Carbon Finance

Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview

The EU ETSPrice Formation

EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research PlanFinancial risk & Opportunity of Carbon finance: Pricedeterminants 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 KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Overview

1 IntroductionCarbon FinanceKyoto ProtocolEU ETS

2 Literature ReviewThe EU ETS Price FormationEconometric ModelingAI & Neural NetworksCO2 determinants

3 Research MethodologyGapsObjectivesDesignScopeDeterminantsSampleData SourcesModelsHypothesesAnalysesImplicationsChapter Plan

4 Gantt Chart


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Introduction Carbon Finance

Introduction

Carbon Finance

� Financial risk and opportunities associated with living in carbonconstrained society

� Within auspices of Environmental Finance

� Use of market based instruments to transfer environmental risk

� Resources provided to a project to purchase greenhouse gasemission reductions


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Introduction Kyoto Protocol

Introduction

Kyoto Protocol

� Protocol of United Nations Framework Convention on ClimateChange (UNFCCC), targeted to contain global warming

� Initially adopted on 11 December 1997 in Kyoto, Japan, andentered into force on 16 February 2005

� 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 countries

3 Non-Annex I: Developing countries (145 nations) having nocommitments.

� Sink activities: LULUCF (Land use,Land use change, andForestry) activities.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Introduction Kyoto Protocol

Introduction

Kyoto Protocol

� Four Mechanisms1 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)

� Problem of Surplus of Allowances

� Regional Trading Schemes e.g. Regional Greenhouse GasInitiative (RGGI)


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Introduction EU ETS

Introduction

EU ETS

� Covers over 11, 000 industrial installations in 15 EU memberstates that together are responsible for 40% of the EU’sgreenhouse gas emissions

� The cap is then tightened year-on-year in order to meetreduction targets

� UNFCCC allocates certificates for units of CO2 emissionsallowances, or carbon credits to polluting industries

� By April every year, companies need to return verified emissioncredits regardless of how many credits was allocated

� Companies may either buy / sell credits or adopt technology toreduce emissions or acquire credits through CER / ERU mode

� Market Players: Energy sector and industrial sector ascompliance buyers and speculators


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Introduction EU ETS

Introduction

EU ETS - Three Phases

� Phase I (2005-2007)� Carbon credits given corresponding to 100% of their respective

emissions - Overallocation problem� Credits not bankable - prices collapsed in mid 2006, but Phase II

futures stable

� Phase II (2008-2012)� Credits bankable so no price fall is expected� Recent global financial crisis => industrial production slowdown =>

demand for power decreased => less emissions => collapse ofcarbon credit demand

� Super contango structure� Speculators active

� Phase III (2013-2020)� 21% reduction of emission targets� Auction of allocations and Central allocation� Others sectors like aviation being included


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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

� Aggeryd, J. & Stromqvist, F. (2008). An empirical examination ofthe EUA emission rights market. Working Paper, Stockholm Schoolof Economics

� Alexander, C. (2001). Market Models: A Guide to Financial DataAnalysis. West Sussex: John Wiley & Sons Ltd.

� Benz, E. & Truck, S. (2007). Modeling the price dynamics of CO2

emission allowances. Working Paper, Bonn Graduate School ofEconomics, Germany

Findings

Clean dark spread = Pelectricity −[Pcoal .

1

ρcoal+ PCO2

. Ecoal

](1)

Clean spark spread = Pelectricity −[Pgas .

1

ρgas+ PCO2

. Egas

](2)

Switching Price =

Pcoalρcoal

− Pgasρgas

Egas − Ecoal(3)


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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

� Benz, E. & Truck, S. (2007). Modeling the price dynamics of CO2

emission allowances. Working Paper, Bonn Graduate School ofEconomics, Germany

� Kanamura, T. (2009). A classification study of carbon assets intocommodities. Working Paper, J-Power

� Mansanet-Bataller, M., Tornero, A., & Mico, E. (2006). CO2 prices,energy and weather. Working Paper, Department of FinancialEconomics, University of Valencia

� POMAR (2007). Market analysis and risk management of EUemissions trading. University of Helsinki & Helsinki University ofTechnology

� Sklar, A. (1973). Random variables, joint distribution functions andcopulas. Kybernetika, 9, 449-460.

Findings

� Correlation, Linear Regression, Cointegration, Copula for analysis offinancial time series


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart



Conditional Heteroscedastic Models

Major assumption of least square estimation i.e.homoscedasticity is violated in financial time series

� Baillie, R. & Bollerslev, T. (1989). The message in daily exchangerates: A conditional-variance tale. Journal of Business and EconomicStatistics, 7 (3), 297-305.

� Neely, C. J. (1999). Target zones and conditional volatility:the role ofrealignments. Journal of Empirical Finance, 6 (2), 177-192

� West, K. D. & Cho, D. (1995). The predictive ability of severalmodels of exchange rate volatility. Journal of Econometrics, 69 (2),367-391

� Jorion, P. (1995). Predicting volatility in the foreign exchangemarket. Journal of Finance, 50 (2), 507-528

� Andersen, T. & Bollerslev, T. (1998). Answering the skeptics: Yes,standard volatility models do provide accurate forecasts. InternationalEconomic Review, 39 (4), 885-905

� Mandelbrot, B. B. (1963). The variation of certain speculative prices.Journal of Business, 36, 394-419


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart



Conditional Heteroscedastic Models

� Dhamija, A. K. & Bhalla, V. K. (2010). Financial Time SeriesForecasting : Comparison Of Various Arch Models. Global Journal ofFinance and Management, 2(1), 159-172

� Fama, F. (1965). Random walks in stock market prices. FinancialAnalysts Journal, 21, 55-59

� Engle, R. F. (2003). Risk and volatility: Econometric models andfinancial practice. Nobel Lecture

� Tsay, R. S. (2005). Analysis of Financial Time Series. New Jersey:Wiley Interscience

Findings

� Conditional volatility forecasting using ARCH, GARCH, IGARCH,TARCH, EGARC for financial time series


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart



Approaches based on fundamentals of Co2� Paolella, M. & Taschini, L. (2008). An econometric analysis of

emission allowance prices. Journal of Banking and Finance, 32(10),2022-2032

� Bailey, E. (1998). Intertemporal pricing of sulfur dioxide allowances.MIT Center for Energy and Environmental Policy Research

� Mittnik, S. & Palolella, M. (2003). Handbook of Heavy TailedDistributions in Finance. Ansterdam: Elsevier Science

� DuMouchel, W. H. (1983). Estimating the stable index α in order tomeasure tail thickness : A review. Annuls of Statistics, 11(4),1019-1031

� Hols, M. C. A. B. & de Vries, C. G. (1991). The limiting distributionof the external exchange rate returns. Journal of AppliedEconometrics, 6, 287-302

� Haas, M., Mittnik, S., & Paolella, M. S. (2004). Mixed normalconditional heteroskedasticity. Journal of Financial Econometrics, 2(4), 493-530.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart



Approaches based on fundamentals of Co2� Alexander, C. & Lazar, E. (2006). Normal mixture GARCH(1,1):

Applications to exchange rate modeling. Journal of AppliedEconometrics, 21, 307-336.

� Harvey, C. R. & Siddique, A. (1999). Autoregressive conditionalskewness. Journal of Financial and Quantitative Analysis, 34 (4),465-487

� Rockinger, M. & Jondeau, E. (2002). Entropy densities with anapplication to autoregressive conditional skewness and kurtosis.Journal of Econometrics, 106, 119-142

� Brannas, K. & Nordman, N. (2003). Conditional skewness modelingfor stock returns. Applied Econometrics Letters, 10, 725-728

� Kuester, M., Mittnik, S., & Paolella, M. S. (2005). Value-at-riskprediction: A comparison of alternative strategies. Journal ofFinancial Econometrics, 4 (1), 53-89


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart



Findings

� Fundamentals of CO2 - fuel prices and economic growth

� Future-Spot parity of CO2 - convenience yield => backwardation,cost of carry => contango, super contango

� Most asset retruns are leptokurtic and a need for one distributionirrespective of time granularity => Stable paretian

� GARCH(1,1) + Innovations fatter than normal and allowance forasymmetry => Stable Paretian GARCH Sα,β-GARCH

� 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 kurtosis

� Mixture model with stable paretian components: Stable Mix-GARCH


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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

� Dhamija, A. K. & Bhalla, V. K. (2011). Exchange rate forecasting:comparison of various architectures of neural networks. NeuralComputing and Applications 20(3): 355-363

� 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, 185-202

� Dhamija, A. K. & Bhalla, V. K. (2009). Forecasting Exchange rate:Use of Neural Networks in Quantitative Finance. VDM Verlag,

� Yao, J. T. & Tan, C. L. (2000). A case study on using neuralnetworks to perform technical forecasting of forex. Neurocomputing,34 (1-4), 79-98

� 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), 383-406


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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

� Giacomini, E. (2003). Neural networks in quantitative finance.Master’s thesis University of Berlin

� Hardle, W., Kleinow, T., & Stahl (2002). Applied QuantitativeFinance. Heildelberg: Springer Verlag

Findings

� MLP and RBF networks for conditional volatility estimation.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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

� Chevallier, J. (2011). Carbon price drivers: An updated literaturereview. University Paris, Dauphine, France

� 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, 267-287

� Alberola, E., Chevallier, J., & Cheze, B. (2008). Price drivers andstructural breaks in European carbon prices 2005-07. Energy Policy,36(2), 787-797

� Paolella, M. & Taschini, L. (2008). An econometric analysis ofemission allowance prices. Journal of Banking and Finance, 32(10),2022-2032

� 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),1230-1241


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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� 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), 1056-1069

� Christiansen, A., Arvanitakis, A., Tangen, K., & Hasselknippe, H.(2005). Price determinants in the EU emissions trading scheme.Climate Policy, 5, 15-30

Findings

� Supply side and demand side factors


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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, 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 error

Current Study

� Data sets of both phase I & II of ETS

� CERs data in Indian context

� AI & DM methods along with GARCH to capture non-linearities


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Objectives


Research Objectives

1 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.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Design


Design of the Study

1 Exploratory study by an extensive literature survey to find outthe factors influencing the returns of EUA

2 Empirical analysis

1 For EUA in European context and for CER in Indian context2 using Artificial Intelligence and Data Mining techniques like Artificial

Neural Networks and Genetic Algorithms3 GARCH models4 Short term, long term and unified models5 Survey if Indian corporates regarding use of carbon credits for

financing, risk diversification, speculation

3 Synthesis


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Scope


Scope

1 The study is confined to two carbon finance instruments, whichare 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 alreadydealing in either CER or carbon trading. The survey data wouldbe collected on-line.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Determinants


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 -


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Sample


Sample & Data

1 Carbon Credits: EUA 2008 & 2012 expiry futures

2 Power: EEX Peak Load (Fuel switching occurs on the marginal unit ofpower 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 indexfor the Eurozone

7 Switching price: Implied NBP and API2, considering their averageefficiency factors and emission coefficients

8 DAX (German Stock Index DAX 30 was formerly known as DeutscherAktien IndeX 30

9 Seasonally adjusted industrial production index

10 CER price at ECX, MCX


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Sample


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 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 removed


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Data Sources


Data Sources

1 SKM SYSPower (a Norwegian power and commodities market dataprovider)

2 Bloomberg: Real-time financial information network, which links togetherleading 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.comAjay Kumar Dhamija (2010SMZ8205) Research Plan November 21, 2011 26 / 35

Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Models


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

4(euat) = c + βt4(Brentt) + γt swt + δt4euro50t + νt rest−1 + ut(5)

- 4 is log of first difference- LHS is the return of eua- RHS has return of Brent(BFMC)- sw as Switching Price- 4 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.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Hypotheses


Hypotheses

1 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 4(euat) and 4(Brentt) is not significant (βt isexpected to be positive and significant).

6 The relationship between 4(euat) and swt is not significant (γt isexpected to be negative and significant).

7 The relationship between 4(euat) and 4euro50t 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).


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Hypotheses


Hypotheses

9 Pairwise Granger causality among the variables in short term model are notsignificant

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 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.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Analyses


Analyses

1 Basic time series analysis

1 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 theory

2 Correlation Analysis: long term and 60 day window correlations3 Multivariate Regression Analysis: PCA regression to assess the

degree 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 EUA

6 Copula Analysis: To study the multivariate joint and marginaldistributions of the variables.


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Analyses


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-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 the

levels variables and the short run relationship between the firstdifferences of the variables.

3 Conditional Volatility Estimation

1 Neural Networks2 GARCH models


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Implications


Managerial Implications

1 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 trading


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Research Methodology Chapter Plan


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 volatilitiesmodels for EUA and CER

8 Survey Analysis and Findings

9 Summary and Conclusions


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

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 2010Completion date: December 2012Duration: Thirty months


Research Plan

Ajay KumarDhamija

Introduction

Carbon Finance

Kyoto Protocol

EU ETS

LiteratureReview


EconometricModeling

AI & NeuralNetworks

CO2determinants

ResearchMethodology

Gaps

Objectives

Design

Scope

Determinants

Sample

Data Sources

Models

Hypotheses

Analyses

Implications

Chapter Plan

Gantt Chart

Thank You
