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Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEcono...
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Carbon Finance

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Transcript of "Carbon Finance"

  1. 1. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch PlanFinancial risk & Opportunity of Carbon finance: Pricedeterminants and volatility estimation of EUA & CERAjay Kumar Dhamija2010SMZ8205November 21, 2011Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 1 / 35
  2. 2. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartOverview1 IntroductionCarbon FinanceKyoto ProtocolEU ETS2 Literature ReviewThe EU ETS Price FormationEconometric ModelingAI & Neural NetworksCO2 determinants3 Research MethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter Plan4 Gantt ChartAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 2 / 35
  3. 3. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartIntroduction Carbon FinanceIntroductionCarbon Financeˆ Financial risk and opportunities associated with living in carbonconstrained societyˆ Within auspices of Environmental Financeˆ Use of market based instruments to transfer environmental riskˆ Resources provided to a project to purchase greenhouse gasemission reductionsAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 3 / 35
  4. 4. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartIntroduction Kyoto ProtocolIntroductionKyoto Protocolˆ Protocol of United Nations Framework Convention on ClimateChange (UNFCCC), targeted to contain global warmingˆ Initially adopted on 11 December 1997 in Kyoto, Japan, andentered into force on 16 February 2005ˆ Three categories of 186 countries1 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 provideAdditional financial & tech. supports to Non-Annex I countries3 Non-Annex I: Developing countries (145 nations) having nocommitments.ˆ Sink activities: LULUCF (Land use,Land use change, andForestry) activities.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 4 / 35
  5. 5. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartIntroduction Kyoto ProtocolIntroductionKyoto Protocolˆ Four Mechanisms1 CDM (Clean Development Mechanism) producing CER (CertifiedEmission Reduction)2 IET (International Emissions Trading) ie Carbon Market trading ofAAU (Assigned Amount Unit)3 JI (Joint Implementation) producing ERU (Emission Reduction Units)4 European Union ETS (Emissions Trading Scheme) since January2005 trading EUA (European Union Allowances)ˆ Problem of Surplus of Allowancesˆ Regional Trading Schemes e.g. Regional Greenhouse GasInitiative (RGGI)Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 5 / 35
  6. 6. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartIntroduction EU ETSIntroductionEU ETSˆ Covers over 11, 000 industrial installations in 15 EU memberstates that together are responsible for 40% of the EU’sgreenhouse gas emissionsˆ The cap is then tightened year-on-year in order to meetreduction targetsˆ UNFCCC allocates certificates for units of CO2 emissionsallowances, or carbon credits to polluting industriesˆ By April every year, companies need to return verified emissioncredits regardless of how many credits was allocatedˆ Companies may either buy / sell credits or adopt technology toreduce emissions or acquire credits through CER / ERU modeˆ Market Players: Energy sector and industrial sector ascompliance buyers and speculatorsAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 6 / 35
  7. 7. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartIntroduction EU ETSIntroductionEU ETS - Three Phasesˆ Phase I (2005-2007)ˆ Carbon credits given corresponding to 100% of their respectiveemissions - Overallocation problemˆ Credits not bankable - prices collapsed in mid 2006, but Phase IIfutures stableˆ Phase II (2008-2012)ˆ Credits bankable so no price fall is expectedˆ Recent global financial crisis => industrial production slowdown =>demand for power decreased => less emissions => collapse ofcarbon credit demandˆ Super contango structureˆ Speculators activeˆ Phase III (2013-2020)ˆ 21% reduction of emission targetsˆ Auction of allocations and Central allocationˆ Others sectors like aviation being includedAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 7 / 35
  8. 8. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review The EU ETS Price FormationLiterature ReviewThe EU ETS Price Formationˆ Aggeryd, J. & Stromqvist, F. (2008). An empirical examination ofthe EUA emission rights market. Working Paper, Stockholm Schoolof Economicsˆ Alexander, C. (2001). Market Models: A Guide to Financial DataAnalysis. West Sussex: John Wiley & Sons Ltd.ˆ Benz, E. & Truck, S. (2007). Modeling the price dynamics of CO2emission allowances. Working Paper, Bonn Graduate School ofEconomics, GermanyFindingsClean dark spread = Pelectricity − Pcoal .1ρcoal+ PCO2. Ecoal (1)Clean spark spread = Pelectricity − Pgas .1ρgas+ PCO2. Egas (2)Switching P rice =Pcoalρcoal−PgasρgasEgas − Ecoal(3)Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 8 / 35
  9. 9. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review Econometric ModelingEconometric ModelingBasic Time Series Analysisˆ Benz, E. & Truck, S. (2007). Modeling the price dynamics of CO2emission allowances. Working Paper, Bonn Graduate School ofEconomics, Germanyˆ Kanamura, T. (2009). A classification study of carbon assets intocommodities. Working Paper, J-Powerˆ Mansanet-Bataller, M., Tornero, A., & Mico, E. (2006). CO2 prices,energy and weather. Working Paper, Department of FinancialEconomics, University of Valenciaˆ POMAR (2007). Market analysis and risk management of EUemissions trading. University of Helsinki & Helsinki University ofTechnologyˆ Sklar, A. (1973). Random variables, joint distribution functions andcopulas. Kybernetika, 9, 449-460.Findingsˆ Correlation, Linear Regression, Cointegration, Copula for analysis offinancial time seriesAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 9 / 35
  10. 10. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review Econometric ModelingEconometric ModelingConditional Heteroscedastic ModelsMajor assumption of least square estimation i.e.homoscedasticity is violated in financial time seriesˆ Baillie, R. & Bollerslev, T. (1989). The message in daily exchangerates: A conditional-variance tale. Journal of Business and EconomicStatistics, 7 (3), 297-305.ˆ Neely, C. J. (1999). Target zones and conditional volatility:the role ofrealignments. Journal of Empirical Finance, 6 (2), 177-192ˆ West, K. D. & Cho, D. (1995). The predictive ability of severalmodels of exchange rate volatility. Journal of Econometrics, 69 (2),367-391ˆ Jorion, P. (1995). Predicting volatility in the foreign exchangemarket. Journal of Finance, 50 (2), 507-528ˆ Andersen, T. & Bollerslev, T. (1998). Answering the skeptics: Yes,standard volatility models do provide accurate forecasts. InternationalEconomic Review, 39 (4), 885-905ˆ Mandelbrot, B. B. (1963). The variation of certain speculative prices.Journal of Business, 36, 394-419Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 10 / 35
  11. 11. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review Econometric ModelingEconometric ModelingConditional Heteroscedastic Modelsˆ Dhamija, A. K. & Bhalla, V. K. (2010). Financial Time SeriesForecasting : Comparison Of Various Arch Models. Global Journal ofFinance and Management, 2(1), 159-172ˆ Fama, F. (1965). Random walks in stock market prices. FinancialAnalysts Journal, 21, 55-59ˆ Engle, R. F. (2003). Risk and volatility: Econometric models andfinancial practice. Nobel Lectureˆ Tsay, R. S. (2005). Analysis of Financial Time Series. New Jersey:Wiley InterscienceFindingsˆ Conditional volatility forecasting using ARCH, GARCH, IGARCH,TARCH, EGARC for financial time seriesAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 11 / 35
  12. 12. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review Econometric ModelingEconometric ModelingApproaches based on fundamentals of Co2ˆ Paolella, M. & Taschini, L. (2008). An econometric analysis ofemission allowance prices. Journal of Banking and Finance, 32(10),2022-2032ˆ Bailey, E. (1998). Intertemporal pricing of sulfur dioxide allowances.MIT Center for Energy and Environmental Policy Researchˆ Mittnik, S. & Palolella, M. (2003). Handbook of Heavy TailedDistributions in Finance. Ansterdam: Elsevier Scienceˆ DuMouchel, W. H. (1983). Estimating the stable index α in order tomeasure tail thickness : A review. Annuls of Statistics, 11(4),1019-1031ˆ Hols, M. C. A. B. & de Vries, C. G. (1991). The limiting distributionof the external exchange rate returns. Journal of AppliedEconometrics, 6, 287-302ˆ Haas, M., Mittnik, S., & Paolella, M. S. (2004). Mixed normalconditional heteroskedasticity. Journal of Financial Econometrics, 2(4), 493-530.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 12 / 35
  13. 13. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review Econometric ModelingEconometric ModelingApproaches based on fundamentals of Co2ˆ Alexander, C. & Lazar, E. (2006). Normal mixture GARCH(1,1):Applications to exchange rate modeling. Journal of AppliedEconometrics, 21, 307-336.ˆ Harvey, C. R. & Siddique, A. (1999). Autoregressive conditionalskewness. Journal of Financial and Quantitative Analysis, 34 (4),465-487ˆ Rockinger, M. & Jondeau, E. (2002). Entropy densities with anapplication to autoregressive conditional skewness and kurtosis.Journal of Econometrics, 106, 119-142ˆ Brannas, K. & Nordman, N. (2003). Conditional skewness modelingfor stock returns. Applied Econometrics Letters, 10, 725-728ˆ Kuester, M., Mittnik, S., & Paolella, M. S. (2005). Value-at-riskprediction: A comparison of alternative strategies. Journal ofFinancial Econometrics, 4 (1), 53-89Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 13 / 35
  14. 14. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review Econometric ModelingEconometric ModelingFindingsˆ Fundamentals of CO2 - fuel prices and economic growthˆ Future-Spot parity of CO2 - convenience yield => backwardation,cost of carry => contango, super contangoˆ Most asset retruns are leptokurtic and a need for one distributionirrespective of time granularity => Stable paretianˆ GARCH(1,1) + Innovations fatter than normal and allowance forasymmetry => Stable Paretian GARCH Sα,β-GARCHˆ Preponderance of Zeros precludes gaussian and Sα,β-GARCH =>Mixture Models of two or more normals (decompositions ofcontribution to market volatility) MixN GARCH: flexible, fat tailed,asymmetric, time varying skewness and kurtosisˆ Mixture model with stable paretian components: Stable Mix-GARCHAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 14 / 35
  15. 15. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review AI & Neural NetworksAI & Neural NetworksArtificial Neural Networksˆ Dhamija, A. K. & Bhalla, V. K. (2011). Exchange rate forecasting:comparison of various architectures of neural networks. NeuralComputing and Applications 20(3): 355-363ˆ Dhamija, A. K. & Bhalla, V. K. (2010). Financial Time SeriesForecasting : Comparison of various architectures of Neural Networksand ARCH models. International Research Journal of Finance andEconomics, 49, 185-202ˆ 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 neuralnetworks to perform technical forecasting of forex. Neurocomputing,34 (1-4), 79-98ˆ Kiani, K. M. & Kastens, T. L. (2008). Testing forecast accuracy offoreign exchange rates: Predictions from feed forward and variousrecurrent neural network architectures. Computational Economics,32(4), 383-406Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 15 / 35
  16. 16. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review AI & Neural NetworksAI & Neural NetworksArtificial Neural Networksˆ Giacomini, E. (2003). Neural networks in quantitative finance.Master’s thesis University of Berlinˆ Hardle, W., Kleinow, T., & Stahl (2002). Applied QuantitativeFinance. Heildelberg: Springer VerlagFindingsˆ MLP and RBF networks for conditional volatility estimation.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 16 / 35
  17. 17. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review CO2 determinantsDeterminants for Carbon PriceCarbon Price Determinantsˆ Chevallier, J. (2011). Carbon price drivers: An updated literaturereview. University Paris, Dauphine, Franceˆ Ellerman, A. & Buchner, B. (2007). Over-allocation or abatement? apreliminary analysis of the EU ETS based on the 2005-06 emissionsdata. Environmental and Resource Economics, 41, 267-287ˆ Alberola, E., Chevallier, J., & Cheze, B. (2008). Price drivers andstructural breaks in European carbon prices 2005-07. Energy Policy,36(2), 787-797ˆ Paolella, M. & Taschini, L. (2008). An econometric analysis ofemission allowance prices. Journal of Banking and Finance, 32(10),2022-2032ˆ Daskalakis, G., Psychoyios, D., & Markellos, R. (2009). ModelingCO2 emission allowance prices and derivatives : Evidence from theeuropean trading scheme. Journal of Banking and Finance, 33(7),1230-1241Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 17 / 35
  18. 18. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartLiterature Review CO2 determinantsDeterminants for Carbon PriceCarbon Price Determinantsˆ Mansanet-Bataller, M., Chevallier, J., Herve-Mignucci, M., &Alberola, E. (2011). EUA and CER phase II price drivers: Unveilingthe reasons for the existence of the EUAs CERs spread. EnergyPolicy, 39(3), 1056-1069ˆ Christiansen, A., Arvanitakis, A., Tangen, K., & Hasselknippe, H.(2005). Price determinants in the EU emissions trading scheme.Climate Policy, 5, 15-30Findingsˆ Supply side and demand side factorsAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 18 / 35
  19. 19. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology GapsResearch MethodologyGaps Identified1 GARCH models have been employed for EUA ETS phase I, butno study has yet been done to employ other non-linear AI andDM 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 useas methods of financing, risk diversification and speculation.4 Carbon is not behaving like other commodities and not priced asper the established models => model specification errorCurrent Studyˆ Data sets of both phase I & II of ETSˆ CERs data in Indian contextˆ AI & DM methods along with GARCH to capture non-linearitiesAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 19 / 35
  20. 20. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology ObjectivesResearch MethodologyResearch Objectives1 To identify and compare the factors which influence returns ofEUA (European Union Allowances) futures in the short termand long term in the EU ETS Phase I (2005-2007) and II(2008-2012)2 To develop short term, long term and the unified econometricmodels for forecasting the returns of EUA.3 To forecast the volatility of returns of EUA and CER usingconditional volatility models and Neural Networks and comparethe results.4 To conduct Inter phase comparison (phase I: 2005-2007 vsphase II: 2008-2012) of price determinants and volatility modelsfor EUA and CER.5 To conduct a survey among Indian Corporates about their usageof Carbon Credits (both CER and EUA) as the methods offinancing, risk diversification and speculation.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 20 / 35
  21. 21. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology DesignResearch MethodologyDesign of the Study1 Exploratory study by an extensive literature survey to find outthe factors influencing the returns of EUA2 Empirical analysis1 For EUA in European context and for CER in Indian context2 using Artificial Intelligence and Data Mining techniques like ArtificialNeural Networks and Genetic Algorithms3 GARCH models4 Short term, long term and unified models5 Survey if Indian corporates regarding use of carbon credits forfinancing, risk diversification, speculation3 SynthesisAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 21 / 35
  22. 22. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology ScopeResearch MethodologyScope1 The study is confined to two carbon finance instruments, whichare EUA and CER2 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 alreadydealing in either CER or carbon trading. The survey data wouldbe collected on-line.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 22 / 35
  23. 23. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology DeterminantsResearch MethodologyTable: Carbon price determinantsFactor Time Expected Impact on CO2Supply FactorsOverall Allocation Long Term -CDM & JI Supply Medium Term -Banking of Permits Long Term +Borrowing of Permits Long Term -Demand FactorsEconomic 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. 24. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology SampleResearch MethodologySample & Data1 Carbon Credits: EUA 2008 & 2012 expiry futures2 Power: EEX Peak Load (Fuel switching occurs on the marginal unit ofpower produced)3 BFMC (ICE): The most liquid fuel market in Europe4 Coal: API2, the biggest coal derivatives market5 NBP Summer 2010 Futures Prices (ICE): UK natural gas trading6 The Dow Jones EURO STOXX 50 Index: Europe’s leading Blue-chip indexfor the Eurozone7 Switching price: Implied NBP and API2, considering their averageefficiency factors and emission coefficients8 DAX (German Stock Index DAX 30 was formerly known as DeutscherAktien IndeX 309 Seasonally adjusted industrial production index10 CER price at ECX, MCXAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 24 / 35
  25. 25. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology SampleResearch MethodologySample & Data1 All the prices will be converted to Euro2 Power, coal, and natural gas prices will be stated per MWh equivalent3 Coal prices (quoted as $/ton) have been converted to e/MWh using theUSDEUR spot rate and a conversion factor of 0.12286 MWh/ton coal, andnatural gas prices (quoted as GBpence/therm) will be converted toe/MWh using the GBPEUR spot rate and a conversion factor of 0.02931MWh/therm gas.4 In concatenating time series data for futures, the return data pointcorresponding to discontinuity would be removedAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 25 / 35
  26. 26. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology Data SourcesResearch MethodologyData Sources1 SKM SYSPower (a Norwegian power and commodities market dataprovider)2 Bloomberg: Real-time financial information network, which links togetherleading financial professionals3 Inter Continental Exchange (ICE), Atlanta4 Chicago Climate Exchange (CCX)5 Nord Pool (Norway) for CO2 futures6 EEX in Leipzig - CO2 spot transactions7 ECX in Amsterdam for CO2 futures - 40% of daily volume8 Powernext in France - CO2 spot transactions - most liquid spot market9 SendeCO2 in Spain10 http://www.carbonmarketdata.com/index.php11 http://www.eea.europa.eu for Allocationsdata12 http://www.pointcarbon.comAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 26 / 35
  27. 27. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology ModelsResearch MethodologyProposed Models1 Long Term Modellog(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 ofres lagged is negative by construction. It represents the error correctionterm, that is the speed of the adjustment process of EUA over time to goback towards the long term equilibrium.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 27 / 35
  28. 28. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology HypothesesResearch MethodologyHypotheses1 The long term relationship with the Brent and eua is not significant (αt isexpected 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 isexpected to be positive and significant).6 The relationship between (euat) and swt is not significant (γt isexpected to be negative and significant).7 The relationship between (euat) and euro50t is not significant (δt isexpected to be positive and significant).8 The speed of adjustment to the long term relationship is not significant (νtis expected to be negative and significant).Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 28 / 35
  29. 29. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology HypothesesResearch MethodologyHypotheses9 Pairwise Granger causality among the variables in short term model are notsignificant10 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 twophases.12 There is no significant difference in the conditional volatilities of EUAs andCERs.13 There is no significant difference between model fitness of the variousapproaches i.e. Neural Network gives, conditional heteroscedastic and othereconometric models.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 29 / 35
  30. 30. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology AnalysesResearch MethodologyAnalyses1 Basic time series analysis1 ρcoal and ρgas will be set to 36% and 50% respectively, Ecoal andEgas will be set to 0.86 tCO2/MWh and 0.36 tCO2/MWhrespectively . The fluctuations in switching price and EUA will becompared to see one to one correspondence and hence to assess thedegree of association and to evaluate the fundamentals theory2 Correlation Analysis: long term and 60 day window correlations3 Multivariate Regression Analysis: PCA regression to assess thedegree to which a linear combination of multiple independentvariables (power, nat gas, oil, coal, DAX can explain the dependentvariable EUA returns)4 Prediction: Using the BIC criteria will be done taking care of theregime switching.5 Co-integration: to see which independent variables are co-integratedwith EUA6 Copula Analysis: To study the multivariate joint and marginaldistributions of the variables.Ajay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 30 / 35
  31. 31. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology AnalysesResearch MethodologyAnalyses2 Econometric Analysis1 Jarque-Bera: rejection of the null hypotheses of normality (prices,returns, log returns)2 ADF test: Null hypothesis is non-stationarity3 Isolation of Trend component4 Correlogram of EUA (levels) and log (first differences)5 DF test of first difference log : non-Stationarity6 ECM combines the long run cointegrating relationship between thelevels variables and the short run relationship between the firstdifferences of the variables.3 Conditional Volatility Estimation1 Neural Networks2 GARCH modelsAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 31 / 35
  32. 32. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology ImplicationsResearch MethodologyManagerial Implications1 Financial risks and opportunities impact corporate balancesheets, and market-based instruments are capable of transferringenvironmental risk and achieving environmental objectives.2 CER entail up to 3.0% incremental IRR for renewables / energyefficiency.3 CER give high quality cash flow and contract value: OECDbuyers, $ or edenominated, Long-term contract with no pricefluctuation guarantees flow . WB is one of few buyerspurchasing beyond 2012!4 New instruments for volatility tradingAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 32 / 35
  33. 33. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartResearch Methodology Chapter PlanResearch MethodologyChapter Plan1 Introduction2 Literature Review3 Research Methodology4 Factors influencing returns of EUA5 Econometric models for forecasting returns of EUA6 Estimation of volatilities of returns of EUA and CER7 Comparative analysis of price determinants and volatilitiesmodels for EUA and CER8 Survey Analysis and Findings9 Summary and ConclusionsAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 33 / 35
  34. 34. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartGantt ChartGantt ChartTimelineFigure: Gantt ChartStart date: July 2010Completion date: December 2012Duration: Thirty monthsAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 34 / 35
  35. 35. Research PlanAjay KumarDhamijaIntroductionCarbon FinanceKyoto ProtocolEU ETSLiteratureReviewThe EU ETSPrice FormationEconometricModelingAI & NeuralNetworksCO2determinantsResearchMethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter PlanGantt ChartThank YouAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 35 / 35
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