Methodology of Exchange Design

1. Methodology of Exchange Design John Ledyard and Preston McAfee Caltech and Yahoo!

2. 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 Cornell University

3. 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? Cornell University

4. 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 Cornell University

5. 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. Cornell University

6. 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. Cornell University

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9. Converging slowly when thin Cornell University

10. A little faster when much thicker (N=40) Cornell University

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13. What a CVM can do to a thin market! (N=12) Cornell University

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18. 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 Cornell University

19. 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 Cornell University

20. 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. Cornell University

21. 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. Cornell University

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