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

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shanglei Chai ◽  
Mo Du ◽  
Xi Chen ◽  
Wenjun Chu
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Emission Trading ◽  
Carbon Price ◽  
Superior Performance ◽  
Forecasting Model ◽  
Price Fluctuation ◽  
Hybrid Forecasting ◽  
Trading Scheme ◽  
Carbon Prices

Predicting CO2 emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO2 emission trading price fluctuation. This model can granulate raw data into fuzzy-information granular components with minimum (Low), average (R), and maximum (Up) values as changing space-description parameters. Furthermore, it can forecast carbon prices’ changing space with Low, R, and Up as inputs to support a vector regression. This method’s feasibility and effectiveness is examined using empirical experiments on European Union allowances’ spot and futures prices under the European Union’s Emissions Trading Scheme. The proposed FIG-SVM model exhibits fewer errors and superior performance than ARIMA, ARFIMA, and Markov-switching methods. This study provides several important implications for investors and risk managers involved in trading carbon financial products.

scholarly journals Does the Impact of Carbon Price Determinants Change with the Different Quantiles of Carbon Prices? Evidence from China ETS Pilots

2020 ◽  
Vol 12 (14) ◽  
pp. 5581 ◽  
Author(s):  
Wenjun Chu ◽  
Shanglei Chai ◽  
Xi Chen ◽  
Mo Du
Keyword(s):  
Emission Trading ◽  
Carbon Price ◽  
Carbon Market ◽  
Emission Trading Scheme ◽  
Energy Prices ◽  
Price Determinants ◽  
Trading Scheme ◽  
The Impact ◽  
Carbon Prices ◽  
Different Levels

Since carbon price volatility is critical to the risk management of the CO2 emissions trading market, research has focused on energy prices and macroeconomic drivers which cause changes in carbon prices and make the carbon market more volatile than other markets. However, they have ignored whether the impact of carbon price determinants changes when the carbon price is at different levels. To fill this gap, this paper applies a semiparametric quantile regression model to explore the effects of energy prices and macroeconomic drivers on carbon prices at different quantiles. The model combines the advantages of parameter estimation, nonparametric estimation and quantile regression to describe the nonlinear relationship between carbon price and its fundamentals, which do not need to make any assumptions about the random error. Carbon prices are high–tailed and exhibit higher kurtosis, the traditional models which tend to assume that data are normally distributed can’t perform well. Furthermore, the semiparametric model doesn’t need to assume that the data are normally distributed. Therefore, the semiparametric model can effectively model the data. Some new evidence from China’s emission trading scheme (ETS) pilots shows that energy prices and macroeconomic drivers have different effects on carbon prices at high or low quantiles. First, the negative impact of coal prices on carbon prices was greater at the lower quantile of carbon prices in the Shenzhen ETS pilot. However, the effects of coal prices were positive in the Beijing ETS pilot, which may be attributed to great demand for coal. Second, oil prices had greater negative effects on carbon prices at higher quantiles in Beijing and Hubei ETS pilots. This can be attributed to the fact that businesses use less oil when carbon prices are high. For the Shenzhen ETS pilot, the effects of oil prices were positive. Third, natural gas prices have a stronger effect on carbon prices as quantiles increased in the Beijing and Hubei ETS pilots. Lastly, the effects of macroeconomic drivers on carbon prices at low quantiles were stronger in the Shenzhen ETS pilots and higher at the medium quantiles in Beijing and Hubei ETS pilots. These findings suggest that the impact of determinants on the carbon prices at different levels is not constant. Ignoring this issue will lead to a missed warning about the risks of the carbon market. This study will be of positive significance for China’s emission trading scheme (ETS) pilots, in order to accurately monitor the effects of carbon prices determinants and effectively avoid carbon market risks.

scholarly journals Hybrid forecasting model for non-linear time series

2020 ◽  
Author(s):  
E. Priyadarshini ◽  
G. Raj Gayathri ◽  
M. Vidhya ◽  
A. Govindarajan ◽  
Samuel Chakkravarthi
Keyword(s):  
Time Series ◽  
Linear Time ◽  
Forecasting Model ◽  
Non Linear ◽  
Hybrid Forecasting

A Hybrid Forecasting Model for Non-Stationary Time Series

2012 ◽  
Vol 3 (2) ◽  
pp. 67-82 ◽  
Author(s):  
Yi Xiao ◽  
Jin Xiao ◽  
Shouyang Wang
Keyword(s):  
Time Series ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Stationary Time Series ◽  
Adaptive Mutation ◽  
Forecasting Model ◽  
Feed Forward Neural Network ◽  
Swarm Optimization ◽  
Individual Fitness ◽  
Hybrid Forecasting

In time series analysis, an important problem is how to extract the information hidden in the non-stationary and noise data and combine it into a model for forecasting. In this paper, the authors propose a TEI@I based hybrid forecasting model. A novel feed forward neural network is developed based on the improved particle swarm optimization with adaptive genetic operator (IPSO-FNN) for forecasting. In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Subsequently, a crossover rate which only depends on generation and an adaptive mutation rate based on individual fitness are designed. The parameters of FNN are optimized by binary and decimal particle swarm optimization. Further, the forecast results of IPSO-FNN are adjusted with the knowledge from text mining and an expert system. The empirical results on the container throughput forecast of Tianjin Port show that forecasts with the proposed method are much better than some other methods.

scholarly journals European emission allowance and equity markets: evidence from further trading phases

2019 ◽  
Vol 36 (4) ◽  
pp. 616-636
Author(s):  
Murad Harasheh ◽  
Andrea Amaduzzi
Keyword(s):  
Time Series ◽  
Value Relevance ◽  
Risk Mitigation ◽  
Emission Trading ◽  
Weighted Average ◽  
Market Model ◽  
Emission Trading Scheme ◽  
Content Type ◽  
Emission Allowance ◽  
Trading Scheme

Purpose This paper aims to investigate the value relevance of the European Emission Allowance (EUA) return and volatility on the equity value of the top listed European Power Generation Firms for the three trading phases of the European Emission Trading Scheme. Design/methodology/approach The authors use the multifactor financial market model over the period 2005-2016 on daily basis for the return relevance relationship, whereas time series models such as autoregression moving average and generalized autoregressive conditional heteroskedasticity are applied on a weighted average portfolio of the sample firms to test serial correlation and volatility of returns. Findings The findings are novel in which a positive and significant relevance of EUA return on equity return is shown; however, a vanishing effect is seen as one moves to further trading phases. Another remarkable finding is that the return relationship remains constant until a certain level in EUA price then inverts. Finally, the authors present that EUA is considered a systematic factor as firm and country-specific features are not statistically significant. Practical implications At policy level, these findings signal policymakers for an appropriate design of the future trading phases in which they achieve the balance between public interests, as climate risk mitigation by reducing emissions, and the private interests of the market players to support innovative changes. Originality/value To the authors’ knowledge, this study would be the first to offer recent and comprehensive findings on the economic and financial implications of the European Emission Trading Scheme for the three trading phases. Additionally, the research offers time series robustness check besides the standard regression analysis and shows that there is an optimal EUA price that triggers polluters’ decision on emission and generation.

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

scholarly journals Carbon Pricing Induces Innovation: Evidence from China's Regional Carbon Market Pilots

2018 ◽  
Vol 108 ◽  
pp. 453-457 ◽  
Author(s):  
Jingbo Cui ◽  
Junjie Zhang ◽  
Yang Zheng
Keyword(s):  
Patent Application ◽  
Emission Trading ◽  
Carbon Price ◽  
Low Carbon ◽  
Firm Level ◽  
Listed Firms ◽  
Allowance Trading ◽  
Application Data ◽  
Trading Scheme ◽  
Low Carbon Technologies

China has launched seven regional pilots of emission trading scheme (ETS) to limit its carbon emissions. Taking advantage of the variations in the regional ETS pilots across regions and sectors and over time, we employ a difference-in-difference-indifferences (DDD) approach to evaluate the effect of ETS on low-carbon innovation at the firm level. Using patent application data of publicly-listed firms in China between 2003 and 2015, we find that the ETS pilots induced innovation in low-carbon technologies. The more active pilots—measured by carbon price and turnover rate of allowance trading—are associated with more intense low-carbon innovation.

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