Price Discovery and Information Linkages in the Emission Allowance and Energy Markets John ...

156 Pages·2013·1.43 MB·English

Price Discovery and Information Linkages in the Emission
Allowance and Energy Markets

John Edward Swieringa
February 2013

A thesis submitted for the degree of Doctor of Philosophy of The Australian National University

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Declaration

I hereby certify that this thesis is entirely the
work of the author and has not been submitted
to any other institution. Furthermore, all
sources used in the preparation of the thesis
have been acknowledged in the usual manner.

………………………………………...............
John Swieringa
15 February 2013

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Acknowledgements

I would like to thank my supervisors Emma Schultz and Tom Smith. Emma was an indefatigable
reader of drafts whose ruthless efficiency with a red pen was invaluable. She was the best sounding
board a PhD student could ask for and I am very grateful for her opinions, her judgement and her
enthusiasm. Tom provided key direction to the research, using his vast depth of experience and
knowledge to point out relevant literature and empirical techniques. I would also like to thank
Raymond Liu and Carole Comerton-Forde for insightful comments on drafts of the first chapter.

PhD students are always indebted to those who put up with them during their struggles and in that
regard I thank Gaurav Khemka, with whom I share an office and revelled in our daily coffee and
darts sessions. I thank my parents for their encouragement and for bravely attempting to read my
work. Most importantly, I would like to thank my wife Jess, who went through with marrying me in
the depths of this undertaking. Her love and support made this work possible.

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Abstract
We provide the first evidence on the catalysts for price discovery in the European Union Emissions
Trading System. Short-run return dynamics are analysed using a regression approach similar to
Fleming, Ostdiek and Whaley (1996), while the permanent contribution of securities to long-run
price equilibrium is examined by calculating Hasbrouck‘s (1995) information shares. By employing
high frequency data across a wide range of securities, we find that trading costs are a more
important determinant of price discovery than the implicit provision of leverage in securities such as
futures and options. Securities with low trading costs display greater price discovery than those with
high trading costs.
We also examine price discovery within the European markets for coal, natural gas and crude
oil. Results show that Brent crude oil futures display greater price discovery than a proxy for the
physical Brent market, while there is evidence that West Texas Intermediate futures still dominate
price discovery globally. In natural gas markets, UK natural gas futures display greater price
discovery than physical trading at North-West Europe‘s main natural gas hubs, though weak links
to the crude oil market remain. Due to a lack of liquidity and transparency, it remains difficult to
distinguish between coal securities. Overall, our results support the importance of futures contracts
as a source of price discovery in contrast with opaque over-the-counter physical trading.
Having established where price discovery is taking place in the European emission allowance
and energy markets, we examine volatility and information linkages between them by employing a
rational expectations framework similar to Fleming, Kirby and Ostdiek (1998). The model specifies
volatility linkages operating through common information and information spillover channels. We
estimate a representation of this model using GMM for bivariate pairings of emission allowances
with coal, natural gas and crude oil. We find that emission allowances are most strongly linked to
the crude oil market, in spite of more direct economic relationships with coal and natural gas.
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Table of Contents

CHAPTER 1: INTRODUCTION ........................................................................................................................ 1
CHAPTER 2: PRICE DISCOVERY IN THE EUROPEAN UNION EMISSIONS TRADING SYSTEM ............................ 4
2.1 A BRIEF INTRODUCTION TO THE EU ETS ............................................................................................................. 8
2.1.1 Emission Allowances .......................................................................................................................... 8
2.1.2 Phases .............................................................................................................................................. 11
2.2 DATA ......................................................................................................................................................... 14
2.2.1 Series Selection ................................................................................................................................ 15
2.2.2 Measures of Trading Cost ................................................................................................................ 20
2.2.3 Return Series Construction .............................................................................................................. 21
2.2.4 Serial Correlation ............................................................................................................................. 23
2.2.5 Stationarity and Cointegration ........................................................................................................ 27
2.3 METHODOLOGY ........................................................................................................................................... 30
2.3.1 Basic Regression Specification ......................................................................................................... 30
2.3.2 Cointegration and Error Correction ................................................................................................. 33
2.3.3 The Final Model Specification .......................................................................................................... 35
2.3.4 Information Shares .......................................................................................................................... 36
2.4 RESULTS ..................................................................................................................................................... 39
2.4.1 Regression Results ........................................................................................................................... 39
2.4.2 Regression R-Squared and F-Statistics ............................................................................................. 44
2.4.3 Ordinal Ranking ............................................................................................................................... 48
2.4.4 Information Shares .......................................................................................................................... 53
2.4.5 Strength of Findings ......................................................................................................................... 57
2.5 CONCLUSION ............................................................................................................................................... 58
CHAPTER 3: PRICE DISCOVERY IN EUROPEAN ENERGY MARKETS ............................................................... 60
3.1 METHODOLOGY ........................................................................................................................................... 64
3.1.1 Regression Approach ....................................................................................................................... 64
3.1.2 Information Shares .......................................................................................................................... 65
3.2 COAL ......................................................................................................................................................... 67
3.3 NATURAL GAS ............................................................................................................................................. 73
3.4 CRUDE OIL.................................................................................................................................................. 82
3.4.1 Price Discovery in the Brent Crude Oil Complex ............................................................................... 82
3.4.2 Price Discovery in Brent and WTI Futures ........................................................................................ 88
3.5 CONCLUSION ............................................................................................................................................. 100
3.6 APPENDIX ................................................................................................................................................. 102
CHAPTER 4: INFORMATION LINKAGES BETWEEN THE EMISSION ALLOWANCE AND ENERGY MARKETS ... 108
4.1 EXISTING EVIDENCE ON MARKET INTERACTIONS ............................................................................................... 110
4.2 INFORMATION LINKAGES ............................................................................................................................. 112
4.3 METHODOLOGY ......................................................................................................................................... 117
4.3.1 Directionality of Emission Allowance and Energy Market Relationships ....................................... 117
4.3.2 Stochastic Volatility Model ............................................................................................................ 117
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4.4 DATA ....................................................................................................................................................... 121
4.4.1 Security Selection ........................................................................................................................... 121
4.4.2 Descriptive Statistics ...................................................................................................................... 123
4.4.3 Serial Correlation ........................................................................................................................... 125
4.4.4 Cross-Market Correlations ............................................................................................................. 126
4.5 RESULTS ................................................................................................................................................... 127
4.5.1 Regression Results ......................................................................................................................... 128
4.5.2 Information Linkages ..................................................................................................................... 128
4.6 CONCLUSION ............................................................................................................................................. 135
4.7 APPENDIX ................................................................................................................................................. 137
CHAPTER 5: CONCLUSION ........................................................................................................................ 143
REFERENCES ............................................................................................................................................. 146

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CHAPTER 1: Introduction

There is widespread agreement that anthropogenic global warming is the result of long standing
market failures where, in the absence of defined property rights, firms combusting fossil fuels have
no incentive to restrict their resultant emissions of carbon dioxide and other greenhouse gases (see,
for example, Stern, 2006). Emissions trading is an attempt to redress this market failure along the
lines first suggested by Coase (1960) insofar as efficient outcomes are achieved by allocating
property rights and facilitating trade between the parties affected by an externality. Under a cap and
trade system, polluters who emit more than their allocation of carbon dioxide emissions must
purchase allowances from other market participants who emit less or pay significant penalties.
While scarcity of emission allowances encourages polluters who can abate their emissions at low
cost to do so and profit by selling their excess allowances, scarcity may also force polluters who
cannot afford to purchase allowances or abate their emissions to cease production altogether.
Emissions trading was first undertaken on a significant scale in the United States in the mid-
1990s to reduce acid rain. While the European Union Emissions Trading System (EU ETS) was
introduced a decade later, only being launched in 2005, it is many times larger in size. The EU ETS
places a cap on the total amount of carbon dioxide emissions Europe‘s large polluters are allowed to
emit each year, with emission allowances allocated or auctioned to polluters by Europe‘s
governments. The cap is reduced each year such that the right to pollute becomes increasingly
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scarce over time. Emission allowances in the EU ETS are traded over-the-counter between
polluters, sometimes with the involvement of financial institutions, as well as on organised
exchanges that facilitate spot, futures and options trading. Given its importance in mitigating the
effects of global warming, understanding the dynamics of this relatively new emission allowance
market, and the energy markets more broadly, is paramount for policy makers, market participants
and academics alike. We contribute to the literature in this regard by assessing price discovery in
both the EU ETS and the main fossil fuel energy markets as well as information linkages between
the two.
Specifically, in Chapter 2 we examine price discovery in the main spot and futures securities
traded in the EU ETS. In doing so, we provide the first investigation into the catalysts for price
discovery in this market, paying particular attention to trading costs and leverage. We also consider
whether market segmentation between emission allowances created within the EU ETS or created
under the auspices of the United Nations impacts upon price discovery. Consistent with evidence on
price discovery in other markets, we find trading costs are more important than leverage or a
security‘s origin.
In Chapter 3, we broaden our focus to examine both short and long-run price discovery in the
main European fossil fuel energy markets, namely coal, natural gas and crude oil. Prior research
into the nature of price discovery in these energy commodities has been relatively sparse. These
markets are characterised by the operation of price reporting agencies who survey physical market
participants in order to construct benchmark prices. These prices then form the basis for long-term
supply contracts, which remain the most prevalent mode of exchange for all three commodities.
However, because these survey processes are often opaque and physical markets commonly suffer
liquidity problems, the financial layers of these markets, particularly futures trading, play an
important role in price discovery. We attempt to span both the physical and financial layers of these
markets and, to the best of our knowledge, we provide the first assessment of price discovery in the
European coal market and the broadest study of price discovery for European natural gas.
Thereafter, we make the first attempt to assess both the physical and financial layers of the Brent
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market. Finally, in light of recent market dislocations, we assess linkages between the European
crude oil and natural gas markets and re-examine linkages between the major global crude oil
benchmarks, namely Brent and West Texas Intermediate. Overall, our results support the
importance of futures contracts as a source of price discovery in contrast with opaque over-the-
counter physical trading.
Having established price discovery both in the emission allowance and energy markets, we
analyse channels of interaction between them. Specifically, in Chapter 4 we employ a rational
expectations framework in the tradition of Tauchen and Pitts (1983), Fleming, Kirby and Ostdiek
(1998) and Kodres and Pritsker (2002) to analyse market interactions on the basis of responses to
commonly relevant information and the spillover effects of idiosyncratic information. By doing this,
we take account of complexities that have previously been overlooked, including the true impact of
operational and strategic considerations in the generation of electricity and the limitations imposed
by the current power generation mix in a given economy. These factors inhibit the extent to which
fuel switching is likely to be an observable short-term phenomena, let alone one that dictates
directional interactions between fuel input and emission allowance prices. We estimate a stochastic
volatility representation of this model and, in the absence of a priori expectations concerning the
directional relationships between the markets of interest, we assess information linkages using the
correlation of volatilities. Our results show that, despite the strong economic linkages between
emission allowances and coal and natural gas, emission allowances have the strongest information
linkages to the crude oil market, which is likely a product of strong common information linkages.
Our findings highlight the importance of information arrival in our understanding of markets
generally and, for the emission allowance and energy markets in particular, it is a reminder that
their interactions will be strongly influenced by information that is commonly relevant as they share
many fundamental determinants of value.
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CHAPTER 2: Price Discovery in the European Union
Emissions Trading System

The EU ETS is a decentralised market place that includes over-the-counter trading of emission
allowances as well as spot, futures and option trading on nearly a dozen organised exchanges. With
such a wide dispersion of tradable securities and trading venues, identifying the security price most
reflective of current, relevant information—the source of price discovery—is important for market
participants and regulators alike. The large number of securities that are all essentially fungible with
one another also provides a good opportunity to examine which market frictions are important
drivers of price discovery, such as trading costs or leverage. Against this backdrop, we analyse
intraday return data for seven of the most traded securities in the EU ETS to establish which
security‘s returns have a greater tendency to lead the returns of the others. Thereafter, we look at
whether the relationships between securities are consistent with trading costs or the implicit
leverage in futures and options determining price discovery. In addition, we look at whether the
government allocated or auctioned European Union Allowances are better sources of price
discovery compared with external, project-based allowances such as the Certified Emission
Reduction units created under the auspices of the United Nations Clean Development Mechanism.
Price discovery in the EU ETS is a topic that has received attention in previous literature, but
prior studies rarely utilise high frequency data nor do they examine the underlying catalysts for
price discovery. As it is a relatively new market, much of this literature has concentrated on the
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