Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors

Abstract

In this study, the hybrid of combination-mixed data sampling regression model and back propagation neural network (combination-MIDAS-BP) is proposed to perform real-time forecasting of weekly carbon prices in China's Shenzhen carbon market. In addition to daily energy, economy and weather conditions, environmental factor is introduced into predictive indicators. The empirical results show that the carbon price is more sensitive to coal, temperature and AQI (air quality index) than to other factors. It is also shown that the forecast accuracy of the proposed model is approximately 30% and 40% higher than that of combination-MIDAS models and benchmark models, respectively. Given these forecast results, China's government and enterprises can effectively manage nonlinear, nonstationary, and irregular carbon prices, providing a better investing and managing tool from behavioural economics.

A Hybrid Forecasting Model for Nonstationary and Nonlinear Time Series in the Stochastic Process of CO2 Emission Trading Price Fluctuation

“…e empirical results suggest that a hybrid model performs well in nonstationary and nonlinear time-series analyses, and especially in forecasting carbon prices. Unlike the previously mentioned studies which primarily predict the EU carbon market, some studies have predicted the emerging carbon market in China [3,13,20]. Despite the different topics investigated, these papers reach the same conclusion, in which hybrid models have a higher forecasting accuracy than benchmark models.…”

“…Arouri et al [8] capture the asymmetry and nonlinearity of carbon prices and confirm the necessity of establishing a nonlinear carbon price-prediction model. To achieve a better nonlinear approximation, empirical studies on carbon prices' forecasting have increasingly built a second strand of the literature using computational intelligence techniques [3,[11][12][13][14][15][16][17][18]. ese primarily refer to the artificial neural network, fuzzy logic, evolutionary algorithm, support vector machine (SVM), and hybrid models, which exploit the advantages of the intelligence algorithm and statistical models.…”

A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

“…With the development of neural networks and deep learning, the second is the neural network model prediction. The backpropagation neural network (BP) model [9], the least square support vector machine method (LSSVM) model [10], and the artificial neural network (MLP) model [11] are the other models.…”

Tripartite Evolutionary Game Theory Approach for Low-Carbon Power Grid Technology Cooperation With Government Intervention

“…Facing the challenges of global climate safety and domestic environmental pressure, China has established seven emission trading markets since 2011, including Beijing, Tianjin, Shanghai, Guangdong, Shenzhen, Hebei and Chongqing [1]. In the marked-based emission trading scheme (ETS), the financial intermediaries, such as banks and funds are indispensable to promote cost-effective emission mitigation [2].…”