New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Carbon Finance
1. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Plan
Financial risk & Opportunity of Carbon finance: Price
determinants and volatility estimation of EUA & CER
Ajay Kumar Dhamija
2010SMZ8205
November 21, 2011
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 1 / 35
2. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Overview
1 Introduction
Carbon Finance
Kyoto Protocol
EU ETS
2 Literature Review
The EU ETS Price Formation
Econometric Modeling
AI & Neural Networks
CO2 determinants
3 Research Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
4 Gantt Chart
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 2 / 35
3. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Introduction Carbon Finance
Introduction
Carbon Finance
ˆ Financial risk and opportunities associated with living in carbon
constrained society
ˆ Within auspices of Environmental Finance
ˆ Use of market based instruments to transfer environmental risk
ˆ Resources provided to a project to purchase greenhouse gas
emission reductions
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 3 / 35
4. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Introduction Kyoto Protocol
Introduction
Kyoto Protocol
ˆ Protocol of United Nations Framework Convention on Climate
Change (UNFCCC), targeted to contain global warming
ˆ Initially adopted on 11 December 1997 in Kyoto, Japan, and
entered into force on 16 February 2005
ˆ Three categories of 186 countries
1 Annex I : Leading industrialized countries (41 nations) to cut GHG
((CO2, CH4, N2O, SF6) and two groups of gases HFC & PFC)
emissions by 5.2% below 1990 level (during 2008- 2012)
2 Annex II : Wealthy countries in Annex I (24 nations) to provide
Additional financial & tech. supports to Non-Annex I countries
3 Non-Annex I: Developing countries (145 nations) having no
commitments.
ˆ Sink activities: LULUCF (Land use,Land use change, and
Forestry) activities.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 4 / 35
5. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Introduction Kyoto Protocol
Introduction
Kyoto Protocol
ˆ Four Mechanisms
1 CDM (Clean Development Mechanism) producing CER (Certified
Emission Reduction)
2 IET (International Emissions Trading) ie Carbon Market trading of
AAU (Assigned Amount Unit)
3 JI (Joint Implementation) producing ERU (Emission Reduction Units)
4 European Union ETS (Emissions Trading Scheme) since January
2005 trading EUA (European Union Allowances)
ˆ Problem of Surplus of Allowances
ˆ Regional Trading Schemes e.g. Regional Greenhouse Gas
Initiative (RGGI)
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 5 / 35
6. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Introduction EU ETS
Introduction
EU ETS
ˆ Covers over 11, 000 industrial installations in 15 EU member
states that together are responsible for 40% of the EU’s
greenhouse gas emissions
ˆ The cap is then tightened year-on-year in order to meet
reduction targets
ˆ UNFCCC allocates certificates for units of CO2 emissions
allowances, or carbon credits to polluting industries
ˆ By April every year, companies need to return verified emission
credits regardless of how many credits was allocated
ˆ Companies may either buy / sell credits or adopt technology to
reduce emissions or acquire credits through CER / ERU mode
ˆ Market Players: Energy sector and industrial sector as
compliance buyers and speculators
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 6 / 35
7. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Introduction EU ETS
Introduction
EU ETS - Three Phases
ˆ Phase I (2005-2007)
ˆ Carbon credits given corresponding to 100% of their respective
emissions - Overallocation problem
ˆ Credits not bankable - prices collapsed in mid 2006, but Phase II
futures stable
ˆ Phase II (2008-2012)
ˆ Credits bankable so no price fall is expected
ˆ Recent global financial crisis => industrial production slowdown =>
demand for power decreased => less emissions => collapse of
carbon credit demand
ˆ Super contango structure
ˆ Speculators active
ˆ Phase III (2013-2020)
ˆ 21% reduction of emission targets
ˆ Auction of allocations and Central allocation
ˆ Others sectors like aviation being included
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 7 / 35
8. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review The EU ETS Price Formation
Literature Review
The EU ETS Price Formation
ˆ Aggeryd, J. & Stromqvist, F. (2008). An empirical examination of
the EUA emission rights market. Working Paper, Stockholm School
of Economics
ˆ Alexander, C. (2001). Market Models: A Guide to Financial Data
Analysis. West Sussex: John Wiley & Sons Ltd.
ˆ Benz, E. & Truck, S. (2007). Modeling the price dynamics of CO2
emission allowances. Working Paper, Bonn Graduate School of
Economics, Germany
Findings
Clean dark spread = Pelectricity − Pcoal .
1
ρcoal
+ PCO2
. Ecoal (1)
Clean spark spread = Pelectricity − Pgas .
1
ρgas
+ PCO2
. Egas (2)
Switching P rice =
Pcoal
ρcoal
−
Pgas
ρgas
Egas − Ecoal
(3)
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 8 / 35
9. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review Econometric Modeling
Econometric Modeling
Basic Time Series Analysis
ˆ Benz, E. & Truck, S. (2007). Modeling the price dynamics of CO2
emission allowances. Working Paper, Bonn Graduate School of
Economics, Germany
ˆ Kanamura, T. (2009). A classification study of carbon assets into
commodities. Working Paper, J-Power
ˆ Mansanet-Bataller, M., Tornero, A., & Mico, E. (2006). CO2 prices,
energy and weather. Working Paper, Department of Financial
Economics, University of Valencia
ˆ POMAR (2007). Market analysis and risk management of EU
emissions trading. University of Helsinki & Helsinki University of
Technology
ˆ Sklar, A. (1973). Random variables, joint distribution functions and
copulas. Kybernetika, 9, 449-460.
Findings
ˆ Correlation, Linear Regression, Cointegration, Copula for analysis of
financial time series
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 9 / 35
10. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review Econometric Modeling
Econometric Modeling
Conditional Heteroscedastic Models
Major assumption of least square estimation i.e.
homoscedasticity is violated in financial time series
ˆ Baillie, R. & Bollerslev, T. (1989). The message in daily exchange
rates: A conditional-variance tale. Journal of Business and Economic
Statistics, 7 (3), 297-305.
ˆ Neely, C. J. (1999). Target zones and conditional volatility:the role of
realignments. Journal of Empirical Finance, 6 (2), 177-192
ˆ West, K. D. & Cho, D. (1995). The predictive ability of several
models of exchange rate volatility. Journal of Econometrics, 69 (2),
367-391
ˆ Jorion, P. (1995). Predicting volatility in the foreign exchange
market. Journal of Finance, 50 (2), 507-528
ˆ Andersen, T. & Bollerslev, T. (1998). Answering the skeptics: Yes,
standard volatility models do provide accurate forecasts. International
Economic Review, 39 (4), 885-905
ˆ Mandelbrot, B. B. (1963). The variation of certain speculative prices.
Journal of Business, 36, 394-419
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 10 / 35
11. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review Econometric Modeling
Econometric Modeling
Conditional Heteroscedastic Models
ˆ Dhamija, A. K. & Bhalla, V. K. (2010). Financial Time Series
Forecasting : Comparison Of Various Arch Models. Global Journal of
Finance and Management, 2(1), 159-172
ˆ Fama, F. (1965). Random walks in stock market prices. Financial
Analysts Journal, 21, 55-59
ˆ Engle, R. F. (2003). Risk and volatility: Econometric models and
financial practice. Nobel Lecture
ˆ Tsay, R. S. (2005). Analysis of Financial Time Series. New Jersey:
Wiley Interscience
Findings
ˆ Conditional volatility forecasting using ARCH, GARCH, IGARCH,
TARCH, EGARC for financial time series
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 11 / 35
12. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review Econometric Modeling
Econometric Modeling
Approaches based on fundamentals of Co2
ˆ Paolella, M. & Taschini, L. (2008). An econometric analysis of
emission allowance prices. Journal of Banking and Finance, 32(10),
2022-2032
ˆ Bailey, E. (1998). Intertemporal pricing of sulfur dioxide allowances.
MIT Center for Energy and Environmental Policy Research
ˆ Mittnik, S. & Palolella, M. (2003). Handbook of Heavy Tailed
Distributions in Finance. Ansterdam: Elsevier Science
ˆ DuMouchel, W. H. (1983). Estimating the stable index α in order to
measure tail thickness : A review. Annuls of Statistics, 11(4),
1019-1031
ˆ Hols, M. C. A. B. & de Vries, C. G. (1991). The limiting distribution
of the external exchange rate returns. Journal of Applied
Econometrics, 6, 287-302
ˆ Haas, M., Mittnik, S., & Paolella, M. S. (2004). Mixed normal
conditional heteroskedasticity. Journal of Financial Econometrics, 2
(4), 493-530.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 12 / 35
13. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review Econometric Modeling
Econometric Modeling
Approaches based on fundamentals of Co2
ˆ Alexander, C. & Lazar, E. (2006). Normal mixture GARCH(1,1):
Applications to exchange rate modeling. Journal of Applied
Econometrics, 21, 307-336.
ˆ Harvey, C. R. & Siddique, A. (1999). Autoregressive conditional
skewness. Journal of Financial and Quantitative Analysis, 34 (4),
465-487
ˆ Rockinger, M. & Jondeau, E. (2002). Entropy densities with an
application to autoregressive conditional skewness and kurtosis.
Journal of Econometrics, 106, 119-142
ˆ Brannas, K. & Nordman, N. (2003). Conditional skewness modeling
for stock returns. Applied Econometrics Letters, 10, 725-728
ˆ Kuester, M., Mittnik, S., & Paolella, M. S. (2005). Value-at-risk
prediction: A comparison of alternative strategies. Journal of
Financial Econometrics, 4 (1), 53-89
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 13 / 35
14. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review Econometric Modeling
Econometric Modeling
Findings
ˆ Fundamentals of CO2 - fuel prices and economic growth
ˆ Future-Spot parity of CO2 - convenience yield => backwardation,
cost of carry => contango, super contango
ˆ Most asset retruns are leptokurtic and a need for one distribution
irrespective of time granularity => Stable paretian
ˆ GARCH(1,1) + Innovations fatter than normal and allowance for
asymmetry => Stable Paretian GARCH Sα,β-GARCH
ˆ Preponderance of Zeros precludes gaussian and Sα,β-GARCH =>
Mixture Models of two or more normals (decompositions of
contribution to market volatility) MixN GARCH: flexible, fat tailed,
asymmetric, time varying skewness and kurtosis
ˆ Mixture model with stable paretian components: Stable Mix-GARCH
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 14 / 35
15. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review AI & Neural Networks
AI & Neural Networks
Artificial Neural Networks
ˆ Dhamija, A. K. & Bhalla, V. K. (2011). Exchange rate forecasting:
comparison of various architectures of neural networks. Neural
Computing and Applications 20(3): 355-363
ˆ Dhamija, A. K. & Bhalla, V. K. (2010). Financial Time Series
Forecasting : Comparison of various architectures of Neural Networks
and ARCH models. International Research Journal of Finance and
Economics, 49, 185-202
ˆ Dhamija, A. K. & Bhalla, V. K. (2009). Forecasting Exchange rate:
Use of Neural Networks in Quantitative Finance. VDM Verlag,
ˆ Yao, J. T. & Tan, C. L. (2000). A case study on using neural
networks to perform technical forecasting of forex. Neurocomputing,
34 (1-4), 79-98
ˆ Kiani, K. M. & Kastens, T. L. (2008). Testing forecast accuracy of
foreign exchange rates: Predictions from feed forward and various
recurrent neural network architectures. Computational Economics,
32(4), 383-406
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 15 / 35
16. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review AI & Neural Networks
AI & Neural Networks
Artificial Neural Networks
ˆ Giacomini, E. (2003). Neural networks in quantitative finance.
Master’s thesis University of Berlin
ˆ Hardle, W., Kleinow, T., & Stahl (2002). Applied Quantitative
Finance. Heildelberg: Springer Verlag
Findings
ˆ MLP and RBF networks for conditional volatility estimation.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 16 / 35
17. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review CO2 determinants
Determinants for Carbon Price
Carbon Price Determinants
ˆ Chevallier, J. (2011). Carbon price drivers: An updated literature
review. University Paris, Dauphine, France
ˆ Ellerman, A. & Buchner, B. (2007). Over-allocation or abatement? a
preliminary analysis of the EU ETS based on the 2005-06 emissions
data. Environmental and Resource Economics, 41, 267-287
ˆ Alberola, E., Chevallier, J., & Cheze, B. (2008). Price drivers and
structural breaks in European carbon prices 2005-07. Energy Policy,
36(2), 787-797
ˆ Paolella, M. & Taschini, L. (2008). An econometric analysis of
emission allowance prices. Journal of Banking and Finance, 32(10),
2022-2032
ˆ Daskalakis, G., Psychoyios, D., & Markellos, R. (2009). Modeling
CO2 emission allowance prices and derivatives : Evidence from the
european trading scheme. Journal of Banking and Finance, 33(7),
1230-1241
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 17 / 35
18. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Literature Review CO2 determinants
Determinants for Carbon Price
Carbon Price Determinants
ˆ Mansanet-Bataller, M., Chevallier, J., Herve-Mignucci, M., &
Alberola, E. (2011). EUA and CER phase II price drivers: Unveiling
the reasons for the existence of the EUAs CERs spread. Energy
Policy, 39(3), 1056-1069
ˆ Christiansen, A., Arvanitakis, A., Tangen, K., & Hasselknippe, H.
(2005). Price determinants in the EU emissions trading scheme.
Climate Policy, 5, 15-30
Findings
ˆ Supply side and demand side factors
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 18 / 35
19. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Gaps
Research Methodology
Gaps Identified
1 GARCH models have been employed for EUA ETS phase I, but
no study has yet been done to employ other non-linear AI and
DM methods like NN and GA.
2 Phase II (2008-2012) EUA data has not been studied yet .
3 No study to estimate the volatility of CER and establish its use
as methods of financing, risk diversification and speculation.
4 Carbon is not behaving like other commodities and not priced as
per the established models => model specification error
Current Study
ˆ Data sets of both phase I & II of ETS
ˆ CERs data in Indian context
ˆ AI & DM methods along with GARCH to capture non-linearities
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 19 / 35
20. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Objectives
Research Methodology
Research Objectives
1 To identify and compare the factors which influence returns of
EUA (European Union Allowances) futures in the short term
and long term in the EU ETS Phase I (2005-2007) and II
(2008-2012)
2 To develop short term, long term and the unified econometric
models for forecasting the returns of EUA.
3 To forecast the volatility of returns of EUA and CER using
conditional volatility models and Neural Networks and compare
the results.
4 To conduct Inter phase comparison (phase I: 2005-2007 vs
phase II: 2008-2012) of price determinants and volatility models
for EUA and CER.
5 To conduct a survey among Indian Corporates about their usage
of Carbon Credits (both CER and EUA) as the methods of
financing, risk diversification and speculation.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 20 / 35
21. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Design
Research Methodology
Design of the Study
1 Exploratory study by an extensive literature survey to find out
the factors influencing the returns of EUA
2 Empirical analysis
1 For EUA in European context and for CER in Indian context
2 using Artificial Intelligence and Data Mining techniques like Artificial
Neural Networks and Genetic Algorithms
3 GARCH models
4 Short term, long term and unified models
5 Survey if Indian corporates regarding use of carbon credits for
financing, risk diversification, speculation
3 Synthesis
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 21 / 35
22. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Scope
Research Methodology
Scope
1 The study is confined to two carbon finance instruments, which
are EUA and CER
2 The data would be taken from ECX, ICE and Bloomberg.
3 The study covers both phases of ETS (phase I: 2005-2007,
phase II: 2008-2012) for EUA and data of 2008-2012 for CER.
4 The survey would be conducted in the Indian companies already
dealing in either CER or carbon trading. The survey data would
be collected on-line.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 22 / 35
23. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Determinants
Research Methodology
Table: Carbon price determinants
Factor Time Expected Impact on CO2
Supply Factors
Overall Allocation Long Term -
CDM & JI Supply Medium Term -
Banking of Permits Long Term +
Borrowing of Permits Long Term -
Demand Factors
Economic Growth Medium Term +
Extreme Temperature Short Term +
Rainfall and wind Short Term -
Oil,coal and gas prices Short & Long Term -
Relative prices oil/coal, gas/coal Short & Long Term +
Abatement costs Long Term +
Info on abatement Long Term -
Market power Medium Term +/-
Fundamentally shortage Long Term +
Fundamentally surplus Long Term -
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 23 / 35
24. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Sample
Research Methodology
Sample & Data
1 Carbon Credits: EUA 2008 & 2012 expiry futures
2 Power: EEX Peak Load (Fuel switching occurs on the marginal unit of
power produced)
3 BFMC (ICE): The most liquid fuel market in Europe
4 Coal: API2, the biggest coal derivatives market
5 NBP Summer 2010 Futures Prices (ICE): UK natural gas trading
6 The Dow Jones EURO STOXX 50 Index: Europe’s leading Blue-chip index
for the Eurozone
7 Switching price: Implied NBP and API2, considering their average
efficiency factors and emission coefficients
8 DAX (German Stock Index DAX 30 was formerly known as Deutscher
Aktien IndeX 30
9 Seasonally adjusted industrial production index
10 CER price at ECX, MCX
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 24 / 35
25. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Sample
Research Methodology
Sample & Data
1 All the prices will be converted to Euro
2 Power, coal, and natural gas prices will be stated per MWh equivalent
3 Coal prices (quoted as $/ton) have been converted to e/MWh using the
USDEUR spot rate and a conversion factor of 0.12286 MWh/ton coal, and
natural gas prices (quoted as GBpence/therm) will be converted to
e/MWh using the GBPEUR spot rate and a conversion factor of 0.02931
MWh/therm gas.
4 In concatenating time series data for futures, the return data point
corresponding to discontinuity would be removed
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 25 / 35
26. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Data Sources
Research Methodology
Data Sources
1 SKM SYSPower (a Norwegian power and commodities market data
provider)
2 Bloomberg: Real-time financial information network, which links together
leading financial professionals
3 Inter Continental Exchange (ICE), Atlanta
4 Chicago Climate Exchange (CCX)
5 Nord Pool (Norway) for CO2 futures
6 EEX in Leipzig - CO2 spot transactions
7 ECX in Amsterdam for CO2 futures - 40% of daily volume
8 Powernext in France - CO2 spot transactions - most liquid spot market
9 SendeCO2 in Spain
10 http://www.carbonmarketdata.com/index.php
11 http://www.eea.europa.eu for Allocationsdata
12 http://www.pointcarbon.com
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 26 / 35
27. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Models
Research Methodology
Proposed Models
1 Long Term Model
log(euat) = c + αt log(Brentt) + ut (4)
where eua and Brent(BFMC) are I(1) and co-integrated.
2 Short Term Model
(euat) = c + βt (Brentt) + γt swt + δt euro50t + νt rest−1 + ut
(5)
- is log of first difference
- LHS is the return of eua
- RHS has return of Brent(BFMC)
- sw as Switching Price
- euro50 as return of euro stoxx 50
- res as lagged residual of estimated long term model. The coefficient of
res lagged is negative by construction. It represents the error correction
term, that is the speed of the adjustment process of EUA over time to go
back towards the long term equilibrium.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 27 / 35
28. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Hypotheses
Research Methodology
Hypotheses
1 The long term relationship with the Brent and eua is not significant (αt is
expected to be positive and significant).
2 The residual of the long term regression are not stationary (at 95%
level)i.e. the series, Brent and eua, are not co-integrated.
3 The residuals are not serially correlated.
4 The residuals are heteroscedastic.
5 The relationship between (euat) and (Brentt) is not significant (βt is
expected to be positive and significant).
6 The relationship between (euat) and swt is not significant (γt is
expected to be negative and significant).
7 The relationship between (euat) and euro50t is not significant (δt is
expected to be positive and significant).
8 The speed of adjustment to the long term relationship is not significant (νt
is expected to be negative and significant).
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 28 / 35
29. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Hypotheses
Research Methodology
Hypotheses
9 Pairwise Granger causality among the variables in short term model are not
significant
10 There is no difference in the price determinants of phase I and phase II.
11 There is no significant difference in the conditional volatilities of the two
phases.
12 There is no significant difference in the conditional volatilities of EUAs and
CERs.
13 There is no significant difference between model fitness of the various
approaches i.e. Neural Network gives, conditional heteroscedastic and other
econometric models.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 29 / 35
30. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Analyses
Research Methodology
Analyses
1 Basic time series analysis
1 ρcoal and ρgas will be set to 36% and 50% respectively, Ecoal and
Egas will be set to 0.86 tCO2/MWh and 0.36 tCO2/MWh
respectively . The fluctuations in switching price and EUA will be
compared to see one to one correspondence and hence to assess the
degree of association and to evaluate the fundamentals theory
2 Correlation Analysis: long term and 60 day window correlations
3 Multivariate Regression Analysis: PCA regression to assess the
degree to which a linear combination of multiple independent
variables (power, nat gas, oil, coal, DAX can explain the dependent
variable EUA returns)
4 Prediction: Using the BIC criteria will be done taking care of the
regime switching.
5 Co-integration: to see which independent variables are co-integrated
with EUA
6 Copula Analysis: To study the multivariate joint and marginal
distributions of the variables.
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 30 / 35
31. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Analyses
Research Methodology
Analyses
2 Econometric Analysis
1 Jarque-Bera: rejection of the null hypotheses of normality (prices,
returns, log returns)
2 ADF test: Null hypothesis is non-stationarity
3 Isolation of Trend component
4 Correlogram of EUA (levels) and log (first differences)
5 DF test of first difference log : non-Stationarity
6 ECM combines the long run cointegrating relationship between the
levels variables and the short run relationship between the first
differences of the variables.
3 Conditional Volatility Estimation
1 Neural Networks
2 GARCH models
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 31 / 35
32. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Implications
Research Methodology
Managerial Implications
1 Financial risks and opportunities impact corporate balance
sheets, and market-based instruments are capable of transferring
environmental risk and achieving environmental objectives.
2 CER entail up to 3.0% incremental IRR for renewables / energy
efficiency.
3 CER give high quality cash flow and contract value: OECD
buyers, $ or edenominated, Long-term contract with no price
fluctuation guarantees flow . WB is one of few buyers
purchasing beyond 2012!
4 New instruments for volatility trading
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 32 / 35
33. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Research Methodology Chapter Plan
Research Methodology
Chapter Plan
1 Introduction
2 Literature Review
3 Research Methodology
4 Factors influencing returns of EUA
5 Econometric models for forecasting returns of EUA
6 Estimation of volatilities of returns of EUA and CER
7 Comparative analysis of price determinants and volatilities
models for EUA and CER
8 Survey Analysis and Findings
9 Summary and Conclusions
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 33 / 35
34. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Gantt Chart
Gantt Chart
Timeline
Figure: Gantt Chart
Start date: July 2010
Completion date: December 2012
Duration: Thirty months
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 34 / 35
35. Research Plan
Ajay Kumar
Dhamija
Introduction
Carbon Finance
Kyoto Protocol
EU ETS
Literature
Review
The EU ETS
Price Formation
Econometric
Modeling
AI & Neural
Networks
CO2
determinants
Research
Methodology
Gaps
Objectives
Design
Scope
Determinants
Sample
Data Sources
Models
Hypotheses
Analyses
Implications
Chapter Plan
Gantt Chart
Thank You
Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 35 / 35