|In order to meet the obligations under the Kyoto Protocol, the European Union decided in 2003
to introduce the ?rst cap-and-trade system for greenhouse gas emissions in the world. In 2005
the European Emissions Trading System (EU ETS) was launched. Whereas until now the majority
of the European Emission Allowances (EUA's) was handed out for free to the installations
in the system, starting from 2013, a large amount of EUA's in the ETS will be auctioned. This
raises the question how this new primary auction market will affect the secondary market. For a
smooth functioning of the ETS, it is important to minimize price distortions. Price distortions on
the secondary market will increase the volatility of the secondary market price. The uncertainty
about the price in the market will increase, which makes investments in the ETS more expensive.
Furthermore, from Member States revenue perspectives, it is important to assure the clearing price
paid in the auctions is close to the secondary market price.
Since the ETS is still very young and the large-scale auctioning of approximately 1 billion EUA's
per year was never done before, it is very hard to estimate how the market will react to these
auctions. Not in the least because the trading system is very complex. Answering questions about
the price impact of the auctions on the secondary market is only possible within a framework in
which the properties of both primary and secondary market are combined and interaction between
the two can be modeled. As far as we know, this thesis is the first attempt to construct such an
integrated model. The model we constructed builds on two branches of mathematics, namely
auction theory and market impact models. Also from a mathematical point of view, it is a ?rst
attempt to build a bridge between these two theories. It will turn out to be a very powerful
instrument in addressing a wide range of policy questions. Our framework makes it possible to
address the following subjects:
- Determining under which conditions auctions are less distorting to the secondary market
than regular sell market orders.
- Determining whether uniform price auctions allocate efficiently.
- Determining how auction revenue can be maximized while secondary market distortion
caused by the auctions is minimized.
- Determining the effects of specific auction properties on secondary market distortion.
- Comparing the differences in secondary market distortion between different auction frequencies.
- Determining the optimal division of volumes over all auctions.
First, we apply auction theory and market impact models to the European Emissions Trading
System. After that, we combine these theories to be able to address the questions concerning
interactions between primary and secondary markets.
In auction theory, bidding strategies and equilibrium outcomes are studied. An equilibrium is
a situation in which no bidder can improve his position by individually changing his bidding strategy.
The auction mechanism which will probably be used in the ETS is a uniform price sealed bid
auction. This means there will be only one price paid by the winning bidders, the clearing price.
Under specific conditions, bidders in a uniform price auction may adopt a strategy of demand
reduction. This means they bid lower than their true values for the EUA's. When all bidders
adopt this strategy, the clearing price in the auction is below the secondary market price.
In general, a uniform price auction does not allocate efficiently. Efficient allocation means that the
bidders who value the items the most, will win the items. However, because bidders may adopt a
strategy of demand reduction, efficient allocation is not always achieved. Also reselling after the
auction does not automatically lead to efficient allocation, because the auction does not reveal
complete information about bidders' valuations.
To reduce the risk of a low-price equilibrium, it is crucial to attract a suffcient number of bidders
to participate in the auction. When the number of bidders is high enough, bidders will compete
over the price. The higher the number of bidders, the lower equilibrium underpricing will be. A
small tick size, which allows bidders to make very small changes to their bid prices, can make
equilibrium underpricing arbitrarily low. By making the tick size small, bidders are encouraged
to compete over the price. So instead of using 0,01 euro as a tick size, it could be considered to
use 0,001 euro.
Market impact models address the question how to optimally sell a large volume of shares in
an uncompetitive market. An optimal selling strategy maximizes the revenue to the seller and
minimizes market distortion. Market impact models study markets which are operated through
Limit Order Books. In these electronic books, sell and bid orders are collected. Whenever a sell
and bid order meet, i.e. have the same price, a trade is executed. That way prices are established.
Dynamic Limit Order Book models study supply/demand dynamics at a micro-level.
Suppose a large trader wants to sell a very high volume compared to the volumes normally traded
in the order book and he wants to execute this order within a short period of time. The trader will
temporarily distort the Limit Order Book, because his large trade 'eats' a lot of bid orders from
the book. Potentially many of these bid orders are lower than the best bid price. Consequently,
the large trader has to make 'costs' for selling a large volume very quickly by accepting bids below
the best bid price. After the large trade, the new best bid price in the Limit Order Book will be
lower than before. The market is distorted. So market distortion and costs to the large seller are
equivalent in these models.
By splitting up the trade in smaller pieces, costs to the seller and market distortion can be minimized.
Market impact models study the question how small these pieces should be and how the
volumes should be divided over these trades. The answers to these questions depend highly on
speci?c properties of the market. In general, when the market shows quick recovery from trades,
it is optimal to trade very small equal volumes at a high rate.
There are two main reasons why auctions will have an impact on the secondary market price.
Firstly, because the auctions are a specific type of sell orders in a non-competitive market. Because
of the auctions, bid orders may be (temporarily) detracted from the secondary market. This
may lead to distortions of the secondary market price, which can be studied using market impact
models. Secondly, because the auction mechanism itself may give rise to irregularities. The price
paid in the auction is not always equal to the market price. When the clearing price differs from
the market price, it is likely that the market price will be (temporarily) affected after the auction.
To increase revenues and minimize market distortion it is crucial to make the auctions as attractive
as possible both for bidders active on the secondary market and for other bidders. Market
distortion will be minimized when the overlap between the primary and secondary market is maximal,
while extra bidders are attracted to the auction as well. Furthermore, choosing a sufficiently
small tick size can make the risk of equilibrium underpricing low. If this is combined with frequent
auctions, the market impact is minimized.
These conditions are crucial in answering the question whether auctions are less distorting than
regular selling. Smart and smooth auction design is necessary to minimize market distortion.
Otherwise, the outcome of the auctions may cause high price distortions in the secondary market,
and the Member States could do better by selling the EUA's directly on the secondary market.
The approach and the resulting model in this thesis might be useful for other purposes as well.
Applications one could think of are the following:
- Determining criteria for choosing an auction platform while aiming at minimal distortion of
the secondary market.
- Monitoring functioning (and possible manipulations or distortions) of these markets in general
and the impact of auctions in particular.
- Determining optimal selling strategies for selling large amounts of EUA's directly on the
market: (how) should volumes be split up and divided over time?
- Determining optimal selling strategies for auctioning other assets, e.g. government bonds.