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ECONOMIC ANALYSES OF WORLD’S CARBON MARKETS
By
Tajinder Pal Singh Bhatia
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Faculty of Forestry and Centre for Environment University of Toronto
3.1 Data description for the price series of carbon markets in EU, USA and Canada for both Allowance and CER based assets (credits)…………….. 26
3.2 Results of ADF and Phillips-Perron unit root tests for carbon prices in the markets of EU, USA and Canada…………………………………… 28
3.3 Results of DF-GLS and Zivot-Andrews unit-root tests for carbon prices in the markets of EU, USA and Canada………………………………… 29
3.4 Results of Johansen’s multivariate co-integration tests for the carbon markets of EU, USA and Canada for both Allowance and CER based credits of EUA, CCXA, RGGIA, MONTLA, EUCER, and CCXCER (using 3 lags)….. 30
3.5 Results of Johansen’s multivariate co-integration tests for carbon markets of EU, USA and Canada for both Allowance and CER based credits of EUA, CCXA, RGGIA, MONTLA, EUCER, and CCXCER, taking five assets at a time (using 3 lags)…………………………………………………… 31
3.6 Results of Johansen’s multivariate co-integration tests for carbon markets of EU, USA and Canada for both Allowance and CER based credits of EUA, CCXA, RGGIA, MONTLA, EUCER, and CCXCER, taking four assets at a time (using 3 lags)…………………………………………… 31
3.7 Results of Johansen’s multivariate co-integration tests for only Allowance-based credits of CCXA, RGGIA and MONTLA in North America only (using 3 lags)……………………………………………… 33
3.8 Results of Johansen’s multivariate co-integration tests for carbon markets of EU and North America for CER-based credits EUCER and CCXCER (using 3 lags)…………………………………………………… 34
4.1 Summary statistics of the volatility series and tests for non-stationarity for ECX and CCX………………………………………………………... 54
4.2 Results of DF-GLS and Zivot-Andrews unit-root tests for carbon prices in ECX and CCX………………………………………………………… 55
4.3 Results for performance of econometric models for ECX Options Market……………………………………………………………………. 56
4.4 Results for performance of econometric models for ECX Futures Market 57
4.5 Results for performance of econometric models for CCX, Spot Market... 58
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5.1 Statistics for log returns for price in ECX and CCX…………………….. 80
5.2 Summary statistics for log returns of prices of actual and simulated markets…………………………………………………………………… 81
5.3 Statistics for the log returns for the price series of the simulated carbon markets of ECX and CCX……………………………………………….. 84
5.4 Results of experiments on artificial carbon markets for different wealth distribution of agents…………………………………………………….. 89
5.5 Results of experiments for changing the proportion of carbon assets…… 90
5.6 Results of experiments for changing N in ECX…………………………. 91
5.7 Results of experiments for changing N in CCX…………………………. 92
5.8 Results of forecasting ECX and CCX with GARCH (1, 1) and ABM...… 93
5.9 Results of forecasting CCX spot market with NL-GARCH and ABM…. 94
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List of Figures
Figure Page
3.1 Allowance and CER asset prices in carbon markets of EU and North America………………………………………………... 27
3.2 Graph of structural stability tests using Eigenvalue stability condition in VEC model for Allowance-based assets CCXA, RGGIA and MONTLA in North America…………………….. 33
3.3 Graph of structural stability tests using Eigenvalue stability condition in VEC model for CER-based assets EUCER, and CCXCER in EU and North America…………………………… 35
4.1 Price in ECX Options Market, Oct. 2006 to Jul. 2009……….. 52
4.2 Volatility in ECX Options Market, Oct. 2006 to Jul. 2009…... 52
4.3 Price in ECX Futures Market, Mar. 2006 to Jul. 2009……….. 52
4.4 Volatility in ECX Futures Market, Mar. 2006 to Jul. 2009…... 52
4.5 Price in CCX, Dec. 2003 to Jul. 2009………………………… 53
4.6 Volatility in CCX, Dec. 2003 to Jul. 2009…………………… 53
5.1 Cycle of an agent based model in Adaptive Modeler………… 73
(1989), Pagan and Schwert (1990), Benz and Trück (2009) and Chapter 4 of this thesis
indicate that in financial markets and carbon markets, most of the price series are
described most accurately by GARCH types of models. In this section, a comparison is
also made for forecasting performance of GARCH type of models with agent based
models. The rationale for using these parameters for experimentation is explained first.
5.7.1 Wealth distribution of agents (W)
In both the carbon markets of Europe and Chicago, there is an involvement of agents of
diverse backgrounds. In Europe, emission reduction targets are fixed by the regulator,
whereas in Chicago, the involvement of agents is purely on voluntary basis. Even though
the nature of EU and Chicago market is different in this respect, there is one common
factor: agents with different wealth levels are participating in both of them. Therefore it
appears reasonable to see how forecasting performance of the model changes when we
change the wealth distribution of the agents in the artificially simulated markets. For
experimentation in this chapter, initial wealth is assigned to the agents by the following
methods:
(a). Equal for all agents: All agents are assigned the same initial wealth. Though it is not
realistic to expect equal wealth for all agents in the real market, it could be a good
starting point for simulation exercise.
(b). Pareto distribution: Here, the agent wealth is randomly sampled from a Pareto
distribution, which is a well known power law distribution commonly used to describe
wealth or income distributions. In particular it describes an unequal distribution where a
large part of total wealth is owned by a small percentage of individuals, generally in the
80-20 ratio. Pareto distribution has been used considerably in the financial market
literature, for example by Reed (2010) to describe wealth of a population of agents; and
hence is used in experimentation for the purpose of this study also.
(c). Maxwell-Boltzmann distribution: Agent wealth can also be randomly sampled from a
Maxwell-Boltzmann distribution. This is an exponential distribution originating from the
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field of statistical mechanics for describing the distribution of energy of atoms in a gas. In
econo-physics, for example, by Mantegna and Stanley (2000), its general significance has
been recognized for describing the distribution of money among agents in an economy.
Although money may be considered conserved in a closed economy, wealth, when it
includes non-cash assets, is not necessarily conserved. Wealth may change due to
changes in asset prices or through the creation and destruction of assets. For example, in
carbon markets, agents may keep selling, buying or retiring carbon credits, depending
upon their emission reduction commitments, production enhancement motives or social
and environmental obligations. Maxwell-Boltzmann distribution is the most random
distribution among all of these distributions (Laurendeau 2003).
5.7.2 Proportion of carbon allowances vis-à-vis total wealth (P)
In EU countries, due to fixing of targets under Kyoto Protocol, CO2 allowance
distribution is done by the government to different emitters. The allowances are either
allotted or auctioned initially by the government regulator within the overall cap on
emissions. Subsequently, if any company wishes to emit more, it has to buy allowances
from the market. Similarly those companies that have reduced their targets below the
fixed levels can sell the surplus allowances. However in USA, there is no government
regulation for the emission targets. So it is completely up to the emitters to buy or sell
carbon allowances in the market (Point Carbon 2009). This makes the nature of EU and
Chicago markets different from each other. Therefore it is expected for the model to
perform in a different way for changing the proportion of carbon assets among different
agents. Therefore an experiment is performed to compare the forecasting accuracy of the
agent based model by varying initial proportion of carbon assets in these markets by two
distributions: (a). Equal: When all agents get the same initial allotment of carbon assets.
The position can be specified initially and is chosen to vary within [-100%, 100%]; and
(b). Gaussian: Where an agent’s position is randomly sampled from a Gaussian (Normal)
distribution with the specified mean and standard deviation. The mean is chosen to vary
within [-100%, 100%].
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5.7.3 Number of agents (N)
Carbon markets are expanding day by day. With more and more countries fixing
emission reduction targets and the growing environmental consciousness, it is expected
that more carbon markets will be established around the world. Already the markets have
become operational in China, Japan, Australia and various other countries (World Bank,
2009). As a result, it is expected that the number of agents will also go on increasing in
the times to come. Therefore it seems reasonable to carry out experiments to see the
forecasting performance of an agent based model with change in the number of agents N.
This experiment is performed with N equal to 5000, 25000, 50000 and 100000 and
change in forecasting accuracy is noticed.
Following Yu (2002), comparison of artificially simulated markets with real carbon
markets is made using the two evaluation measures of Root Mean Square Error (RMSE)
and Mean Absolute Error (MAE) to see the forecasting accuracy. These evaluation
measures are defined as
RMSE = ∑=
−I
i
ii ppI 1
2)ˆ(1
(5.13)
MAE = ∑=
−I
i
ii ppI 1
ˆ1
(5.14)
Where, ip is the forecasted price, ip is the carbon price from actual data on ith day and I
the number of days of trading.
5.8 Results and discussion of Experiments
The results of experiments show very striking findings in the use of agent based models
for forecasting in carbon markets. One common finding in these experiments is that both
the indicators perform consistently for the two markets and for different parameters, as
shall be clear from the subsequent discussion.
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5.8.1 Changing W
The results of experiments, by changing wealth of agents, performed on the simulated
markets of European Climate Exchange (ECX) and the Chicago Climate Exchange
(CCX) are given in Table 5.4. From these results, it is evident that forecasting accuracy
using both the performance indicators improve continuously as the initial distribution of
wealth is changed from Equal to Pareto and then to Maxwell-Boltzmann for both ECX
and CCX. The numbers in parenthesis indicate the rank in forecasting accuracy.
Table 5.4. Results of experiments on artificial carbon markets for different wealth
distribution of agents
ECX CCX
Initial wealth distribution
Equal Pareto Maxwell-Boltzmann
Equal Pareto Maxwell-Boltzmann
RMSE 0.092
(3)
0.057
(2)
0.013
(1)
0.086
(3)
0.065
(2)
0.032
(1)
MAE 0.075
(3)
0.026
(2)
0.003
(1)
0.067
(3)
0.035
(2)
0.015
(1)
Figures in parentheses indicate ranking of forecasting accuracy
The results obtained in Table 5.4 indicate that for forecasting both compliance and
voluntary carbon markets, maximum accuracy of forecast is obtained, when we allot
initial wealth among agents according to Maxwell-Boltzmann distribution. This result
seems quite intuitive, as investment agents in both types of carbon markets are expected
to possess any amount of wealth, which is also a reflection of the size of their respective
firms. Taking wealth distribution as equal or Pareto introduces forecasting error, as
behavior of real market agents is not followed completely by the artificial agents in the
learning process or formation of trading rules.
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5.8.2 Changing P
The results of experiments by changing proportion of carbon assets of agents, performed
on the simulated markets, are given in Table 5.5. The numbers in parenthesis indicate the
rank in forecasting accuracy. From these results, it can be observed that both the
evaluation measures show better forecasting accuracy by the model when we consider
equal distribution for proportion of allowances for the compliance market of ECX.
However for the voluntary market of CCX, the performance using both the measures is
better for Gaussian distribution. In compliance markets, all the agents get equally
proportioned allotment of allowances by the regulator, depending upon their level of
emissions in the beginning of commitment period. So by taking equal distribution in
model, the performance is expected to be better, which is true in this case. However, in
Chicago market, there is no allotment of allowances by the regulator and the companies,
investors or the agents are trading purely on voluntarily basis and it cannot be intuitively
expected from all the agents to buy or sell an equal proportion of allowances. Gaussian or
the normal distribution seems to be most appropriate in this case and the results of these
experiments also confirm the same.
Table 5.5. Results of experiments for changing the proportion of
carbon assets
ECX CCX
Proportion of
carbon
allowances
Equal Gaussian Equal Gaussian
RMSE 0.032
(1)
0.089
(2)
0.064
(2)
0.029
(1)
MAE 0.013
(1)
0.056
(2)
0.073
(2)
0.038
(1)
Figures in parentheses indicate ranking of forecasting accuracy
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5.8.3 Changing N
The results of experiments performed for number of agents N are given in Table 5.6 for
the simulated market of European Climate Exchange (ECX) and in Table 5.7 for the
simulated market of Chicago Climate Exchange (CCX). Keeping in view the results of
first two experiments, initial wealth distribution is kept as Maxwell-Boltzmann for both
the markets; and allowance proportion is kept as equal for the artificial market of ECX
and Gaussian for the artificial market of CCX.
From Table 5.6 relating to artificial carbon market of ECX, it is apparent that forecasting
performance using both the measures of accuracy improves continuously with the
increase in number of agents. As EU carbon market is in practice since 2003 and due to
fixation of emission reduction targets and the stringent implementation of rules in
Europe, the number of agents have increased to a considerably high level. As a result,
forecasting accuracy of agent based model is also the highest, when we consider their
number N as 100000. This is a positive indication for forecasting of compliance carbon
market for future also, as more and more agents are getting involved in the process of
carbon trading.
From table 5.7, for Chicago carbon market, the results depict that forecasting accuracy
improves first, as N is increased from 5000 to 25000; however, the same decreases
continuously as we further increase N to 50000 and finally to 100000. These results
again appear to mimic reality of Chicago market, which is a voluntary market so far and
Table 5.6. Results of experiments for changing N in ECX
N=5000 N=25000 N=50000 N=100000
RMSE 0.097
(4)
0.068
(3)
0.013
(2)
0.002
(1)
MAE 0.059
(4)
0.037
(3)
0.008
(2)
0.004
(1)
Figures in parentheses indicate ranking of forecasting accuracy
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number of participants, who make investment in the same, is neither too small nor too
big.
Table 5.7. Results of experiments for changing N in CCX
N=5000 N=25000 N=50000 N=100000
RMSE 0.085
(3)
0.027
(1)
0.054
(2)
0.098
(4)
MAE 0.062
(2)
0.043
(1)
0.067
(3)
0.120
(4)
Figures in parentheses indicate ranking of forecasting accuracy
5.8.4 Comparison with analytical models
Preceding discussion focused on the capabilities of agent based models in forecasting
carbon markets and experiments to improve accuracy of results. However, from an
investor’s point of view, it would be advisable to draw a comparison between the agent
based models and analytical models for forecasting purposes. In Chapter 4, it was proved
that GARCH (1, 1) and non-linear GARCH models give maximum accuracy for
forecasting the spot markets of ECX and CCX, respectively. Hence in this section, a
comparison of agent based model and the respective analytical model is made for both
the markets.
First, the forecasting of ECX spot market is done with GARCH (1, 1) model given.
GARCH (p, q) process is defined as
tttr εσ= ; ∑∑ = −
=
−− ++=Ω≡p
j jtj
q
i
itittt uuE1
2
1
2
01
22 )( σδαασ (5.15)
where 1⟨+ δα and δj > 0 for all j=0,1,….p.
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Forecasting accuracy is derived using both RMSE and MAE and the results are given in
the first column of Table 5.8. In second column the forecasting results from agent based
model in Table 5.4 are reproduced for comparison.
Table 5.8. Results of forecasting ECX spot market with GARCH
(1, 1) and ABM
GARCH (1, 1) Agent based model
RMSE 0.378 0.092 (4.1)*
MAE 0.213 0.075 (2.8)*
* indicates the number of times ABM is more accurate than GARCH (1, 1)
A comparison of the values in the two columns of Table 5.8, indicate that forecasting
accuracy of agent based model is higher than the corresponding analytical model for
ECX spot market.
Second, the forecasting of CCX spot market is done with non-linear GARCH model. N-
GARCH (1, 1), which is given as:
tttr εσ= ; 2
11
2
111
2
1101
22 )()( −−−− +++=Ω≡ tttttt HuuE σδσβαασ (5.16)
Where α1>0 and H1 is increasing function. Forecasting accuracy is again derived using
both RMSE and MAE and the results are given in the first column of Table 5.9. In second
column the forecasting results from agent based model for CCX in Table 5.4 are
reproduced for comparison.
A comparison of the values in the two columns of Table 5.8, indicate that forecasting
accuracy of agent based model is higher than the corresponding analytical model for
ECX spot market also.
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Table 5.9. Results of forecasting CCX spot market with NL-
GARCH and ABM
NL GARCH model Agent based model
RMSE 0.575 0.086 (6.7)*
MAE 0.238 0.067 (3.5)*
* indicates the number of times ABM is more accurate than NL-GARCH
From both the tables 5.8 and 5.9, it is clear agent-based models have got more potential
for forecasting purposes than mathematical models used in finance. In addition, there is
no scope of improving forecasting accuracy in traditional econometric models. However,
in agent based models, by varying the model parameters and agents’ characteristics by
way of experimentation, forecasting accuracy can be considerably increased; which
means that artificial carbon markets simulated by means of agent based models can be
made to mimic real world markets as close as possible.
5.9 Summary and conclusions
One of the major challenges for carbon market investors is to understand and forecast the
prices of carbon assets. However due to involvement of various heterogeneous agents in
the market mechanism, standard analytical models do not seem to provide reliable
forecasting tools. A solution to this problem is found in the world of agent based models.
A genetically-programd agent based model is found to satisfy the stylized facts of the
compliance market of European Climate Exchange (ECX) and the voluntary carbon
market of Chicago Climate Exchange (CCX). Further, the forecasting capabilities of
agent based models are found to be considerably better than the analytical models. This
could be due to the fact that behavior of heterogeneous trading agents in incorporated
well in these models, which is not possible in the traditional mathematical models. In
traditional models, there is no scope of improving forecast accuracy. However, in agent
based models, it is considerably improved by changing model parameters and agent
characteristics by way of experimentation. Three model parameters are changed: wealth
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distribution of agents; distribution of carbon assets as proportion of their total wealth; and
the number of agents in the artificially generated markets of ECX and CCX. Forecast
accuracy is found to increase considerably, when the parameter distributions or values are
close to the real market situations.
These results indicate that agent based models provide a very promising tool to
understand the price dynamics in carbon markets and obtaining high accuracy forecasts
as compared with the traditional econometric models. This study further opens doors to
many research directions. Carbon markets are a recent phenomenon and more and more
carbon markets are coming up in all parts of the world. One obvious extension of this
work is to apply agent based model to other markets for forecasting as the new data
becomes available from different parts of the world. Some of the most challenging
research concerns microstructure of carbon markets (Madhavan 2000), which could be
studied within artificial carbon markets. Going further, different groups of carbon market
participants like electric utilities, forest owners and transporters etc. can be clubbed
together and inter-group dynamics of market can be explored. In addition, the behavior of
fundamental trading agents can also be incorporated using the data of marginal abatement
cost from various industries. In addition, the experiments in this study have focused so far
on three parameters only, keeping others fixed. Future work may also include variations
in other parameters also, so as to have a bigger picture on the market behavior in carbon
trading. Nonetheless, the experiments carried out in this chapter being of pioneering
nature can work as a starting platform for further research in the use of agent based
models in understanding and forecasting of carbon markets in the times to come. Apart
from these issues, only price variable has been included in this chapter due to data and
software limitations. With advancements in the available softwares in the near future,
volume variable can also be included. Similarly efforts could be directed towards
incorporating policy level decisions in the model to incorporate strategic variables in the
model. Quoting LeBaron (2000), “this field is only in its infancy and much remains to be
done”, further improvements could be made in computation capabilities also by making
use of high level programming codes in high level languages, for example, Java and C++,
to have a better picture about use of agent based models in CO2 emission trading markets.
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6 Conclusions, Policy Implications, Limitations and Future Work
6.1 Conclusions
Forests play a major role in setting the CO2 levels in the atmosphere through carbon
sequestration. Carbon markets are one of the most innovative and cost-effective means of
creating a market pull for forestry credits generated through afforestation and
conservation activities. Due to increase in quantum of carbon trading over last few years,
various issues related to price dynamics of carbon markets have arisen in the recent past.
First, carbon markets have emerged at regional, national and international levels and are
governed by specific demand and supply patterns. There is a need to unify these markets
to increase overall carbon market efficiency. Second issue relates to understanding of
short-run volatility of carbon markets, as forecasts of carbon price volatility could be
important inputs into macro-econometric models and market risk assessment calculations
like value at risk and; for the choice of a carbon policy instrument. Third, carbon markets
consist of various heterogeneous agents such as greenhouse gas emitters, agriculturists,
foresters and individual market investors. These agents interact with each other and with
the overall trading-environment to evolve the emergent behavior of these markets.
Traditional approach for study of price dynamics in such markets is through use of
analytical models that assume completely rational agents. This causes biased results and
lesser forecast accuracy. To improve forecast accuracy, there is a need to carry research
on incorporating agent heterogeneity and limited rational behavior.
This research is therefore carried out in the form of three essays and economic analyses
are conducted on world’s carbon markets to examine these issues. Both compliance
markets of EU and voluntary market of North America have been covered in analyses.
The first essay addresses long-run integration of carbon markets at the interregional level
using Johansen full information maximum likelihood procedure for testing co-integration.
The second essay evaluates the performance of various econometric models for
predicting price volatility of carbon in different markets. In the third essay, an agent
based model of carbon markets is analyzed and the statistical properties of the artificially
simulated carbon markets are explored. The agents are sophisticated genetic
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programming based computer programs that co-evolve with learning by predicting
investment opportunities in the market using technical analysis as the main tool.
Experiments are performed on endogenous artificial markets to improve forecasting
accuracy of the model. The major conclusions of these analyses are:
First, the allowance-based carbon markets across North America and EU are not
cointegrated. A possible cause could be the fact that in EU countries, carbon market
investments take place on account of compliance under Kyoto targets; whereas in North
America, investors are trading in carbon markets purely on account of their
environmental responsible behavior or for anticipated fixation of targets in future. In
addition, international protocols like the Marrakesh Accords require that for compliance
purposes in EU, carbon credits could be used only from the Kyoto signatory nations,
which excludes the US based carbon markets. Due to lack of cointegration between EU
and North American carbon markets, an overall inefficiency is introduced in the system
and emissions cannot be reduced at the least possible cost.
Second, allowance-based markets within North America are cointegrated with each other.
This implies a common stochastic trend in the North American carbon market and could
be the result of spatial proximity and uniform regulatory mechanism so far. This common
trend could bind all these markets together and forecasting of prices in any of these
markets can be enhanced significantly by utilizing information from the prices of another
one. The traders, on account of perfect information, can gain potentially through
equalizing transaction costs in the unified North American carbon market. However,
recently, within North-American carbon markets, there is an indication of switching of
some buyers, specifically Canadian buyers, from the US markets to the Canadian market
due to establishment of Montreal exchange, as the firms now have an option to trade in
either of them. In the long-run equilibrium, if more firms trade in the US markets, leading
to increase in prices in the US markets, the Canadian market will face less demand and
therefore prices will decrease or vice-versa.
Third, CDM project based CER markets in Europe and USA are cointegrated because of
the fact that these credits are generated primarily in the developing countries and
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irrespective of the trading countries; their prices show co-movement around the world.
This indicates efficiency and existence of a global carbon market for CDM projects.
Looking at the specifics, the types of projects eligible for generating CERs in ECX and in
CCX are not similar. As a result, the situation market might change in future, if attempts
are not made either to link or to make both the markets homogenous with respect to type
of projects. Nonetheless, the development of global carbon market for CDM can be seen
as one manifestation of improved expectations for ensuring carbon sequestration and
sustainable development in the developing countries.
Fourth, for future, it is expected that new carbon markets coming up in different parts of
the world such as Australia, China, India and other countries, might not be co-integrated.
Such phenomenon could be due to different compliance requirements for different
countries and regions under international protocols, different sets of institutional
arrangements at the local levels, different expectation levels for environmental
sustainability and also due to restrictions on participation of buyers from other countries
and regions. The possibilities of arbitrage across the global markets will hence be limited,
and the carbon trading in these markets are expected to be globally inefficient in future.
Therefore, there is a strong need of a global agreement that allows global carbon trade to
prevent climate change at the least cost options.
Fifth, voluntary market of Chicago is more volatile than the compliance market of EU.
However, despite the various policy level changes, the time-series data for short term
volatility in both the markets follow a stationary pattern and hence volatility can be
forecasted for both of them. This could be helpful for carbon market investors, as they are
trading on the basis of daily price fluctuations, which are represented best by the
volatility.
Sixth, different carbon markets witness different volatility patterns and hence separate
econometric models are required for forecasting volatility for each of these markets. The
volatility behavior of voluntary market of Chicago is quite similar to that of other
financial markets and energy markets like oil and natural gas, which are all forecasted
well by complex non-linear GARCH models. Whereas, simple models like Historical
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averages and GARCH (1, 1) perform the best for compliance bound EU market, thereby
indicating different behavior of compliance carbon market from the voluntary carbon
market and other financial and energy markets.
Seventh, genetically programd agent-based models have considerable potential in
understanding the price dynamics of CO2 emission trading markets. These models
possess much better forecasting capabilities than the traditional econometric models. The
artificial carbon markets obtained from such agent based models generate features which
are remarkably similar to those from the actual data. Experiments performed on
artificially simulated carbon markets show that changing parameters of the model could
be adjusted to further increase the forecast accuracy, which is not possible in the world of
analytical models.
6.2 Policy Implications
Some policy implications are drawn from these analyses:
First, the effects of inter-continental CO2 allowance trade, particularly across Atlantic are
limited so far, and hence the market power1 of regional credit suppliers is large (Vatiero
2010). An important policy intervention could be to allow establishing links between
different countries, irrespective of their Kyoto commitments to tackle climate change
through use of markets. It will provide better opportunities to the traders and also result in
unification of allowance prices in the two continents. The result could also be used for
convincing the North American countries to fix emission reduction targets, as it will
bring efficiency in overall global carbon trading mechanism.
Second, as the CDM project markets are cointegrated and reflect efficiency, a policy
level decision could be taken to boost the CER market to further promote the CDM
projects, as they have an additional virtue of ensuring sustainable development in the
1 Market power is the ability of a firm to alter the market price of a good or service. In perfectly
competitive markets, market participants have no market power.
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developing countries. Developed countries should continue to improve the investment
environment and enhance incentives for these projects in the developing world.
Third, markets which are not integrated show inefficiencies and policy level changes are
required in the structure and design of the individual trading schemes. A policy level
initiative would be to link such emission trading mechanisms so as to integrate them in
the long run.
Fourth, understanding the short-term volatility dynamics might enable companies to
monitor the costs of CO2 emissions in their production process. At firm level, it could be
useful in deciding about banking and borrowing of the carbon credits as these options
allow firms to smooth emissions over time, which in turn smoothes the price of
allowances and increases certainty and thus investment.
Fifth, the study of volatility dynamics may be helpful in choosing between carbon tax and
the market based instruments at the policy level. Drawing comparisons between the
forecasts of various carbon markets with expected returns from a carbon tax, the policy
makers can have a choice to go for either or a mix of the two. Knowledge of volatility
behavior in different markets also presents policy makers an ability to choose from
different mechanisms to reduce uncertainties: To allow banking of allowances for future
use for motivating firms to further reduce their emissions now by allowing them to
establish a reserve for the future; or to allow firms to borrow allowances from future
periods; or to set price floor and/or ceiling to reduce the risks to investments in emission
reductions.
Lastly, the agent based models could be used to draw comparison between not only the
price dynamics, but also for choice of policy instruments, as they are shown to possess
better forecasting capabilities through incorporation of agent behavior, heterogeneity and
limited rational behavior.
6.3 Limitations and Future Work
While economic analyses in this research provide useful results related to price dynamics
of world carbon markets, they suffer from some limitations.
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First, the number of co-integrated markets is not a true measure of the degree of market
integration; that can be only assessed by measuring the reaction time to remove
disequilibria from the cointegrating relationships. Similarly, Johansen’s multivariate
cointegration procedure does not take into account the transaction costs, the marginal
abatement costs and other charges associated with carbon trading; and therefore is not a
very reliable method for analyzing the efficiency of arbitrage between the two markets. In
addition, only univariate price equations have been considered, whereas, volume of
trading can also be taken into account while exploring market integration. Future research
should include more assets from other carbon markets of the world and the enhanced
aspects of market integration. New research should not only confirm or contradict the
present results, but also try to resolve such issues by using further econometric analysis
and looking at more recent developments in the international carbon markets using more
extensive data series coming from newly emerging carbon markets.
Second, this dissertation is one of the pioneering studies pertaining to economics of
world’s carbon markets and hence is limited to examination of only price variable. All
the models considered belong to the univariate time-series family of carbon prices, and
examination of multivariate models that may include traded volume might bring some
changes in the results. Environmental variables like weather patterns and financial
variables like the number and type of listed companies on the climate exchanges and
policy issues like mandatory fixing of emission reduction targets by the federal or
provincial governments in the EU and US can further improve the accuracy of results.
Third, different carbon markets became functional at different points of time and hence
slightly different time horizons have been used in all the analyses. Accuracy of results
might improve after passage of more time, as the markets get mature.
Fourth, more trading options have been introduced recently in both EU market and in
Chicago Climate Exchange. An array of additional financial instruments-options,
derivatives and swaps are becoming available in different carbon markets around the
world. Their comparison will be desirable in future to get a more comprehensive picture
of the world carbon markets.
102
Fifth, an extension of agent based model could be made to other carbon markets for
forecasting as the new data becomes available from different parts of the world. Different
groups of carbon market participants like electric utilities, forest owners and transporters
etc. can be clubbed together and inter-group dynamics of market can be explored. In
addition, the behavior of fundamental trading agents can also be incorporated using the
data of marginal abatement cost from various industries. Experiments on artificial
markets have focused so far on three parameters only, keeping others fixed. Future work
may also include variations in other parameters also, so as to have a bigger picture on the
market behavior in carbon trading. Similarly efforts could be directed towards
incorporating policy level decisions in the model to incorporate strategic variables in the
agent based model. Further improvements could also be made in computation capabilities
also by making use of high level programming codes in high level languages, for
example, Java and C++, to have a better picture about use of agent based models in CO2
emission trading markets.
Lastly, no structural breaks have been observed in data in most of the data, which shows
that policy level changes so far did not have very significant effect on the price dynamics
of these markets. However, with significant policy level changes at Copenhagen and
Cancun meets of UNFCCC, some structural breaks may arise in data in future, which
needs to be taken into account for further analysis.
103
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Appendix-1
Fig. A1. Genetic Programming Flowchart, adopted from Koza (1992)