , 5 ]. The second is the high sensitivity to policy events or external events [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Yun

Zhang

et al. 2022

IJERPH

View full text Add to dashboard Cite

The carbon market is recognized as the most effective means for reducing global carbon dioxide emissions. Effective carbon price forecasting can help the carbon market to solve environmental problems at a lower economic cost. However, the existing studies focus on the carbon premium explanation from the perspective of return and volatility spillover under the framework of the mean-variance low-order moment. Specifically, the time-varying, high-order moment shock of market asymmetry and extreme policies on carbon price have been ignored. The innovation of this paper is constructing a new hybrid model, NAGARCHSK-GRU, that is consistent with the special characteristics of the carbon market. In the proposed model, the NAGARCHSK model is designed to extract the time-varying, high-order moment parameter characteristics of carbon price, and the multilayer GRU model is used to train the obtained time-varying parameter and improve the forecasting accuracy. The results conclude that the NAGARCHSK-GRU model has better accuracy and robustness for forecasting carbon price. Moreover, the long-term forecasting performance has been proved. This conclusion proves the rationality of incorporating the time-varying impact of asymmetric information and extreme factors into the forecasting model, and contributes to a powerful reference for investors to formulate investment strategies and assist a reduction in carbon emissions.

show abstract

Asymmetric risk spillover between financial market uncertainty and the carbon market: A GAS–DCS–copula approach

Cited by 63 publications

References 39 publications

High-dimensional nonlinear dependence and risk spillovers analysis between China’s carbon market and its major influence factors

High-dimensional nonlinear dependence and risk spillovers analysis between China’s carbon market and its major influence factors

Forecasts of Value-at-Risk and Expected Shortfall in the Crude Oil Market: A Wavelet-Based Semiparametric Approach

Forecasting Carbon Dioxide Price Using a Time-Varying High-Order Moment Hybrid Model of NAGARCHSK and Gated Recurrent Unit Network

Contact Info

Product

Resources

About