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Pricing and Forecasting Carbon Markets: Models and Empirical Analyses 1st ed. 2017 [Hardback]

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  • Formāts: Hardback, 168 pages, height x width: 235x155 mm, weight: 4426 g, 26 Illustrations, color; 14 Illustrations, black and white; XX, 168 p. 40 illus., 26 illus. in color., 1 Hardback
  • Izdošanas datums: 18-May-2017
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319576178
  • ISBN-13: 9783319576176
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Pricing and Forecasting Carbon Markets: Models and Empirical Analyses 1st ed. 2017Palielināt
  • Formāts: Hardback, 168 pages, height x width: 235x155 mm, weight: 4426 g, 26 Illustrations, color; 14 Illustrations, black and white; XX, 168 p. 40 illus., 26 illus. in color., 1 Hardback
  • Izdošanas datums: 18-May-2017
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319576178
  • ISBN-13: 9783319576176
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319576176.html
Keywords:

This book applies the multidisciplinary approaches of econometrics, statistics, finance and artificial intelligence for pricing and forecasting the carbon market in the context of managerial issues. It explores the related issues of pricing and forecasting the carbon market using theoretical models and empirical analyses, demonstrating how the carbon market, as a policy-based artificial market, is complex and influenced by both the market mechanisms and the external heterogeneous environments. 

By integrating the features of analytical systems, it offers insights to further our scientific understanding of the pricing mechanism and the variable laws governing the carbon market. Moreover, it lays a foundation for dealing with climate change in China and constructing a national carbon market there. Ultimately, it actively contributes to the energy saving and CO2 emission reduction promoted by the carbon market.

The carbon market, represented by the European Union Emissions Trading System (EU ETS), is a cost-effective measure for tackling climate change. Furthermore, pricing and forecasting carbon market has been one of the research focuses in the fields of energy and climate change. As a policy tool of the trading mechanism, the carbon market offers a great institutional innovation for coping with climate change. Due to its multiple advantages including saving costs and environment protection, and political feasibility, more and more countries including China have applied the carbon market for carbon dioxide (CO2) emission reduction. Accurately understanding the pricing mechanism and mastering the fluctuating law of carbon market is essential to build a national carbon market for China.

1 New Perspectives on the Econometrics of Carbon Markets
1(12)
1.1 Significance of Pricing and Forecasting Carbon Market
1(1)
1.2 Review of Pricing and Forecasting Carbon Mark et
2(4)
1.2.1 Carbon Price Drivers
2(2)
1.2.2 Carbon Price Singlescale Forecasting
4(1)
1.2.3 Carbon Price Multiscale Forecasting
5(1)
1.3 The Organization of This Book
6(7)
References
9(4)
2 European Carbon Futures Prices Drivers During 2006--2012
13(20)
2.1 Introduction
13(2)
2.2 Carbon Price Drivers
15(2)
2.3 Data
17(2)
2.3.1 Carbon Price
17(1)
2.3.2 Energy Prices
17(1)
2.3.3 Temperature Conditions
17(1)
2.3.4 Economic Activities
18(1)
2.3.5 Institutional Decisions
18(1)
2.4 Cointegration Test and Ridge Regression Results
19(7)
2.4.1 Cointegration Test
19(2)
2.4.2 Ridge Regression Estimation
21(3)
2.4.3 Granger Causality Test
24(2)
2.5 Equilibrium Carbon Price
26(4)
2.5.1 Equilibrium Carbon Price Equation
26(2)
2.5.2 Comparison of Observed Carbon Price and Equilibrium Carbon Price
28(2)
2.6 Conclusion
30(3)
References
30(3)
3 Examining the Structural Changes of European Carbon Futures Price 2005--2012
33(14)
3.1 Introduction
33(1)
3.2 Methodology
34(3)
3.2.1 Iterative Cumulative Sums of Squares (ICSS)
34(1)
3.2.2 Event Study
35(2)
3.2.3 The ICSS-ES Model
37(1)
3.3 Empirical Analysis
37(7)
3.3.1 Data
37(1)
3.3.2 Structural Breakpoint Test Using the ICSS Method
38(1)
3.3.3 Structural Changes Analysis Using the ES Model
39(5)
3.4 Conclusion
44(3)
References
44(3)
4 A Multiscale Analysis for Carbon Price with Ensemble Empirical Mode Decomposition
47(20)
4.1 Introduction
47(3)
4.2 Methodology
50(4)
4.2.1 Empirical Mode Decomposition
50(2)
4.2.2 Ensemble Empirical Mode Decomposition
52(1)
4.2.3 Fine-to-Coarse Reconstruction
53(1)
4.3 Decomposition
54(4)
4.3.1 Data
54(1)
4.3.2 IMFs
55(1)
4.3.3 IMF Statistics
56(2)
4.4 Composition
58(6)
4.4.1 Trend
60(1)
4.4.2 Effects of Significant Events
61(2)
4.4.3 Normal Mark et Disequilibrium
63(1)
4.5 Conclusion
64(3)
References
64(3)
5 Modeling the Dynamics of European Carbon Futures Prices: A Zipf Analysis
67(20)
5.1 Introduction
67(1)
5.2 Methodology
68(2)
5.2.1 Zipf Analysis
68(2)
5.2.2 Economic Significance of the Parameters ε and τ
70(1)
5.3 Empirical Analyses
70(11)
5.3.1 Data
70(1)
5.3.2 The Influences of Investment Timescale and Investor Psychology on the Expected Returns
71(1)
5.3.3 Division of Speculators Based on Parameters
72(5)
5.3.4 Absolute Frequencies of Carbon Price Fluctuations
77(1)
5.3.5 Relative Frequencies of Carbon Price Fluctuations
77(4)
5.4 Results: Analysis and Discussion
81(2)
5.5 Concluding Remarks
83(4)
References
84(3)
6 Carbon Price Forecasting with a Hybrid ARIMA and Least Squares Support Vector Machines Methodology
87(22)
6.1 Introduction
87(2)
6.2 Methodology
89(3)
6.2.1 ARIMA Model
89(1)
6.2.2 Least Squares Support Vector Machines for Regression
89(2)
6.2.3 The Hybrid Models
91(1)
6.3 The Optimal LSSVM Model by Particle Swarm Optimization
92(3)
6.4 Forecasting of Carbon Prices
95(10)
6.4.1 Data
95(1)
6.4.2 Forecasting Evaluation Criteria
96(3)
6.4.3 Parameters Determination of Three Models
99(2)
6.4.4 Statistical Performance
101(3)
6.4.5 Trading Performance
104(1)
6.5 Conclusions
105(4)
References
106(3)
7 Carbon Price Forecasting Using a Parameters Simultaneous Optimized Least Squares Support Vector Machine with Uniform Design
109(24)
7.1 Introduction
109(2)
7.2 Methodology
111(3)
7.2.1 Parameter Selection of a LSSVM Predictor
111(2)
7.2.2 Uniform Design for Parameter Selection of a LSSVM Predictor (UD-LSSVM)
113(1)
7.3 Carbon Forecasting Results and Analyses
114(16)
7.3.1 Data
114(1)
7.3.2 Evaluation Criteria
115(1)
7.3.3 Establishment of the UD-LSSVM Model
116(6)
7.3.4 Comparison with PSO
122(8)
7.4 Conclusion
130(3)
References
131(2)
8 Forecasting Carbon Price with Empirical Mode Decomposition and Least Squares Support Vector Regression
133(12)
8.1 Introduction
133(1)
8.2 Methodology
134(2)
8.2.1 Hybridizing EMD and LSSVR for Carbon Price Prediction
134(2)
8.3 Experimental Analysis
136(6)
8.3.1 Carbon Prices
136(1)
8.3.2 Evaluation Criteria
136(1)
8.3.3 Predicted Results
137(5)
8.4 Conclusion
142(3)
References
143(2)
9 An Adaptive Multiscale Ensemble Learning Paradigm for Carbon Price Forecasting
145(22)
9.1 Introduction
145(2)
9.2 Methodology
147(7)
9.2.1 Kernel Function Prototype
147(1)
9.2.2 The Adaptive Parameter Selection for LSSVM with the PSO Algorithm
148(3)
9.2.3 The Proposed Adaptive Multiscale Ensemble Model for Carbon Price Forecasting
151(3)
9.3 Empirical Analysis
154(9)
9.3.1 Data
154(1)
9.3.2 Evaluation Criteria
154(1)
9.3.3 Nonstationary and Nonlinear Tests of Carbon Price
155(1)
9.3.4 Decomposition of EEMD
156(1)
9.3.5 Identification of HFs, LFs, and T
157(1)
9.3.6 Forecasting Results and Analysis
158(5)
9.4 Conclusion
163(4)
References
164(3)
Index 167
Dr. Bangzhu Zhu is a Professor of Management Science & Engineering at Jinan University, Guangzhou, China. His research interest includes energy and carbon markets, energy economy and climate policy, and big data mining. Dr. Zhu received his postdoctor, Ph.D. and M.Sc. in Management Science & Engineering respectively from the Beijing Institute of Technology in 2012, Beijing University of Aeronautics and Astronautics in 2008, and Guilin University of Electronic Technology in 2004, and his Bachelor of Engineering in Industry Engineering from the Zhengzhou Institute of Aeronautical Industry Management in 1999. Dr. Zhu has a visiting research position at the Beijing Jiaotong University.



Dr. Zhu has published articles in leading refereed journals, including the Omega, Ecological Economics and Journal of Forecasting. He also won respectively the Guangdong Young Zhujiang Scholar, Natural ScienceFoundation for Distinguished Young Talents of Guangdong, 1000-100-10 talent project and outstanding young teacher project of Guangdong, China in 2016, 2014, 2012 and 2014.







Dr. Julien Chevallier is a Tenured Associate Professor of Economics (Professeur des Universités) and Director of the MSc Money, Banking, Finance & Insurance. He undertakes research and lectures on empirical finance, applied time-series econometrics, and commodity markets. Dr. Chevallier received his Ph.D. in Economics from the University Paris West in 2008, and his M.Sc. in Economics from the London School of Economics in 2005. Dr. Chevallier has previously held visiting research positions at the Imperial College Business School (London), at the Centre for Economic Performance (London School of Economics), at Georgetown University, and at the World Bank (Washington DC). Dr. Chevallier is the author of the book Econometric Analysis of Carbon Markets (Springer), as wellas the co-author of the book The Economics of Commodity Markets (Wiley Finance). He has published articles in leading refereed journals, including the International Review of Financial Analysis, Journal of International Financial Markets, Institutions & Money and Quantitative Finance. Furthermore, Dr. Chevallier currently serves as Associate Editor at Energy Economics among other appointments.

Julien Chevallier is a pioneer in the empirical research on currently working emissions trading schemes, an instrument originally developed in environmental economics. In particular, his work has focused on the performance of the European Union Emissions Trading Scheme. Dr. Chevallier applies modern financial econometrics tools to examine a broad set of topics related to emissions trading and its theory. His research includes topics, such as drivers and structural changes in carbon prices, the informational efficiency of EU ETS, and cross-market linkagesbetween emissions trading and energy markets. His work provides deeps insight on how to examine risk components in carbon prices and impacts of risks and risk aversion in a trading market where borrowing is possible.





Dr. Chevallier is the 2015 David Pearce Keynote Speaker at the the Annual EAERE conference held in Helsinki (Finland), on the topic Emissions trading: lessons from the theory and empirics.