The Experts below are selected from a list of 32775 Experts worldwide ranked by ideXlab platform
Bangzhu Zhu - One of the best experts on this subject based on the ideXlab platform.
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A multiscale analysis for Carbon Price drivers
Energy Economics, 2019Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Yi-ming Wei, Rui XieAbstract:This study proposes a multiscale analysis model to explore and identify the Carbon Price drivers at different timescales. By introducing the latest multivariate empirical mode decomposition, Carbon Price and its potential drivers are decomposed into several groups of simple modes with specific economic meanings. The cointegration techniques, error correction model and Newey–West estimator are combined to capture the Carbon Price drivers at similar timescales. Illustrated by the samples of the European Union Emissions Trading System from 2009 to 2016, a few interesting results can be found that at the original data level, among the three most important drivers of Carbon Price, electricity Price and stock index show positive impacts, while coal Price shows a negative impact. At different timescales, the effects of electricity and stock index appear comparatively earlier, which drive Carbon Price from the short timescales and continue to strengthen. However, the impacts of coal, oil and gas Prices are lagging behind, which respectively drive the Carbon Price at the medium and long timescales.
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a novel multiscale nonlinear ensemble leaning paradigm for Carbon Price forecasting
Energy Economics, 2018Co-Authors: Bangzhu Zhu, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for Carbon Price forecasting. The EMD algorithm is used to decompose the Carbon Price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original Carbon Price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular Carbon Price.
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forecasting Carbon Price using empirical mode decomposition and evolutionary least squares support vector regression
Applied Energy, 2017Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary Carbon Prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for Carbon Price forecasting. Firstly, each Carbon Price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original Carbon Price. Finally, using four different-matured Carbon futures Prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances.
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An Adaptive Multiscale Ensemble Learning Paradigm for Carbon Price Forecasting
Pricing and Forecasting Carbon Markets, 2017Co-Authors: Bangzhu Zhu, Julien ChevallierAbstract:This final chapter is devoted to an adaptive model of Carbon Price forecasting that makes use of artificial neural networks. Considering either ensemble empirical mode decomposition, the least squares support vector machine, or the particle swarm optimization variant, the competing models are given an extra dimension by incorporating a learning paradigm.
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modelling the dynamics of european Carbon futures Price a zipf analysis
Economic Modelling, 2014Co-Authors: Bangzhu Zhu, Julien Chevallier, Yi-ming WeiAbstract:This article investigates the European Carbon futures Price dynamics by applying the Zipf analysis. The results show that: first, Carbon Price behaviour is asymmetric, and the long-term bearish probability is greater than the long-term bullish probability. Second, time-scales of investment and speculators' expectations of returns have dual effects on Carbon Price behaviour. The longer the time-scales of investment, the higher the bearish probability. The lower the expectations of returns, the smaller the distortion of Carbon Price behaviour. Third, the differences in Carbon market cognitions from non-greedy speculators with different expectations of returns mainly lie in the amplitudes and occasions of Carbon Price fluctuations, rather than in the Carbon Price fluctuations themselves. Fourth, speculators' expectations of returns have critical points. Once the critical points are reached, they will no longer be able to distort Carbon Price behaviour. Finally, we discuss some investment advice for supports of the decision-makers. For non-greedy-type speculators, they will choose to hold negatively in the short term and buy and hold in the long term, while for greedy-type speculators they will sell their European Union Allowances (EUAs) in the short term, and buy and hold in the long term. The results are helpful to hedge against unwanted Carbon Price movements, and to understand the transactions between different types of agents.
Yi-ming Wei - One of the best experts on this subject based on the ideXlab platform.
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A multiscale analysis for Carbon Price drivers
Energy Economics, 2019Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Yi-ming Wei, Rui XieAbstract:This study proposes a multiscale analysis model to explore and identify the Carbon Price drivers at different timescales. By introducing the latest multivariate empirical mode decomposition, Carbon Price and its potential drivers are decomposed into several groups of simple modes with specific economic meanings. The cointegration techniques, error correction model and Newey–West estimator are combined to capture the Carbon Price drivers at similar timescales. Illustrated by the samples of the European Union Emissions Trading System from 2009 to 2016, a few interesting results can be found that at the original data level, among the three most important drivers of Carbon Price, electricity Price and stock index show positive impacts, while coal Price shows a negative impact. At different timescales, the effects of electricity and stock index appear comparatively earlier, which drive Carbon Price from the short timescales and continue to strengthen. However, the impacts of coal, oil and gas Prices are lagging behind, which respectively drive the Carbon Price at the medium and long timescales.
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a novel multiscale nonlinear ensemble leaning paradigm for Carbon Price forecasting
Energy Economics, 2018Co-Authors: Bangzhu Zhu, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for Carbon Price forecasting. The EMD algorithm is used to decompose the Carbon Price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original Carbon Price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular Carbon Price.
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forecasting Carbon Price using empirical mode decomposition and evolutionary least squares support vector regression
Applied Energy, 2017Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary Carbon Prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for Carbon Price forecasting. Firstly, each Carbon Price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original Carbon Price. Finally, using four different-matured Carbon futures Prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances.
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modelling the dynamics of european Carbon futures Price a zipf analysis
Economic Modelling, 2014Co-Authors: Bangzhu Zhu, Julien Chevallier, Yi-ming WeiAbstract:This article investigates the European Carbon futures Price dynamics by applying the Zipf analysis. The results show that: first, Carbon Price behaviour is asymmetric, and the long-term bearish probability is greater than the long-term bullish probability. Second, time-scales of investment and speculators' expectations of returns have dual effects on Carbon Price behaviour. The longer the time-scales of investment, the higher the bearish probability. The lower the expectations of returns, the smaller the distortion of Carbon Price behaviour. Third, the differences in Carbon market cognitions from non-greedy speculators with different expectations of returns mainly lie in the amplitudes and occasions of Carbon Price fluctuations, rather than in the Carbon Price fluctuations themselves. Fourth, speculators' expectations of returns have critical points. Once the critical points are reached, they will no longer be able to distort Carbon Price behaviour. Finally, we discuss some investment advice for supports of the decision-makers. For non-greedy-type speculators, they will choose to hold negatively in the short term and buy and hold in the long term, while for greedy-type speculators they will sell their European Union Allowances (EUAs) in the short term, and buy and hold in the long term. The results are helpful to hedge against unwanted Carbon Price movements, and to understand the transactions between different types of agents.
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Carbon Price Analysis Using Empirical Mode Decomposition
Computational Economics, 2013Co-Authors: Bangzhu Zhu, Julien Chevallier, Ping Wang, Yi-ming WeiAbstract:Mastering the underlying characteristics of Carbon Price changes can help governments formulate correct policies to keep efficient operation of Carbon markets, and investors take effective measures to evade their investment risks. Empirical mode decomposition (EMD), a self-adaption data analysis approach for nonlinear and non-stationary time series, can accurately explain the formation mechanism of Carbon Price by decomposing it into several intrinsic mode functions (IMFs) and one residue from different scales. In this study, we apply EMD to the European Union Emissions Trading Scheme Carbon Price analysis. First, the Carbon Price is decomposed into eight IMFs and one residue. Moreover, these IMFs and residue are reconstructed into a high frequency component, a low frequency component and a trend component using hierarchical clustering method. The economic meanings of these three components are identified as short term market fluctuations, effects of significant trend breaks, and a long-term trend, respectively. Finally, some strategies are proposed for Carbon Price forecasting.
Ping Wang - One of the best experts on this subject based on the ideXlab platform.
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A multiscale analysis for Carbon Price drivers
Energy Economics, 2019Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Yi-ming Wei, Rui XieAbstract:This study proposes a multiscale analysis model to explore and identify the Carbon Price drivers at different timescales. By introducing the latest multivariate empirical mode decomposition, Carbon Price and its potential drivers are decomposed into several groups of simple modes with specific economic meanings. The cointegration techniques, error correction model and Newey–West estimator are combined to capture the Carbon Price drivers at similar timescales. Illustrated by the samples of the European Union Emissions Trading System from 2009 to 2016, a few interesting results can be found that at the original data level, among the three most important drivers of Carbon Price, electricity Price and stock index show positive impacts, while coal Price shows a negative impact. At different timescales, the effects of electricity and stock index appear comparatively earlier, which drive Carbon Price from the short timescales and continue to strengthen. However, the impacts of coal, oil and gas Prices are lagging behind, which respectively drive the Carbon Price at the medium and long timescales.
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a novel multiscale nonlinear ensemble leaning paradigm for Carbon Price forecasting
Energy Economics, 2018Co-Authors: Bangzhu Zhu, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for Carbon Price forecasting. The EMD algorithm is used to decompose the Carbon Price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original Carbon Price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular Carbon Price.
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forecasting Carbon Price using empirical mode decomposition and evolutionary least squares support vector regression
Applied Energy, 2017Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary Carbon Prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for Carbon Price forecasting. Firstly, each Carbon Price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original Carbon Price. Finally, using four different-matured Carbon futures Prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances.
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Carbon Price Analysis Using Empirical Mode Decomposition
Computational Economics, 2013Co-Authors: Bangzhu Zhu, Julien Chevallier, Ping Wang, Yi-ming WeiAbstract:Mastering the underlying characteristics of Carbon Price changes can help governments formulate correct policies to keep efficient operation of Carbon markets, and investors take effective measures to evade their investment risks. Empirical mode decomposition (EMD), a self-adaption data analysis approach for nonlinear and non-stationary time series, can accurately explain the formation mechanism of Carbon Price by decomposing it into several intrinsic mode functions (IMFs) and one residue from different scales. In this study, we apply EMD to the European Union Emissions Trading Scheme Carbon Price analysis. First, the Carbon Price is decomposed into eight IMFs and one residue. Moreover, these IMFs and residue are reconstructed into a high frequency component, a low frequency component and a trend component using hierarchical clustering method. The economic meanings of these three components are identified as short term market fluctuations, effects of significant trend breaks, and a long-term trend, respectively. Finally, some strategies are proposed for Carbon Price forecasting.
Tao Zhang - One of the best experts on this subject based on the ideXlab platform.
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a novel multiscale nonlinear ensemble leaning paradigm for Carbon Price forecasting
Energy Economics, 2018Co-Authors: Bangzhu Zhu, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode decomposition (EMD) and least square support vector machine (LSSVM) with kernel function prototype is proposed for Carbon Price forecasting. The EMD algorithm is used to decompose the Carbon Price into simple intrinsic mode functions (IMFs) and one residue, which are identified as the components of high frequency, low frequency and trend by using the Lempel-Ziv complexity algorithm. The Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to forecast the high frequency IMFs with ARCH effects. The LSSVM model with kernel function prototype is employed to forecast the high frequency IMFs without ARCH effects, the low frequency and trend components. The forecasting values of all the components are aggregated into the ones of original Carbon Price by the LSSVM with kernel function prototype-based nonlinear ensemble approach. Furthermore, particle swarm optimization is used for model selections of the LSSVM with kernel function prototype. Taking the popular prediction methods as benchmarks, the empirical analysis demonstrates that the proposed model can achieve higher level and directional predictions and higher robustness. The findings show that the proposed model seems an advanced approach for predicting the high nonstationary, nonlinear and irregular Carbon Price.
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forecasting Carbon Price using empirical mode decomposition and evolutionary least squares support vector regression
Applied Energy, 2017Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary Carbon Prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for Carbon Price forecasting. Firstly, each Carbon Price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original Carbon Price. Finally, using four different-matured Carbon futures Prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances.
Dong Han - One of the best experts on this subject based on the ideXlab platform.
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A multiscale analysis for Carbon Price drivers
Energy Economics, 2019Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Yi-ming Wei, Rui XieAbstract:This study proposes a multiscale analysis model to explore and identify the Carbon Price drivers at different timescales. By introducing the latest multivariate empirical mode decomposition, Carbon Price and its potential drivers are decomposed into several groups of simple modes with specific economic meanings. The cointegration techniques, error correction model and Newey–West estimator are combined to capture the Carbon Price drivers at similar timescales. Illustrated by the samples of the European Union Emissions Trading System from 2009 to 2016, a few interesting results can be found that at the original data level, among the three most important drivers of Carbon Price, electricity Price and stock index show positive impacts, while coal Price shows a negative impact. At different timescales, the effects of electricity and stock index appear comparatively earlier, which drive Carbon Price from the short timescales and continue to strengthen. However, the impacts of coal, oil and gas Prices are lagging behind, which respectively drive the Carbon Price at the medium and long timescales.
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forecasting Carbon Price using empirical mode decomposition and evolutionary least squares support vector regression
Applied Energy, 2017Co-Authors: Bangzhu Zhu, Dong Han, Ping Wang, Tao Zhang, Yi-ming WeiAbstract:Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary Carbon Prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for Carbon Price forecasting. Firstly, each Carbon Price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original Carbon Price. Finally, using four different-matured Carbon futures Prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances.