A Structural Model for Carbon Cap-and-Trade Schemes

Similar documents
Risk-Neutral Pricing of Financial Instruments in Emission Markets: A Structural Approach

Market Design for Emission Trading Schemes

Risk-Neutral Modeling of Emission Allowance Prices

The valuation of clean spread options: linking electricity, emissions and fuels

SPDE and portfolio choice (joint work with M. Musiela) Princeton University. Thaleia Zariphopoulou The University of Texas at Austin

Tangent Lévy Models. Sergey Nadtochiy (joint work with René Carmona) Oxford-Man Institute of Quantitative Finance University of Oxford.

On the pricing of emission allowances

M5MF6. Advanced Methods in Derivatives Pricing

Commodity and Energy Markets

MATH3075/3975 FINANCIAL MATHEMATICS TUTORIAL PROBLEMS

Stochastic Finance 2010 Summer School Ulm Lecture 1: Energy Derivatives

Valuation of Power Plants and Abatement Costs in Carbon Markets

BACHELIER FINANCE SOCIETY. 4 th World Congress Tokyo, Investments and forward utilities. Thaleia Zariphopoulou The University of Texas at Austin

1.1 Basic Financial Derivatives: Forward Contracts and Options

( ) since this is the benefit of buying the asset at the strike price rather

Model-independent bounds for Asian options

Partial differential approach for continuous models. Closed form pricing formulas for discretely monitored models

Structural Models of Credit Risk and Some Applications

Lecture 4. Finite difference and finite element methods

arxiv: v2 [q-fin.pr] 23 Nov 2017

THE MARTINGALE METHOD DEMYSTIFIED

Stochastic modelling of electricity markets Pricing Forwards and Swaps

The Endogenous Price Dynamics of Emission Permits in the Presence of

Path Dependent British Options

Economathematics. Problem Sheet 1. Zbigniew Palmowski. Ws 2 dw s = 1 t

From Discrete Time to Continuous Time Modeling

Polynomial processes in stochastic portofolio theory

Lecture 17. The model is parametrized by the time period, δt, and three fixed constant parameters, v, σ and the riskless rate r.

The Self-financing Condition: Remembering the Limit Order Book

Pricing theory of financial derivatives

On Using Shadow Prices in Portfolio optimization with Transaction Costs

θ(t ) = T f(0, T ) + σ2 T

Forward Dynamic Utility

Replication under Price Impact and Martingale Representation Property

The British Russian Option

25857 Interest Rate Modelling

Optimal investments under dynamic performance critria. Lecture IV

An overview of some financial models using BSDE with enlarged filtrations

Singular Stochastic Control Models for Optimal Dynamic Withdrawal Policies in Variable Annuities

Optimal Selling Strategy With Piecewise Linear Drift Function

Model-independent bounds for Asian options

Hedging Credit Derivatives in Intensity Based Models

Stochastic Partial Differential Equations and Portfolio Choice. Crete, May Thaleia Zariphopoulou

Lecture 3: Review of mathematical finance and derivative pricing models

Optimally Thresholded Realized Power Variations for Lévy Jump Diffusion Models

Supply Contracts with Financial Hedging

Optimal Stopping Rules of Discrete-Time Callable Financial Commodities with Two Stopping Boundaries

Stochastic Volatility (Working Draft I)

Hedging of Credit Derivatives in Models with Totally Unexpected Default

Hedging Under Jump Diffusions with Transaction Costs. Peter Forsyth, Shannon Kennedy, Ken Vetzal University of Waterloo

MASM006 UNIVERSITY OF EXETER SCHOOL OF ENGINEERING, COMPUTER SCIENCE AND MATHEMATICS MATHEMATICAL SCIENCES FINANCIAL MATHEMATICS.

B8.3 Week 2 summary 2018

Limit Theorems for the Empirical Distribution Function of Scaled Increments of Itô Semimartingales at high frequencies

Extended Libor Models and Their Calibration

IMPA Commodities Course : Forward Price Models

Evaluating Electricity Generation, Energy Options, and Complex Networks

PDE Approach to Credit Derivatives

Imperfect Information and Market Segmentation Walsh Chapter 5

Evaluation of proportional portfolio insurance strategies

Comprehensive Exam. August 19, 2013

Stock Loan Valuation Under Brownian-Motion Based and Markov Chain Stock Models

Chapter 3: Black-Scholes Equation and Its Numerical Evaluation

Bluff Your Way Through Black-Scholes

4. Black-Scholes Models and PDEs. Math6911 S08, HM Zhu

Generalized Multi-Factor Commodity Spot Price Modeling through Dynamic Cournot Resource Extraction Models

Asymptotic Method for Singularity in Path-Dependent Option Pricing

Optimal asset allocation under forward performance criteria Oberwolfach, February 2007

QI SHANG: General Equilibrium Analysis of Portfolio Benchmarking

Local Volatility Dynamic Models

Optimal Investment for Worst-Case Crash Scenarios

Option pricing in the stochastic volatility model of Barndorff-Nielsen and Shephard

Help Session 2. David Sovich. Washington University in St. Louis

Math 6810 (Probability) Fall Lecture notes

The Black-Scholes PDE from Scratch

Path-dependent inefficient strategies and how to make them efficient.

Principal-Agent Problems in Continuous Time

Optimal Switching Games for Emissions Trading

Martingale Pricing Theory in Discrete-Time and Discrete-Space Models

Martingale Measure TA

CONVERGENCE OF OPTION REWARDS FOR MARKOV TYPE PRICE PROCESSES MODULATED BY STOCHASTIC INDICES

Stochastic Calculus, Application of Real Analysis in Finance

A No-Arbitrage Theorem for Uncertain Stock Model

Approximations of Stochastic Programs. Scenario Tree Reduction and Construction

A Robust Option Pricing Problem

CHAPTER 12. Hedging. hedging strategy = replicating strategy. Question : How to find a hedging strategy? In other words, for an attainable contingent

Parametric Inference and Dynamic State Recovery from Option Panels. Nicola Fusari

Valuation of derivative assets Lecture 8

American options and early exercise

Basic Arbitrage Theory KTH Tomas Björk

NEWCASTLE UNIVERSITY SCHOOL OF MATHEMATICS, STATISTICS & PHYSICS SEMESTER 1 SPECIMEN 2 MAS3904. Stochastic Financial Modelling. Time allowed: 2 hours

Multiple Optimal Stopping Problems and Lookback Options

A Worst-Case Approach to Option Pricing in Crash-Threatened Markets

1.1 Implied probability of default and credit yield curves

Variable Annuities with Lifelong Guaranteed Withdrawal Benefits

Volatility. Roberto Renò. 2 March 2010 / Scuola Normale Superiore. Dipartimento di Economia Politica Università di Siena

Asset Pricing Models with Underlying Time-varying Lévy Processes

Lecture Notes for Chapter 6. 1 Prototype model: a one-step binomial tree

Optimal Order Placement

Optimal Dividend Policy of A Large Insurance Company with Solvency Constraints. Zongxia Liang

Interest rate models in continuous time

Transcription:

A Structural Model for Carbon Cap-and-Trade Schemes Sam Howison and Daniel Schwarz University of Oxford, Oxford-Man Institute The New Commodity Markets Oxford-Man Institute, 15 June 2011

Introduction

The Need for Emission Reduction Which Policy Options Should we Use? Aim is to reduce emission of atmospheric gases such as carbon dioxide, methane, ozone and water vapour. Different policy options: Emission norm (direct regulation), Emission tax (market based), Emissions trading (market based).

Emissions Trading How Does it Work? For each country that participates in an emissions trading scheme Impose binding limit (cap) on the cumulative emissions during one year (compliance period) and penalise excess emissions (monetary penalty). Divide cap into equal amounts, which define one unit (AAU). Print paper certificates (allowances) representing one AAU and distribute to firms (initial allocation). Trade allowances.

Emissions Trading How Does it Work? For each country that participates in an emissions trading scheme Impose binding limit (cap) on the cumulative emissions during one year (compliance period) and penalise excess emissions (monetary penalty). Divide cap into equal amounts, which define one unit (AAU). Print paper certificates (allowances) representing one AAU and distribute to firms (initial allocation). Trade allowances. Leads to a liquid market and price formation.

Full equilibrium models: Modelling Approaches 1. R. Carmona et al., Market design for emission trading schemes, 2009 2. R. Carmona et al., Optimal stochastic control and carbon price formation, 2009 Stylized risk-neutral models: 1. R. Carmona et al., Risk-neutral models for emission allowance prices and option valuation, 2010. 2. K. Borovkov et al., Jump-diffusion modelling in emission markets, 2010. 3. J. Hinz et al., On fair pricing of emission-related derivatives, 2010. Structural approach: 1. M. Coulon, Modelling Price Dynamics Through Fundamental Drivers in Electricity and Other Energy Markets, 2009 2. S. Howison and D. Schwarz, Risk-neutral pricing of financial instruments in emission markets, 2010.

Full equilibrium models: Modelling Approaches 1. R. Carmona et al., Market design for emission trading schemes, 2009 2. R. Carmona et al., Optimal stochastic control and carbon price formation, 2009 Stylized risk-neutral models: 1. R. Carmona et al., Risk-neutral models for emission allowance prices and option valuation, 2010. 2. K. Borovkov et al., Jump-diffusion modelling in emission markets, 2010. 3. J. Hinz et al., On fair pricing of emission-related derivatives, 2010. Structural approach: 1. M. Coulon, Modelling Price Dynamics Through Fundamental Drivers in Electricity and Other Energy Markets, 2009 2. S. Howison and D. Schwarz, Risk-neutral pricing of financial instruments in emission markets, 2010. Aim: to explain the price of allowances and of derivatives written on them as a function of demand for a pollution-causing good and cumulative emissions.

From Electricity to CO 2 Emissions

[0, T ] Market Setup Introducing the Key Drivers of the Pricing Model finite time interval (Ω, F, (F t ), P) (F t ) generated by (W t ) R 2 ξ max market s capacity to produce electricity (ξ t ) supply as a proportion of capacity (0 ξ t 1) (D t ) demand as a proportion of capacity (0 D t 1) Walrasian equilibrium assumption, D t = ξ t. (E t ) cumulative emissions up to time t (A t ) allowance certificate price

The Bid- and Emissions Stack Price Setting and Emission Measurement in Electricity Markets Assumption The market administrator arranges generators bids in increasing order of bid price (merit order).

The Bid- and Emissions Stack Price Setting and Emission Measurement in Electricity Markets Assumption The market administrator arranges generators bids in increasing order of bid price (merit order). Definition The business-as-usual bid stack is given by b BAU (ξ) a map from the marginal production unit to the corresponding price of electricity (e per MWh), db BAU dξ (ξ) > 0. Bid Stack

The Bid- and Emissions Stack Price Setting and Emission Measurement in Electricity Markets Assumption The market administrator arranges generators bids in increasing order of bid price (merit order). Definition The business-as-usual bid stack is given by b BAU (ξ) a map from the marginal production unit to the corresponding price of electricity (e per MWh), db BAU dξ (ξ) > 0. Bid Stack Allows us to deduce the generation order.

The Bid- and Emissions Stack Price Setting and Emission Measurement in Electricity Markets Definition The marginal emissions stack is given by e(ξ) a map from the marginal production unit to its marginal emissions rate (tco 2 per MWh). Emissions Stack

The Bid- and Emissions Stack Price Setting and Emission Measurement in Electricity Markets Definition The marginal emissions stack is given by e(ξ) a map from the marginal production unit to its marginal emissions rate (tco 2 per MWh). Emissions Stack To obtain the business-as-usual market emissions rate, µ BAU e (ξ), integrate the marginal emissions stack up to current level of demand: D µ BAU e (D) := e(ξ) dξ. 0

Load Shifting The Effects of Emissions Trading on the BAU Economy With emissions trading, bids increase by (cost of carbon) (marginal emissions rate). Carbon Impact

Load Shifting The Effects of Emissions Trading on the BAU Economy With emissions trading, bids increase by (cost of carbon) (marginal emissions rate). Carbon Impact Given an allowance price A 0, the bid stack now becomes b(ξ; A) := b BAU (ξ) + Ae(ξ). For A > 0 this function may no longer be monotonic!

Load Shifting The Effects of Emissions Trading on the BAU Economy Under the previous assumptions, given electricity price P and allowance price A, active generators are uniquely identified by { } S(A, P) := ξ [0, 1] : b BAU (ξ) + Ae(ξ) P. Further, given demand D, the market price of electricity P is P(A, D) = inf {P 0 : λ(s(a, P)) D}, where λ( ) denotes the Lebesgue measure.

Load Shifting The Effects of Emissions Trading on the BAU Economy Under the previous assumptions, given electricity price P and allowance price A, active generators are uniquely identified by { } S(A, P) := ξ [0, 1] : b BAU (ξ) + Ae(ξ) P. Further, given demand D, the market price of electricity P is P(A, D) = inf {P 0 : λ(s(a, P)) D}, where λ( ) denotes the Lebesgue measure. Market emissions rate µ e (A, D) becomes µ e (A, D) := 1 0 I S(A,P(A,D)) (ξ)e(ξ) dξ.

600 b BAU ( ) The Bid Stack under BAU Price (Euro/MWh) 100 0 D 1 Supply (as a fraction of the market capacity) 1 The Emissions Stack under BAU e( ) Marginal Emissions (tco 2 /MWh) 0.4 0 D 1 Supply (as a fraction of the market capacity)

600 b BAU ( ) b( ;A), A=100 The Bid Stack under Cap and Trade Price (Euro/MWh) 100 0 ξ 1 D ξ 2 1 Supply (as a fraction of the market capacity) 1 The Emissions Stack under Cap and Trade Marginal Emissions (tco 2 /MWh) 0.4 0 ξ 1 D ξ 2 1 Supply (as a fraction of the market capacity)

Allowance Pricing

Market Assumptions Applying the Risk-Neutral Pricing Methodology Traded assets in the market are Allowance certificates Derivatives written on the certificate Riskless money market account Assumption There exists an equivalent (risk-neutral) martingale measure P, under which, for 0 t T, the discounted price of any tradable asset is a martingale.

Market Assumptions Concretising the Demand and Emissions Process Demand (as a fraction of capacity ξ max ) evolves according to the following Itô diffusion; for 0 t T, dd t = η(d t D(t))dt+ 2ησ d D t (1 D t )d W 1 t, D 0 = d (0, 1). With this definition D t (0, 1) t [0, T ].

Market Assumptions Concretising the Demand and Emissions Process Demand (as a fraction of capacity ξ max ) evolves according to the following Itô diffusion; for 0 t T, dd t = η(d t D(t))dt+ 2ησ d D t (1 D t )d W 1 t, D 0 = d (0, 1). With this definition D t (0, 1) t [0, T ]. Cumulative emissions have drift µ e and we allow for uncertainty by adding a volatility term σ e. Then, for 0 t T, de t = µ e (A t, D t )dt + σ e d W 2 t, E 0 = 0.

Γ cap 0 π 0 Market with One Compliance Period Characterising the Allowance Price at t = T inital allocation of certificates monetary penalty {E T Γ cap } non-compliance event

Γ cap 0 π 0 Market with One Compliance Period Characterising the Allowance Price at t = T inital allocation of certificates monetary penalty {E T Γ cap } non-compliance event Terminal value of the allowance certificate is A T = πi {ET Γ cap}.

Γ cap 0 π 0 Market with One Compliance Period Characterising the Allowance Price at t = T inital allocation of certificates monetary penalty {E T Γ cap } non-compliance event Terminal value of the allowance certificate is A T = πi {ET Γ cap}. As a traded asset, A t is given by A t = e r(t t) πẽ [ ] I {ET Γ cap} F t, for 0 t T. Martingale Representation Theorem: d ( e rt A t ) = Z 1 t d W 1 t + Z 2 t d W 2 t, for 0 t T and some F t -adapted process (Z t ) := ( Zt 1, Zt 2 ).

Market with One Compliance Period FBSDE Formulation of the Pricing Problem Combining the processes for demand, cumulative emissions and the allowance certificate leads to the FBSDE dd t = µ d (t, D t )dt + σ d (D t )d W t 1, D 0 = d (0, 1), de t = µ e (A t, D t )dt + σ e (E t )d W t 2, E 0 = 0, da t = ra t dt + Zt 1 d W t 1 + Zt 2 d W t 2, A T = πi {ET Γ cap}. (D t, E t ) forward part (A t ) (Z t ) backward part generator

Market with One Compliance Period PDE Representation of the FBSDE Solution Let A t = α(t, D t, E t ), then α t +1 2 σ2 d (D) 2 α D 2 +1 2 σ2 e(e) 2 α E 2 +µ d(t, D) α D +µ e(α, D) α rα = 0, E with terminal condition α(t, D, E) = πi {E Γcap}, 0 D 1, E 0. The solution α also satisfies lim α(t, D, E) = E e r(t t) π, 0 t T, 0 D 1.

Market with One Compliance Period PDE Representation of the FBSDE Solution Let A t = α(t, D t, E t ), then α t +1 2 σ2 d (D) 2 α D 2 +1 2 σ2 e(e) 2 α E 2 +µ d(t, D) α D +µ e(α, D) α rα = 0, E with terminal condition α(t, D, E) = πi {E Γcap}, 0 D 1, E 0. The solution α also satisfies lim α(t, D, E) = E e r(t t) π, 0 t T, 0 D 1.

Allowance Certificate Price at t=0.5 T (market with one compliance period) Allowance Certificate Price at t=t (market with one compliance period) 100 100 Price (Euro/MWh) Price (Euro/MWh) 0 1 1 0 1 1 Cumulative Emissions (as a fraction of the initial allocation) 0 Demand (as a fraction of the market capacity) Cumulative Emissions (as a fraction of the initial allocation) 0 Demand (as a fraction of the market capacity)

Market with Multiple Compliance Periods Banking Banking: an additional incentive to reduce emissions. Banking 1 1 Γ cap E T 1 1 Γ E 1 2 cap T Γ 1 cap E 2 T 2 0 T 1 T 2 In the event of compliance, a number ( Γ 1 cap E 1) of certificates with price A 1 T 1 are exchanged for certificates valid during the next compliance period, with price A 2 T 1.

Market with Multiple Compliance Periods Withdrawal Withdrawal: additional punishment for excess emissions. Withdrawal 1 Γ cap E 1 T 1 1 Γ E 1 2 cap T Γ 1 cap E 2 T 2 0 T 1 T 2 In the event of non-compliance, a number min ( E 1 Γ 1 cap, Γ 2 cap) of certificates with price A 2 T 1 are subtracted from Γ 2 cap.

Market with Multiple Compliance Periods Banking and Withdrawal Aggregate supply of certificates during the second compliance period: (initial allocation) + (banked or withdrawn certificates). ˆΓ 2 cap = ( Γ 2 cap + Γ 1 cap E 1) +.

Market with Multiple Compliance Periods Banking and Withdrawal Aggregate supply of certificates during the second compliance period: (initial allocation) + (banked or withdrawn certificates). ˆΓ 2 cap = ( Γ 2 cap + Γ 1 cap E 1) +. The terminal condition for the allowance price becomes A 1 T 1 = A 2 T 1 I {ET1 <Γ 1 cap} + ( π 1 + A 2 T 1 ) I{Γ 1 cap E T1 <Γ 1 cap+γ 2 cap} + ( π 1 + π 2) I {ET1 Γ 1 cap+γ 2 cap}.

Allowance Certificate Price at t=t (market with one compliance period) Allowance Certificate Price at t=t1 (market with two compliance periods; banking and withdrawal) 100 Price (Euro/MWh) Price (Euro) 100 0 1 1 0 1 1 Cumulative Emissions (as a fraction of the initial allocation) 0 Demand (as a fraction of the market capacity) Cumulative Emissions (as a fraction of the initial allocation) 0 Demand (as a fraction of the market capacity)

Market with Multiple Compliance Periods Borrowing Borrowing: decreases the probability of non-compliance. Borrowing E 1 1 Γ T cap 1 1 1 Γ cap E T 1 1 Γ cap E 1 T 1 2 Γ cap E 2 T 2 0 T 1 T 2 If the emissions exceed the current compliance period s cap, a number min ( E 1 Γ 1 cap, Γ 2 cap) of certificates with price A 2 T1 are borrowed from Γ 2 cap.

Market with Multiple Compliance Periods Banking, Borrowing and Withdrawal Aggregate supply of certificates: as before. ˆΓ 2 cap = ( Γ 2 cap + Γ 1 cap E 1) +.

Market with Multiple Compliance Periods Banking, Borrowing and Withdrawal Aggregate supply of certificates: as before. ˆΓ 2 cap = ( Γ 2 cap + Γ 1 cap E 1) +. The terminal condition for the allowance price becomes A 1 T 1 = A 2 T 1 I {ET1 <Γ 1 cap +Γ2 cap} + ( π 1 + π 2) I {ET1 Γ 1 cap +Γ2 cap}.

Allowance Certificate Price at t=t1 (market with two compliance periods; banking and withdrawal) Allowance Certificate Price at t=t1 (market with two compliance periods; borrowing, banking and withdrawal) 100 Price (Euro) 100 Price (Euro) 0 1 1 0 1 1 Cumulative Emissions (as a fraction of the initial allocation) 0 Demand (as a fraction of the market capacity) Cumulative Emissions (as a fraction of the initial allocation) 0 Demand (as a fraction of the market capacity)

Option Pricing

The European Call on the Allowance Certificate Formulating the Pricing Problem Our example of choice is a European call (C t (τ)) t [0,τ] with maturity τ, where 0 τ T, and strike K 0. Its payoff is C τ (τ) := (A τ K) +.

The European Call on the Allowance Certificate Formulating the Pricing Problem Our example of choice is a European call (C t (τ)) t [0,τ] with maturity τ, where 0 τ T, and strike K 0. Its payoff is C τ (τ) := (A τ K) +. Require knowledge of A t need to solve problem for A t and for C t in parallel. The option price does not affect the rate at which firms emit expect the option pricing problem to be linear.

The European Call on the Allowance Certificate The Pricing PDE Letting C t = v(t, D t, E t ), v t +1 2 σ2 d(d) 2 v D 2 +1 2 σ2 e (E) 2 v E 2 +µ d(t, D) v D +µ e(α(t, D, E), D) v rv = 0, E with terminal condition Further, v satisfies v(t, D, E) = (A τ K) +, 0 D 1, E 0. ( ) + lim v(t, D, E) = E e r(t t) π e r(t τ) K, 0 t τ, 0 D 1.

The European Call on the Allowance Certificate The Pricing PDE Letting C t = v(t, D t, E t ), v t +1 2 σ2 d(d) 2 v D 2 +1 2 σ2 e (E) 2 v E 2 +µ d(t, D) v D +µ e(α(t, D, E), D) v rv = 0, E with terminal condition Further, v satisfies v(t, D, E) = (A τ K) +, 0 D 1, E 0. ( ) + lim v(t, D, E) = E e r(t t) π e r(t τ) K, 0 t τ, 0 D 1.

Call Option Price at t=τ 100 K Price (Euro) 0 1 1 Cumulative Emissions (as a fraction of the initial allocation) 0 Demand (as a fraction of the market capacity)

Work in Progress

Work in Progress Analysis of Emissions Uncertainty Characterise the allowance price A t := α(t, D t, E t ), as σ e 0 and the pricing problem develops an additional singularity.

Work in Progress Analysis of Emissions Uncertainty Characterise the allowance price A t := α(t, D t, E t ), as σ e 0 and the pricing problem develops an additional singularity. Solution of corresponding first order PDE α t + µ d(t, D) α D + µ e(α, D) α E rα = 0 with terminal condition α(t, D, E) = πi {E Γcap} exhibits a rarefaction wave. Characteristics meet in one line (T, D, Γ cap ).

Work in Progress Analysis of Emissions Uncertainty Characterise the allowance price A t := α(t, D t, E t ), as σ e 0 and the pricing problem develops an additional singularity. Solution of corresponding first order PDE α t + µ d(t, D) α D + µ e(α, D) α E rα = 0 with terminal condition α(t, D, E) = πi {E Γcap} exhibits a rarefaction wave. Characteristics meet in one line (T, D, Γ cap ). Expected result (c.f. Carmona et al, 2010): P(E T = Γ cap ) > 0 πi {ET >Γ cap} A t πi {ET Γ cap}.

A ɛ,δ t Work in Progress Asymptotic Analysis of Pricing Problem (c.f. Howison, Schwarz 2011) := α ɛ,δ (t, D ɛ t, E δ t ): value of the allowance price assuming that Demand is fast mean-reverting; The impact of load shifting is small.

A ɛ,δ t Work in Progress Asymptotic Analysis of Pricing Problem (c.f. Howison, Schwarz 2011) := α ɛ,δ (t, D ɛ t, E δ t ): value of the allowance price assuming that Demand is fast mean-reverting; The impact of load shifting is small. Formal expansion α ɛ,δ = α 0,0 + ɛα 1,0 + δα 0,1 + O(ɛ) + O(δ 2 ), we find that α 0,0 (t, E) satisfies α 0,0 t + 1 2 σ2 e 2 α ( 0,0 E 2 + µ BAU e ) α0,0 (D), Φ E rα 0,0 = 0, with terminal condition α 0,0 (T, E) := πi {E Γcap}.

A ɛ,δ t Work in Progress Asymptotic Analysis of Pricing Problem (c.f. Howison, Schwarz 2011) := α ɛ,δ (t, D ɛ t, E δ t ): value of the allowance price assuming that Demand is fast mean-reverting; The impact of load shifting is small. Formal expansion α ɛ,δ = α 0,0 + ɛα 1,0 + δα 0,1 + O(ɛ) + O(δ 2 ), we find that α 0,0 (t, E) satisfies α 0,0 t + 1 2 σ2 e 2 α ( 0,0 E 2 + µ BAU e ) α0,0 (D), Φ E rα 0,0 = 0, with terminal condition α 0,0 (T, E) := πi {E Γcap}. Explicit Solution: ( T E + t µ BAU e (D), Φ ) du α 0,0 (t, E) = πφ. σ e T t

Work in Progress Valuation of Power Plants in a Cap-and-Trade market (c.f. Carmona, Coulon, Schwarz, 2011) Let (S c t ) and (S g t ) denote the price processes of coal and gas. Extend the suggested model to a stochastic bid stack model, in which the electricity price P t is given by and the allowance price A t by P t := b(a t, D t, S c t, S g t ) A t := α(t, D t, E t, S c t, S g t ).

Work in Progress Valuation of Power Plants in a Cap-and-Trade market (c.f. Carmona, Coulon, Schwarz, 2011) Let (S c t ) and (S g t ) denote the price processes of coal and gas. Extend the suggested model to a stochastic bid stack model, in which the electricity price P t is given by and the allowance price A t by P t := b(a t, D t, S c t, S g t ) A t := α(t, D t, E t, S c t, S g t ). For maturities T 1, T 2,..., T n, closed form approximation to the price of spread options and hence value a gas plant v g, where v g (t, A t, D t, S c t, S g t ) := T n ( PT h g S g T e g A T ) + T =T 1 and h g, e g denote the heat and emissions rate of the plant under consideration.

Future Work Real Option Problem: When is it optimal to invest in a large abatement project, which changes the BAU emissions stack forever? Market Design: Analysis of alternatives to standard cap-and-trade schemes; e.g. allow the regulator to adjust the cap at specific times throughout the trading period.

Conclusion

Conclusion In this talk we showed: Bid stack contains information about active generators (in principal). Allowances can be regarded as derivatives on demand for polluting goods and cumulative emissions. Borrowing, banking and withdrawal can be analysed in multi-period setting. Options on allowances can be priced in this structural modelling framework.

Thank you for your attention. Questions?

600 b BAU ( ) The Bid Stack under BAU Price (Euro/MWh) 100 0 D 1 Supply (as a fraction of the market capacity)

1 The Emissions Stack under BAU e( ) Marginal Emissions (tco 2 /MWh) 0.4 0 D 1 Supply (as a fraction of the market capacity)

Market Bids, Low Carbon Cost Market Bids, High Carbon Cost Market Bids, Rearranged Price (Euro/MWh) Coal Gas Price (Euro/MWh) Coal Gas Price (Euro/MWh) Gas Coal Marginal Emissions (tco 2 /MWh) 0 Supply (MW) Emission Stack, Low Carbon Cost Coal Gas 0 Supply (MW) Marginal Emissions (tco 2 /MWh) 0 Supply (MW) Emission Stack, High Carbon Cost Coal Gas 0 Supply (MW) Marginal Emissions (tco 2 /MWh) 0 Supply (MW) Emission Stack, Rearranged Gas Coal 0 Supply (MW)