Download Methodology of Exchange Design

Methodology of Exchange
Design
John Ledyard and Preston McAfee
Caltech and Yahoo!
Introduction
• There is a large literature on the design of selling
mechanisms.
– Builds on theory, experiment, and other practical tests.
– Has led to a practical methodology for the choice of
selling methods
• Little has been written on practical exchange design.
• Exchange examples
– Flow: a group of sellers sells a continuing sequence of
differentiated goods to a group of buyers
– Stock: A group of traders is continuously rebalancing
asset positions
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Introduction (continued)
• An exchange maps expressed preferences into
allocations and $
– It is a mechanism, which may be iterative and reactive
– The process of expressing preferences and the mapping into
allocations is exchange design
• Different from auction design
– Competition between multiple sellers
– Goals: efficiency not revenue, exchange profits
– Auction methods (e.g. ascending prices) may not be
applicable
– Replacing brokers – network externalities
• A single seller can replace own brokers with auction.
– New issue – who to charge?
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Introduction (continued)
• Not much literature
– Theory
• VCG
• Myerson-Satterthwaite, Gresik-Satterthwaite,
• General equilibrium
– Experiment and Practice
• One-sided lessons
– We are putting together a bibliography
– Please send references
• Today:
- A rough “state of the art” commentary
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Summary
(In case I don’t get to the end.)
• Expressively Easy
– Design a language for expressing desired trades that
accommodates important distinctions.
– Understanding what participants actually value is critical to
a successful design.
• Strategically Simple
– Design trading algorithms so that a straight-forward
strategy performs reasonably well.
– Permitting iterative adjustment of bids can simplify
strategies but should be binding.
• Functionally Fair
– The exchange design should not be tilted towards one type of
participant.
– Exchange and traders must keep commitments.
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A running example – RECLAIM
• The Cap – maximal pollution levels by year
• The Permits – (year, cycle, zone)
–
–
–
–
–
Years: (initial) 1994-2010
Cycle 1 – Jan to Dec, Cycle 2 – July to June
Zone 1 – inland, Zone 2 – coastal
Declining aggregate amount, total 50%.
Example: To cover pollution in Feb 2008 an inland firm can
use either (2008, 1, 1), (2007, 2,1), (2008, 1, 2), or (2007, 2,2)
• A trader’s problem is to decide whether to buy and sell
permits or to install abatement equipment covering 20
years, one needs to negotiate over quantities and prices
of 80 different permits.
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Converging slowly when thin
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A little faster when much thicker
(N=40)
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What a CVM can do to a thin market!
(N=12)
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A real application - bonds
• Allowed more order types
– Downward sloping demand (diminishing MU)
– Upward sloping demand (quantity discounts)
– ORs of ANDS, ANDs of ORs, etc.
• Size and difficulty of the real problem
– 200,000 variables, 300,000 constraints
• 2,000 bonds
• 50,000 bids (many contingencies allowed = {0,1})
– Relaxed algorithm (LP) took 20 minutes
– Needed a solution in 7 minutes
– Could get 85% of best known bound 90% of the time
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Expressively Easy
• Intentionally design a language for expressing trades
that accommodates important distinctions.
• Understanding what participants actually value is
critical to a successful design.
– The exchange is replacing brokers who “know their clients”
• Different but similar products can be treated as identical
to simplify
– Issue: exogenous or endogenous?
• Different buyers can have different interfaces and bid
formats
– Spot buyers vs. impression buyers
– Portfolio balancers vs. single issue speculator
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Strategically Simple
•
Design trading algorithm so that a straight-forward strategy performs
reasonably well
– Dominant strategy is simple but may cost in efficiency
•
VCG vs McAfee vs Uniform Price Call
– Algorithmic complexity can make sensible participation difficult and should be
minimized
•
Generalized Uniform Price Call Market works very well with single-minded
traders.
– Open question: what if they are not single-minded? Conjecture from BFL: still ok.
•
If prices depend primarily on the marginal traders then most have incentive to
“honestly” report willingness to pay and accept.
– Pay what you bid is not a particularly good approach.
– Prices can be set in a relatively coarse manner without significant efficiency loss
•
•
Permitting iteration of bids simplifies but bids should be binding
Information
– Generally want individual bid information not available
– Do want aggregate information, like prices, available
– With combinatorics, fitting in is important so providing individual information can be
valuable. Endogenous sunshine seems to work here.
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Functionally Fair
• Exchange neutrality
– Exchange design should not be tilted towards one type of
participant.
– Example: Max stated surplus and not sellers surplus
• Commitments
– It is crucial that commitments be filled.
• Traders: Deliver promised assets and cash.
– Can enforce with escrow, etc.
• Exchange: Stick to the stated rules.
– Bad examples: Enron, ACE, …..
• Balanced “revenue model”
– Modest levels of revenue can be raised with a straight percentage
charge (and can be incorporated in pricing information).
– Large revenue should be collected with value-add pricing to cause
less damage to efficiency.
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END
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