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Carbon Finance

This document outlines a research plan for analyzing the price determinants and volatility of EU emission allowances (EUAs) and certified emission reductions (CERs). The plan includes an introduction to carbon finance, the Kyoto Protocol, and the EU Emissions Trading Scheme (ETS). It then reviews relevant literature on EU ETS price formation, econometric modeling approaches, and determinants of carbon dioxide emissions. Finally, it describes the proposed research methodology, including identifying gaps in current research, objectives, design, variables, data sources, models, hypotheses, analyses, and implications. The plan is to guide research on the financial risk and opportunities associated with the carbon market.

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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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

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Carbon Finance

  • 1.
    Research Plan Ajay Kumar Dhamija Introduction CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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 CarbonFinance 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