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The joint behavior of internal and external system brings out a high complexity of the carbon and oil price interactions, such as non-linearity and multi-frequency. This paper innovatively proposed a time-frequency mechanism between carbon and oil markets from the two aspects of internal system and external factors, and introduced a novelty partial wavelet analytics to explore their dynamic multi-scale interactions. We selected the European carbon and Brent oil futures prices data from March 2009 to December 2020, with the consideration of several necessary control variables from the external surroundings. Our findings point to a stable and strong in-phase relationship between the two markets, with oil leading at medium and lower frequencies. However, the mutual leading relationships are especially sensitive during abnormal political events and periods of financial recession and global emergency, which are observed at different periods for intermediate horizons. What is more, the interactions are more diversified and feebler at short-timescale. Under the vision of carbon neutrality, these evidences provide invaluable guidance for regulators to structure a more flexible adjusting mechanism for the risk control of carbon markets, and also help investors to hedge risk aimed at different time horizons.
Yaqi Wu; Chen Zhang; Po Yun; Dandan Zhu; Wei Cao; Zulfiqar Ali Wagan. Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets. Energy & Environment 2021, 1 .
AMA StyleYaqi Wu, Chen Zhang, Po Yun, Dandan Zhu, Wei Cao, Zulfiqar Ali Wagan. Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets. Energy & Environment. 2021; ():1.
Chicago/Turabian StyleYaqi Wu; Chen Zhang; Po Yun; Dandan Zhu; Wei Cao; Zulfiqar Ali Wagan. 2021. "Time–frequency analysis of the interaction mechanism between European carbon and crude oil markets." Energy & Environment , no. : 1.
To address climate change, the carbon emission trading scheme has become one of the main measures to achieve emission reduction goals. One of the core problems in constructing the carbon emissions trading market is determining carbon emissions trading prices. The scientific nature of carbon emissions pricing determines the effectiveness of market regulation. Research on the influencing factors and heterogeneous tail distribution of carbon prices can increase the accuracy of carbon pricing, which is particularly important for the development of the carbon emissions trading market. The current studies have some limitations and lack heterogeneous tail description. We employ the arbitrage pricing theory-standardized standard asymmetric exponential power distribution model to analyze China’s regional carbon emissions trading price and use a genetic algorithm to solve linear programming. The results confirm the theoretical results and efficiency of the proposed algorithm. First, the new model can capture the skewness, fat-tailed distribution, and asymmetric effects of China’s regional carbon emissions trading price. Second, the macroeconomy, similar products, energy price, and exchange rate influence the carbon price fluctuation; investors’ behavior plays an important role in the heterogeneous tail distribution of carbon price. The findings provide references for the government to take appropriate measures to promote carbon emission reduction and improve the effectiveness of China’s carbon market. Therefore, our findings can help enhance emission reduction and achieve sustainable development of a low-carbon environment.
Xianzi Yang; Chen Zhang; Yu Yang; Yaqi Wu; Po Yun; Zulfiqar Ali Wagan. China’s Carbon Pricing Based on Heterogeneous Tail Distribution. Sustainability 2020, 12, 2754 .
AMA StyleXianzi Yang, Chen Zhang, Yu Yang, Yaqi Wu, Po Yun, Zulfiqar Ali Wagan. China’s Carbon Pricing Based on Heterogeneous Tail Distribution. Sustainability. 2020; 12 (7):2754.
Chicago/Turabian StyleXianzi Yang; Chen Zhang; Yu Yang; Yaqi Wu; Po Yun; Zulfiqar Ali Wagan. 2020. "China’s Carbon Pricing Based on Heterogeneous Tail Distribution." Sustainability 12, no. 7: 2754.
The certified emission reduction (CER) carbon trading market promoted by the clean development mechanism (CDM) has become an important platform for the development of the international carbon market. However, the CER carbon market has shown unsteady development with the present phenomena of price decrease, transaction inactivity, and recession. Against this backdrop, this study aims to explore the intuition behind CER price volatility from the new perspective of internal and external market dynamic linkages. By introducing three homogeneous carbon products of CER futures, namely, the daily dataset of CER spot, EUA (European Union Allowance) spot and EUA futures, and taking five heterogeneous market drivers comprising stock, exchange rates, coal, crude oil, and natural gas into account, we analyze the dynamic correlations and volatility spillovers between CER futures returns and these influencing factors using the DGC-MSV model. With sample data from January 2013 to May 2019, our empirical results show a persistent dynamic dependence between CER futures price and its factors. The homogeneous and heterogeneous markets have significant positive and negative spillover effects, respectively, on the CER futures market. The decline of CER futures price in the post-Kyoto era is due to two aspects: fluctuation of the exchange rate market, which is closely connected to the settlement of currency, and coal price volatility in energy markets. However, the CER futures market has no obvious spillover effect on other markets, except for its strong impact on the CER spot market and weak information spillover to the exchange rate market. Overall, this finding indicates the feeblest financial property of CER carbon futures market.
Yaqi Wu; Chen Zhang; Yu Yang; Xianzi Yang; Po Yun; Wei Cao. What Happened to the CER Market? A Dynamic Linkage Effect Analysis. IEEE Access 2020, 8, 62322 -62333.
AMA StyleYaqi Wu, Chen Zhang, Yu Yang, Xianzi Yang, Po Yun, Wei Cao. What Happened to the CER Market? A Dynamic Linkage Effect Analysis. IEEE Access. 2020; 8 (99):62322-62333.
Chicago/Turabian StyleYaqi Wu; Chen Zhang; Yu Yang; Xianzi Yang; Po Yun; Wei Cao. 2020. "What Happened to the CER Market? A Dynamic Linkage Effect Analysis." IEEE Access 8, no. 99: 62322-62333.
Predicting the carbon price accurately can not only promote the sustainability of the carbon market and the price driving mechanism of carbon emissions, but can also help investors avoid market risks and increase returns. However, previous research has only focused on the low-order moment perspective of the returns for predicting the carbon price, while ignoring the shock of extreme events and market asymmetry originating from its pricing factor markets. In this paper, a novel extended higher-order moment multi-factor framework (EHM-APT) was formed to improve the prediction and to capture the driving mechanism of the carbon price. Furthermore, a multi-layer and multi-variable Long Short-Term Memory Network (Multi-LSTM) model was constructed so that the parameters and structure could be determined experimentally for testing the performance of the proposed framework. The results show that the pricing framework considers the shock of extreme events and market asymmetry and can improve the prediction compared with a framework that does not consider the shock of higher-order moment terms. Additionally, the Multi-LSTM model is more competitive for prediction than other benchmark models. This conclusion proves the rationality and accuracy of the proposed framework. The application of the pricing framework encourages investors and financial institutions to pay more attention to the pricing factor of extreme events and market asymmetry for accurate price prediction and investment analysis.
Po Yun; Chen Zhang; Yaqi Wu; Xianzi Yang; Zulfiqar Ali Wagan. A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network. Sustainability 2020, 12, 1869 .
AMA StylePo Yun, Chen Zhang, Yaqi Wu, Xianzi Yang, Zulfiqar Ali Wagan. A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network. Sustainability. 2020; 12 (5):1869.
Chicago/Turabian StylePo Yun; Chen Zhang; Yaqi Wu; Xianzi Yang; Zulfiqar Ali Wagan. 2020. "A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network." Sustainability 12, no. 5: 1869.