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

Author(s):  
Po Yun ◽  
Chen Zhang ◽  
Yaqi Wu ◽  
Yu Yang

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.

1993 ◽  
Author(s):  
K. T. Tsang ◽  
C. Kostas ◽  
A. Mondelli

2018 ◽  
Vol 78 (4) ◽  
pp. 2003-2027 ◽  
Author(s):  
Mohamed Essadki ◽  
Stephane de Chaisemartin ◽  
Frédérique Laurent ◽  
Marc Massot

2020 ◽  
pp. 1-10
Author(s):  
Li Wang

This paper discusses the modeling of financial volatility under the condition of non-normal distribution. In order to solve the problem that the traditional central moment cannot estimate the thick-tailed distribution, the L-moment which is widely used in the hydrological field is introduced, and the autoregressive conditional moment model is used for static and dynamic fitting based on the generalized Pareto distribution. In order to solve the dimension disaster of multidimensional conditional skewness and kurtosis modeling, the multidimensional skewness and kurtosis model based on distribution is established, and the high-order moment model is deduced. Finally, the problems existing in the traditional investment portfolio are discussed, and on this basis, the high-order moment portfolio is further studied. The results show that the key lies in the selection of the model and the assumption of asset probability distribution. Financial risk analysis can be effective only with a large sample. High-frequency data contain more information and can provide rich data resources. The conditional generalized extreme value distribution can well describe the time-varying characteristics of scale parameters and shape parameters and capture the conditional heteroscedasticity in the high-frequency extreme value time series. Better describe the persistence and aggregation of the extreme value of high frequency data as well as the peak and thick tail characteristics of its distribution.


2018 ◽  
Vol 108 ◽  
pp. 463-467 ◽  
Author(s):  
William A. Pizer ◽  
Xiliang Zhang

On December 19, 2017, China announced the official start of its national emissions trading system (ETS) construction program. When fully implemented, this program will more than double the volume of worldwide carbon dioxide emissions covered by either tax or tradable permit policy. Many of program's design features reflect those of China's pilot programs but differ from those of most emissions trading programs in the United States and Europe. This paper explains the context and design of China's new carbon market, discusses implications and possible modifications, and suggests topics for further research.


Mechatronics ◽  
2020 ◽  
Vol 65 ◽  
pp. 102320
Author(s):  
Wannes De Groote ◽  
Tom Lefebvre ◽  
Georges Tod ◽  
Nele De Geeter ◽  
Bruno Depraetere ◽  
...  

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