Aatola, P. ; Ollikainen, M. ; Toppinen, A. Price determination in the EU ETS market: theory and econometric analysis with market fundamentals. 2013 Energy Econ.. 36 380-395
Abrell, J. ; Ndoye, F.A. ; Zachmann, G. Assessing the impact of the EU ETS using firm level data. Bruegel working paper. 2011 :
Alberola, E. ; Chevallier, J. ; Ch’eze, B. Price drivers and structural breaks in European carbon prices 2005–07. 2008 Energy Policy. 36 787-797
- Batten, J.A. ; Maddox, G.E. ; Young, M.R. Does weather, or energy prices, affect carbon prices?. 2020 Energy Econ.. 105016 -
Paper not yet in RePEc: Add citation now
- Bayer, P. ; Aklin, M. The European Union emissions trading system reduced CO2 emissions despite low prices. 2020 Proc. Natl. Acad. Sci.. 117 8804-8812
Paper not yet in RePEc: Add citation now
Benz, E. ; Trück, S. Modeling the price dynamics of CO2 emission allowances. 2009 Energy Econ.. 31 4-15
Bruninx, K. ; Ovaere, M. ; Delarue, E. The long-term impact of the market stability reserve on the EU emission trading system. 2020 Energy Econ.. 89 -
Byun, S.J. ; Cho, H. Forecasting carbon futures volatility using GARCH models with energy volatilities. 2013 Energy Econ.. 40 207-221
- Caginalp, G. ; Laurent, H. The predictive power of price patterns. 1998 Appl. Math. Finan.. 5 181-205
Paper not yet in RePEc: Add citation now
- Cagliero, L. ; Fior, J. ; Garza, P. Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognition. 2023 Expert Syst. Appl.. 216 -
Paper not yet in RePEc: Add citation now
- Chen, J. ; Wen, Y. ; Nanehkaran, Y.A. Machine learning techniques for stock price prediction and graphic signal recognition. 2023 Eng. Appl. Artif. Intell.. 121 -
Paper not yet in RePEc: Add citation now
Chen, J.H. ; Tsai, Y.C. Encoding candlesticks as images for pattern classification using convolutional neural networks. 2020 Finan. Innov.. 6 1-19
Chen, S. ; Bao, S. ; Zhou, Y. The predictive power of Japanese candlestick charting in Chinese stock market. 2016 Physica A: Stat. Mech. Appl.. 457 148-165
Chevallier, J. A model of carbon price interactions with macroeconomic and energy dynamics. 2011 Energy Econ.. 33 1295-1312
Chevallier, J. Carbon futures and macroeconomic risk factors: a view from the EU ETS. 2009 Energy Econ.. 31 614-625
Chevallier, J. Volatility forecasting of carbon prices using factor models. 2010 Econ. Bull.. 30 1642-1660
Chevallier, J. ; Sévi, B. On the realized volatility of the ECX CO2 emissions 2008 futures contract: distribution, dynamics and forecasting. 2011 Ann. Finance. 7 1-29
Convery, F.J. Origins and development of the EU ETS. 2009 Environ. Resour. Econ.. 43 391-412
- Cooper, M.J. ; Cliff, M.T. ; Gulen, H. Return differences between trading and non-trading hours: Like night and day. Available at SSRN 1004081. 2008 :
Paper not yet in RePEc: Add citation now
Creti, A. ; Joets, M. Multiple bubbles in the European union emission trading scheme. 2017 Energy Policy. 107 119-130
Creti, A. ; Jouvet, P.A. ; Mignon, V. Carbon price drivers: Phase I versus phase II equilibrium?. 2012 Energy Econ.. 34 327-334
Dai, X. ; Xiao, L. ; Wang, Q. Multiscale interplay of higher-order moments between the carbon and energy markets during Phase III of the EU ETS. 2021 Energy Policy. 156 -
De Perthuis, C. ; Trotignon, R. Governance of CO2 markets: lessons from the EU ETS. 2014 Energy Policy. 75 100-106
Dhamija, A.K. ; Yadav, S.S. ; Jain, P.K. Forecasting volatility of carbon under EU ETS: a multi-phase study. 2017 Environ. Econ. Policy Stud.. 19 299-335
- Dolan, R.J. Emotion, cognition, and behavior. 2002 Science. 298 1191-1194
Paper not yet in RePEc: Add citation now
Dunis, C.L. ; Laws, J. ; Rudy, J. Profitable mean reversion after large price drops: a story of day and night in the S&P 500, 400 MidCap and 600 SmallCap indices. 2011 J. Asset Manag.. 12 185-202
Ellerman, A.D. ; Marcantonini, C. ; Zaklan, A. The European Union emissions trading system: ten years and counting. 2016 Rev. Environ. Econ. Policy. 10 89-107
- Fan, X. ; Li, S. ; Tian, L. Chaotic characteristic identification for carbon price and an multi-layer perceptron network prediction model. 2015 Expert Syst. Appl.. 42 3945-3952
Paper not yet in RePEc: Add citation now
Flachsland, C. ; Pahle, M. ; Burtraw, D. How to avoid history repeating itself: the case for an EU emissions trading system (EU ETS) price floor revisited. 2020 Clim. Pol.. 20 133-142
- García-Martos, C. ; Rodríguez, J. ; Sánchez, M.J. Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities. 2013 Appl. Energy. 101 363-375
Paper not yet in RePEc: Add citation now
García, A. ; Jaramillo-Morán, M.A. Short-term European Union allowance price forecasting with artificial neural networks. 2020 Entrepr. Sustain. Issues. 8 261-
Gong, X. ; Li, M. ; Guan, K. Climate change attention and carbon futures return prediction. 2023 J. Futur. Mark.. 43 1261-1288
- Goo, Y. ; Chen, D. ; Chang, Y. The application of Japanese candlestick trading strategies in Taiwan. 2007 Invest. Manag. Finan. Innov.. 4 49-79
Paper not yet in RePEc: Add citation now
- Graves, A. ; Schmidhuber, J. Framewise phoneme classification with bidirectional LSTMand other neural network architectures. 2005 Neural Netw.. 18 602-610
Paper not yet in RePEc: Add citation now
Gu, L. ; Peng, Y. ; Vigne, S.A. ; Wang, Y. Hidden costs of non-green performance? The impact of air pollution awareness on loan rates for Chinese firms. 2023 J. Econ. Behav. Organ.. 213 233-250
- Guðbrandsdóttir, H.N. ; Haraldsson, H.Ó. Predicting the price of EU ETS carbon credits. 2011 Syst. Eng. Proc.. 1 481-489
Paper not yet in RePEc: Add citation now
Hammoudeh, S. ; Nguyen, D.K. ; Sousa, R.M. What explain the short-term dynamics of the prices of CO2 emissions?. 2014 Energy Econ.. 46 22-135
Hepburn, C. ; Grubb, M. ; Neuhoff, K. Auctioning of EU ETS phase II allowances: how and why?. 2006 Clim. Pol.. 6 137-160
Hickmann, T. Science–policy interaction in international environmental politics: an analysis of the ozone regime and the climate regime. 2014 Environ. Econ. Policy Stud.. 16 21-44
Hintermann, B. Allowance price drivers in the first phase of the EU ETS. 2010 J. Environ. Econ. Manag.. 59 43-56
Hintermann, B. ; Peterson, S. ; Rickels, W. Price and market behavior in phase II of the EU ETS: a review of the literature. 2016 Rev. Environ. Econ. Policy. 10 108-128
- Hoseinzade, E. ; Haratizadeh, S. CNNpred: CNN-based stock market prediction using a diverse set of variables. 2019 Expert Syst. Appl.. 129 273-285
Paper not yet in RePEc: Add citation now
- Hu, C. ; He, L.T. An application of interval methods to stock market forecasting. 2007 Reliable Computing. 13 423-434
Paper not yet in RePEc: Add citation now
Hu, J. ; Crijns-Graus, W. ; Lam, L. Ex-ante evaluation of EU ETS during 2013–2030: EU-internal abatement. 2015 Energy Policy. 77 152-163
Huang, T. ; Saporta, G. ; Wang, H. A robust spatial autoregressive scalar on function regression with t distribution. 2021 ADAC. 15 57-81
Huang, W. ; Gao, T. ; Hao, Y. Transformer-based forecasting for intraday trading in the Shanghai crude oil market: analyzing open-high-low-close prices. 2023 Energy Econ.. 127 -
Huang, W. ; Wang, H. ; Qin, H. Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method. 2022 Energy Econ.. 110 -
- Huang, W. ; Wang, H. ; Wang, S. A pseudo principal component analysis method for multi-dimensional open-high-low-close data in candlestick chart. 2022 Commun. Stat. Theory Methods. 1-27
Paper not yet in RePEc: Add citation now
- Huang, W. ; Wang, H. ; Wang, S. A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart. 2024 Financ. Innov.. 10 97-
Paper not yet in RePEc: Add citation now
- Huang, W. ; Wang, H. ; Wei, Y. Complex network analysis of global stock market co-movement during the COVID-19 pandemic based on intraday open-high-low-close data. 2024 Financ. Innov.. 10 7-
Paper not yet in RePEc: Add citation now
Huang, W. ; Wang, H. ; Wei, Y. Identifying the determinants of European carbon allowances prices: a novel robust partial least squares method for open-high-low-close data. 2023 Int. Rev. Financ. Anal.. 90 -
Huang, Y. ; Dai, X. ; Wang, Q. A hybrid model for carbon price forecasting using GARCH and long short-term memory network. 2021 Appl. Energy. 285 -
Hung, C.C. ; Chen, Y.J. DPP: deep predictor for price movement from candlestick charts. 2021 PLoS One. 16 -
- Hung, C.C. ; Chen, Y.J. ; Guo, S.J. Predicting the price movement from candlestick charts: a CNN-based approach. 2020 Int. J. Ad Hoc Ubiquitous Comput.. 34 111-120
Paper not yet in RePEc: Add citation now
- Jeszke, R. ; Lizak, S. Reflections on the mechanisms to protect against formation of price bubble in the EU ETS market. 2021 Environ. Protect. Nat. Resourc.. 32 8-17
Paper not yet in RePEc: Add citation now
- Ji, L. ; Zou, Y. ; He, K. Carbon futures price forecasting based with ARIMA-CNN-LSTM model. 2019 Proc. Comput. Sci.. 162 33-38
Paper not yet in RePEc: Add citation now
- Jiang, J. ; Kelly, B.T. ; Xiu, D. (Re-) Imag (in) ing price trends. 2023 The Journal of Finance. 78 3193-3249
Paper not yet in RePEc: Add citation now
Kelly, M.A. ; Clark, S.P. Returns in trading versus non-trading hours: the difference is day and night. 2011 J. Asset Manag.. 12 132-145
Kim, J. ; Park, Y.J. ; Ryu, D. Stochastic volatility of the futures prices of emission allowances: a Bayesian approach. 2017 Physica A: Stat. Mech. Appl.. 465 714-724
Kim, T. ; Kim, H.Y. Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. 2019 PLoS One. 14 -
Koch, N. ; Fuss, S. ; Grosjean, G. ; Edenhofer, O. Causes of the EU ETS price drop: recession, CDM, renewable policies or a bit of everything?—new evidence. 2014 Energy Policy. 73 676-685
Kossoy, A. ; Guigon, P. State and trends of the carbon market. 2012 :
Laing, T. ; Sato, M. ; Grubb, M. Assessing the Effectiveness of the EU Emissions Trading System. 2013 Grantham Research Institute on Climate Change and the Environment: London
- Lepone, A. ; Rahman, R.T. ; Yang, J.Y. The Impact of European Union Emissions Trading Scheme (EU ETS) National Allocation Plans (NAP) on carbon markets. 2011 Low Carbon Econ.. 2 71-
Paper not yet in RePEc: Add citation now
Li, C.Y. ; Chen, S.N. ; Lin, S.K. Pricing derivatives with modeling CO2 emission allowance using a regime-switching jump diffusion model: with regime-switching risk premium. 2016 Eur. J. Financ.. 22 887-908
Li, D. ; Li, Y. ; Wang, C. Forecasting carbon prices based on real-time decomposition and causal temporal convolutional networks. 2023 Appl. Energy. 331 -
- Li, H. ; Jin, F. ; Sun, S. A new secondary decomposition ensemble learning approach for carbon price forecasting. 2021 Knowl.-Based Syst.. 214 -
Paper not yet in RePEc: Add citation now
- Liang, M. ; Wu, S. ; Wang, X. A stock time series forecasting approach incorporating candlestick patterns and sequence similarity. 2022 Expert Syst. Appl.. 205 -
Paper not yet in RePEc: Add citation now
- Liu, H. ; Shen, L. Forecasting carbon price using empirical wavelet transform and gated recurrent unit neural network. 2020 Carbon Manag.. 11 25-37
Paper not yet in RePEc: Add citation now
Liu, X. ; An, H. ; Wang, L. An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms. 2017 Appl. Energy. 185 1778-1787
- Lu, W. ; Li, J. ; Wang, J. A CNN-BiLSTM-AM method for stock price prediction. 2021 Neural Comput. & Applic.. 33 4741-4753
Paper not yet in RePEc: Add citation now
- Lu, W. ; Wang, W.J. An investigation into the evolved relationship between spot and futures in the European Union emission trading scheme. 2011 Int. J. Green Econ.. 5 133-142
Paper not yet in RePEc: Add citation now
Lucia, J.J. ; Mansanet-Bataller, M. ; Pardo, Á. Speculative and hedging activities in the European carbon market. 2015 Energy Policy. 82 342-351
Lutz, B.J. ; Pigorsch, U. ; Rotfuß, W. Nonlinearity in cap-and-trade systems: the EUA price and its fundamentals. 2013 Energy Econ.. 40 222-232
- Lv, T. ; Hao, Y. Further analysis of candlestick Patterns’ predictive power. 2017 En : Data Science: Third International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2017, Changsha, China, September 22–24, 2017, Proceedings, Part I. Springer Singapore:
Paper not yet in RePEc: Add citation now
- Ma, F. ; Cao, J. ; Wang, Y. Dissecting climate change risk and financial market instability: Implications for ecological risk management. 2023 Risk Analysis, 1-27. -
Paper not yet in RePEc: Add citation now
- Meadows, D. ; Vis, P. ; Zapfel, P. The EU emissions trading system. 2019 En : Towards a Climate-Neutral Europe. :
Paper not yet in RePEc: Add citation now
- Mirzaee Ghazani, M. ; Jafari, M.A. The efficiency of CO2 market in the phase III EU ETS: analyzing in the context of a dynamic approach. 2021 Environ. Sci. Pollut. Res.. 28 61080-61095
Paper not yet in RePEc: Add citation now
- Nadirgil, O. Carbon price prediction using multiple hybrid machine learning models optimized by genetic algorithm. 2023 J. Environ. Manag.. 342 -
Paper not yet in RePEc: Add citation now
- Needham, T. A visual explanation of Jensen’s inequality. 1993 Am. Math. Mon.. 100 768-771
Paper not yet in RePEc: Add citation now
Neuhoff, K. ; Åhman, M. ; Betz, R. Implications of announced phase II national allocation plans for the EU ETS. 2006 Clim. Pol.. 6 411-422
- Nison, S. Beyond Candlesticks: New Japanese Charting Techniques Revealed. 1994 John Wiley & Sons:
Paper not yet in RePEc: Add citation now
- Nison, S. Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East. 2001 Penguin:
Paper not yet in RePEc: Add citation now
- Pan, D. ; Zhang, C. ; Zhu, D. Carbon price forecasting based on news text mining considering investor attention. 2023 Environ. Sci. Pollut. Res.. 30 28704-28717
Paper not yet in RePEc: Add citation now
Paolella, M.S. ; Taschini, L. An econometric analysis of emission allowance prices. 2008 J. Bank. Financ.. 32 2022-2032
- Pawłowski, P. Carbon Emissions Futures Price Forecasting with Random Forest. 2021 Rynek Energii:
Paper not yet in RePEc: Add citation now
Perino, G. New EU ETS phase 4 rules temporarily puncture waterbed. 2018 Nat. Clim. Chang.. 8 262-264
- Qin, Q. ; Huang, Z. ; Zhou, Z. Hodrick–Prescott filter-based hybrid ARIMA–SLFNs model with residual decomposition scheme for carbon price forecasting. 2022 Appl. Soft Comput.. 119 -
Paper not yet in RePEc: Add citation now
Rannou, Y. ; Boutabba, M.A. ; Barneto, P. Are Green Bond and Carbon Markets in Europe complements or substitutes? Insights from the activity of power firms. 2021 Energy Econ.. 104 -
Reboredo, J.C. Modeling EU allowances and oil market interdependence. Implications for portfolio management. 2013 Energy Econ.. 36 471-480
- Rostamian, A. ; O’Hara, J.G. Event prediction within directional change framework using a CNN-LSTM model. 2022 Neural Comput. & Applic.. 34 17193-17205
Paper not yet in RePEc: Add citation now
- Santur, Y. Candlestick chart based trading system using ensemble learning for financial assets. 2022 Sigma J. Eng. Nat. Sci.. 40 370-379
Paper not yet in RePEc: Add citation now
Sartor, O. ; Pallière, C. ; Lecourt, S. Benchmark-based allocations in EU ETS Phase 3: an early assessment. 2014 Clim. Pol.. 14 507-524
Sato, M. ; Rafaty, R. ; Calel, R. Allocation, allocation, allocation! The political economy of the development of the European Union emissions trading system. 2022 Wiley Interdiscip. Rev. Clim. Chang.. 13 -
Selvamuthu, D. ; Kumar, V. ; Mishra, A. Indian stock market prediction using artificial neural networks on tick data. 2019 Finan. Innov.. 5 1-12
Sheng, C. ; Wang, G. ; Geng, Y. The correlation analysis of futures pricing mechanism in China’s carbon financial market. 2020 Sustainability. 12 7317-
- Sherstinsky, A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. 2020 Physica D: Nonlinear Phenomena. 404 -
Paper not yet in RePEc: Add citation now
- Shin, H.G. ; Ra, I. ; Choi, Y.H. A deep multimodal reinforcement learning system combined with CNN and LSTM for stock trading. 2019 En : International Conference on Information and Communication Technology Convergence (ICTC). IEEE:
Paper not yet in RePEc: Add citation now
Skjærseth, J.B. ; Wettestad, J. The origin, evolution and consequences of the EU emissions trading system. 2009 Glob. Environ. Polit.. 9 101-122
Sun, G.Q. ; Che, T. ; Wei, Z.N. A carbon price forecasting model based on variational mode decomposition and spiking neural networks. 2016 Energies. 9 54-
Tan, X.P. ; Wang, X.Y. Dependence changes between the carbon price and its fundamentals: a quantile regression approach. 2017 Appl. Energy. 190 306-325
- Tsai, C.F. ; Quan, Z.Y. Stock prediction by searching for similarities in candlestick charts. 2014 ACM Trans. Manag. Inf. Syst.. 5 1-21
Paper not yet in RePEc: Add citation now
- Tsai, M.T. ; Kuo, Y.T. Application of radial basis function neural network for carbon price forecasting. 2014 Appl. Mech. Mater.. 590 683-687
Paper not yet in RePEc: Add citation now
- Varadharajan, P. ; Vikkraman, P. Effectiveness of technical analysis using candlestick chart for selection of equity stock in Indian capital market. 2011 J. Contemp. Manag. Res.. 5 -
Paper not yet in RePEc: Add citation now
Viteva, S. ; Veld-Merkoulova, Y.V. ; Campbell, K. The forecasting accuracy of implied volatility from ECX carbon options. 2014 Energy Econ.. 45 475-484
- Wang, H. ; Huang, T. ; Wang, S. A flexible spatial autoregressive modelling framework for mixed covariates of multiple data types. 2021 Commun. Stat. Simul. Comput.. 50 3498-3515
Paper not yet in RePEc: Add citation now
- Wang, H. ; Wang, J. ; Cao, L. A stock closing price prediction model based on CNN-BiSLSTM. 2021 Complexity. 1-12
Paper not yet in RePEc: Add citation now
Wang, M. ; Zhu, M. ; Tian, L. A novel framework for carbon price forecasting with uncertainties. 2022 Energy Econ.. 106162 -
Wang, Y. Volatility spillovers across NFTs news attention and financial markets. 2022 Int. Rev. Financ. Anal.. 83 102313-
- Wang, Y. ; Lucey, B.M. ; Vigne, S.A. The effects of central bank digital currencies news on financial markets. 2022 Technol. Forecast. Soc. Change.. 180 121715-
Paper not yet in RePEc: Add citation now
Wang, Y. ; Wei, Y. ; Lucey, B.M. Return spillover analysis across central bank digital currency attention and cryptocurrency markets. 2023 Res. Int. Bus. Finance. 64 101896-
Wei, Y. ; Gong, P. ; Zhang, J. Exploring public opinions on climate change policy in “Big Data Era”—A case study of the European Union Emission Trading System (EU-ETS) based on Twitter. 2021 Energy Policy. 158 -
Wei, Y. ; Li, Y. ; Wang, Z. Multiple price bubbles in global major emission trading schemes: Evidence from European Union, New Zealand, South Korea and China. 2022 Energy Econ.. 113 -
Wei, Y. ; Zhang, J. ; Bai, L. Connectedness among El Niño-Southern Oscillation, carbon emission allowance, crude oil and renewable energy stock markets: Time-and frequency-domain evidence based on TVP-VAR model. 2023 Renew. Energy. 202 289-309
- Wu, J.M.T. ; Li, Z. ; Herencsar, N. A graph-based CNN-LSTM stock price prediction algorithm with leading indicators. 2021 Multimedia Systems. 1-20
Paper not yet in RePEc: Add citation now
- Wu, M. ; Li, K.X. ; Xiao, Y. Carbon emission trading scheme in the shipping sector: drivers, challenges, and impacts. 2022 Mar. Policy. 138 -
Paper not yet in RePEc: Add citation now
Xu, H. ; Wang, M. ; Jiang, S. Carbon price forecasting with complex network and extreme learning machine. 2020 Physica A: Stat. Mech. Appl.. 545 -
- Yu, Y. ; Si, X. ; Hu, C. A review of recurrent neural networks: LSTM cells and network architectures. 2019 Neural Comput.. 31 1235-1270
Paper not yet in RePEc: Add citation now
- Yun, P. ; Huang, X. ; Wu, Y. Forecasting carbon dioxide emission price using a novel mode decomposition machine learning hybrid model of CEEMDAN-LSTM. 2023 Energy Sci. Eng.. 11 79-96
Paper not yet in RePEc: Add citation now
Yun, P. ; Zhang, C. ; Wu, Y. Forecasting carbon dioxide price using a time-varying high-order moment hybrid model of NAGARCHSK and gated recurrent unit network. 2022 Int. J. Environ. Res. Public Health. 19 899-
Zhang, D. ; Tang, P. Forecasting European Union allowances futures: the role of technical indicators. 2023 Energy. 270 -
Zhang, D. ; Wang, C. ; Wang, Y. Unveiling the critical nexus: Volatility of crude oil future prices and trade partner’s cash holding behavior in the face of the Russia–Ukraine conflict. 2024 Energy Econ.. 107413 -
- Zhang, F. ; Wen, N. Carbon price forecasting: a novel deep learning approach. 2022 Environ. Sci. Pollut. Res.. 29 54782-54795
Paper not yet in RePEc: Add citation now
Zhang, F. ; Xia, Y. Carbon price prediction models based on online news information analytics. 2022 Financ. Res. Lett.. 46 -
Zhang, K. ; Cao, H. ; Thé, J. A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms. 2022 Appl. Energy. 306 -
- Zhang, K. ; Yang, X. ; Wang, T. Multi-step carbon price forecasting using a hybrid model based on multivariate decomposition strategy and deep learning algorithms. 2023 J. Clean. Prod.. 136959 -
Paper not yet in RePEc: Add citation now
- Zhang, T. ; Tang, Z. Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach. 2023 Front. Energy Res.. 10 -
Paper not yet in RePEc: Add citation now
Zhang, W. ; Wu, Z. ; Zeng, X. An ensemble dynamic self-learning model for multiscale carbon price forecasting. 2023 Energy. 263 -
- Zhang, X. ; Li, Z. ; Zhao, Y. Carbon trading and COVID-19: a hybrid machine learning approach for international carbon price forecasting. 2023 Ann. Oper. Res.. 1-29
Paper not yet in RePEc: Add citation now
- Zhao, Q. ; Wang, H. ; Lu, S. MLDQ feature embedding and regression modeling for distribution valued data. 2022 Inf. Sci.. 609 121-152
Paper not yet in RePEc: Add citation now
Zheng, Z. ; Xiao, R. ; Shi, H. Statistical regularities of carbon emission trading market: evidence from European Union allowances. 2015 Physica A: Stat. Mech. Appl.. 426 9-15
Zhu, B. ; Wei, Y. Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology. 2013 Omega. 41 517-524
Zhu, B. ; Ye, S. ; Wang, P. Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels. 2022 J. Forecast.. 41 100-117
Zhu, B.Z. ; Han, D. ; Wang, P. Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression. 2017 Appl. Energy. 191 521e530-