Platforms and Exchanges

Platforms and Exchanges
Jon Levin Winter 2010 Economics 136

Auction Platforms

Introduction What is a “platform”?
By analogy to computing, where a platform is a hardware architecture or set of standards that that allow software applications to run. Platform markets bring together different sides of the market to interact. Many examples: visa payment network, video game systems, online dating sites, iPhone app store, etc. Today, focus on auction platforms: markets such as eBay, Amazon, financial exchanges, with A structured environment for buying and selling goods Often a very specific set of market rules & institutions.

Introduction Idea 1: Bigger than the auction itself
Auction design: specify the bidding rules, who gets the object(s), and payments to be made. Search/Information: platform markets help buyers and sellers find one another, and allow them to exchange information (e.g. presentation of informaiton on eBay/Amazon). Standards: quality scores, reputations scores, grading of used goods, targeting in online ads. Contract design and enforcement: rules for pricing and exchange and various mechanisms for verifying and enforcing that these rules are followed.

Introduction Idea 2: Platform creates an ongoing market
We’ve been analyzing auctions “one at a time” Platform markets generally involve ongoing exchange, many sales every day, market evolves over time. Platform is setting the rules of the environment, e.g. on eBay the type of ways that sellers can offer stuff to buyers or set prices. Doesn’t always have to be auctions, e.g. Craigslist and eBay have quite different pricing…

Market design Platform operators have to consider
How to attract buyers and sellers How to match them efficiently How to ensure market runs in an orderly fashion How are the gains from trade shared How does the platform make money Tools for answering these questions Theoretical models (as in search auction case) Experiments (very common in online markets) Data analysis (platforms get to collect a lot of data)

Monopoly platforms “Indirect” network effects: more buyers attracts more sellers and vice-versa. Platform may have to decide which side to charge - Does it matter? Why might it matter? Common to charge one side of the market but not the other (e.g. yellow pages, sponsored search, dance clubs, visa?) Platform may have to trade off market efficiency (creating a bigger pie) and profit (taking a bigger slice). Recall our auction design choices in sponsored search.

Competing platforms Nature of competition and effects of competition depend on single vs multi-homing. If one side “single-homes,” platform can charge the other side for “unique access” Can create a lot of competition to attract single-homers, e.g. payments, exclusive contracts. Scale economies may be very important Scale can allow platform to improve its technology (fixed costs amortized over more transactions). Scale effects can also be subtle - sellers like more buyers, but not necessarily more competing sellers!

eBay’s Market Design

eBay and online markets
eBay: largest site for e-commerce 81,000,000 monthly visitors 140,000,000 listings on a given day $8,500,000,000 platform revenue Competes against Amazon, Craigslist, other online sites, plus many off-line companies. Today: discuss its marketplace design.

Market + Search Technology
Many heterogeneous objects being sold Different kinds of sellers and buyers Range from full-time “pros” to casual participants. Ongoing market, sequential sales/closes Multiple pricing mechanisms Auctions, Posted prices (Buy it Now) Buyer search Catalogs/Browsing, Sophisticated search Featured listings, advertising

Structure of Buyer Search
What distinguishes listings What is the good, new/used Who is the seller, reputation, location What is the sale type: auction, posted price. Some important issues Prioritize auctions or posted prices Catalogue vs non-catalog items Distinguish sellers, or initially just goods Conflation? Or emphasize diversity?

Conflation in market design
Emphasize diversity Traditional eBay search brought up page of listings, organized by auction ending time. Similar items might look very different – sellers have an incentive to emphasize diversity! Conflation (a la Amazon, eBay more recently) Search for product, product is displayed Sellers, and seller distinctions revealed later. What are the trade-offs? Do they depend on the nature of the item and perhaps buyer?

Listings and Information
eBay in principle controls what sellers can communicate to buyers Different types of information in listing Standardized information “Free-form” information (photos, videos, text) Some important issues eBay imposes relatively little structure on sellers Only recently has started to collect “extra” information from sellers – why would you do this?

Disclosure and Adverse Selection
Especially for big-ticket items, buyers may be worried about the quality of the item. There is potential for adverse selection problems. Seller disclosure might mitigate these issues. Greg Lewis (2009) study of eBay motors. Provides empirical evidence by correlating sale prices with amount of information disclosed by sellers.

The Lemons Problem Three kinds of car: peach, apple, lemon
Buyer values: $2500, $1800, $1100 Seller values: $2000, $1500, $900 Seller knows quality, buyer doesn’t Equal numbers of car types “Akerlof” lemons problem At market price > $2000, all sellers will sell, but buyer expected value is only $1800… No trade. At market price btwn $1500 and $2000, apples and lemons will be available, but then buyer expected value only $1450 At market price btwn $900 and $1500, only lemons will be available, so buyer value is $1100. Market eqm: Lemons trade at btwn $900 and $1100.

Disclosure If quality can be costlessly disclosed
Peach sellers will disclose quality So apple sellers will also disclose So buyers will be able to identify lemons! Then all types of cars will trade… This “unraveling” result is quite striking Depends on buyers being sophisticated Or alternatively, on seller competition… What happens if disclosure is costly?

Costly disclosure Back to our example Suppose disclosure costs $400.
Buyer values: $2500, $1800, $1100 Seller values: $2000, $1500, $900 Suppose disclosure costs $400. Peaches will disclose, sell for $2000-$2100. Apples will not disclose – too expensive! Lemons will trade w/ no disclosure $900-$1100. Costly disclosure can lead to intermediate trade. Doesn’t have to be the best items that trade, but the items where there are large gains from trade!

Contract/Transaction Design
eBay can place structure on the transaction itself, or facilitate transaction in various ways. What is the contract? Seller agrees to provide object as represented. Some “free form” terms and conditions Buyer pays first, usually no escrow! Trust-based (compare with China eBay) Main issue: safety for buyers…

Reputation mechanisms
Seller reputation helps enforce “contract” Buyers give feedback post transaction Sellers also give feedback post transaction Participants acquire scores – pos. & neg reviews What are the issues What is the incentive to give feedback Retaliation problems Bolton et al. innovative designs New programs for “top sellers” – good idea?

Bolton et al. on Reputation
How can you avoid the “retaliation” problem? Don’t let seller give feedback Don’t let buyer/seller see each other’s feedback Mix of disclosed/non-disclosed feedback What are the trade-offs? Other reputation issues Why are the sellers trusted? Why not buyers? Are there other ways to organize the market? Markets turn out to be quite local – trust issues?

Auction mechanism Ascending auction with proxy bidding
Enter “maximum bid” – proxy bids up to maximum Object awarded to standing high bidder at close Hard close: auction ends at a fixed time Secret reserve – minimum bid usual reserve, but possible an extra “secret” reserve. What are the issues? Sniping (late bids: why does this happen?) Squatting (early bids: why does this happen?)

Sniping (Roth et al.) Roth and Ockenfels (2002)
Sniping observed at “hard close” auctions Sniping doesn’t occur when there is a “soft close” Why would you wait to the last minute Early bids might attract attention to a listing Early bids might signal object was valuable Even if these benefits are small, the costs of delaying to the last minute are also small or zero. Squatting is what you expect if the effects go the other way – early bids “scare off” other bidders.

Auctions vs Posted Prices
eBay has moved toward to posted prices Now more than half of the platform is prices What are the trade-offs? Auctions: good for price discovery, maybe fun for buyers, perhaps good for unusual or unique items, forces buyers to compete. Prices: good for immediacy, speeds up the market potentially, forces sellers to compete. Why would the market have shifted?

Market design, broadly What are all these smaller decisions aimed at
Attracting participants Efficient matching of buyers and sellers Orderly and safe transactions How could we assess the efficiency of the market? What measures to look at? Number of participants and level of engagement, how easily/quickly buyers and sellers can trade, level of prices, amount of fraud, market structure.

Financial Exchanges

Today’s Lecture Background on financial exchanges
The role of financial exchanges Desirable attributes of an exchange History of these markets Specialist markets, OTC markets, exchanges The move to electronic exchanges Market design issues Information aggregration, large orders Competing platforms, transparency, dark pools.

Public equity markets Focus on markets for public equity
Companies issue publicly traded stock. Historically traded on a few large exchanges Recently competition between exchanges and a great deal of innovation in exchange design. Questions to consider What are the objectives for a successful market? What designs help achieve these objectives? What is the role of competition between markets?

Market objectives Objectives for the public equities market
Price discovery (prices reflect current information) Fair competition (open access, nondiscrimination) Investor protection and confidence US regulates financial markets to achieve these objectives, looking at things such as How fast are orders executed? How large are spreads? How large is systemic risk (e.g. risk of a complete market shut-down)? Are certain investors being advantaged or disadvantaged? Is there cheating or fraud?

Desirable market properties
Liquidity In liquid markets, traders can buy or sell large quantities of shares without a large price impact. Transparency Participants have information available to them before making a trade (receive a quote, see open offers) and after a trade (see prices, quantities). Price discovery Prices incorporate and track available information in the market - and do so in a reasonable and efficient way.

Organization of Markets
Historically, equities in US were mainly traded on the floor of the NYSE. NYSE as a “specialist” market Each stock managed by a specialist Specialist quotes “bid” and “ask” prices Investors, who are physically on the trading floor, trade with the specialist at these prices Specialist holds some stock to keep market functioning, but not very large positions.

Organization of Markets
Nasdaq competes with NYSE and was historically an “over the counter” market. Organization of OTC markets Small number of “brokers” quote bids/ask to prospective traders, who can trade with any of the brokers. In some OTC markets, executed trades are posted publicly creating a degree of transparency. OTC organization is typical for less “liquid” securities: corporate and municipal bonds, derivatives, etc.

Organization of markets
Equity trading has increasingly moved to electronic order books, including at NYSE. Organization of electronic exchanges Traders submit orders to buy or sell Orders are posted in an electronic “book” If a buy order comes in above a current sell order, the orders are “crossed” and a trade is executed. Different exchanges allow different types of orders (more on this in a minute).

Organization of markets
Many large trades take place “upstairs” - not on the NYSE floor or in a public exchange Organization of large trades Often a bilateral negotiation or by private placement. Example: investor approaches Goldman Sachs to sell a large position. GS either finds a buyer, or buyers, or buys the position itself and then dribbles it out over time. Recently, many electronic exchanges are trying to automate large trades by allowing for more sophisticated types of orders (more later).

Recent events: 2005-2009 Location of trades
In Jan 2005: NYSE accounted for 80% of trading volume in NYSE-listed stocks; by Oct 2009, down to 25% Execution speeds for trades Falls from 10.1 seconds in 2005, to 0.7 seconds in 2009. Trading volume From 2.1 bn shares/day in 2005 to 5.9 bn in 2009. Average trade size Falls from 724 shares in 2005 to 268 shares in 2009

Current market Trading mostly done on electronic platforms Questions
Five large exchanges (transparent) Multiple smaller electronic exchanges Internal or “dark” trading pools (not transparent) Questions Does this fragmentation matter? (Offer to buy and sell must be posted publicly to all exchanges.) Why the proliferation of markets? Should different types of trades be executed in different markets?

Applying economic theory
Price formation and price evolution “Efficient” markets with asymmetric information Model bid/ask spreads in specialist markets Search costs and market frictions Bid/Ask spreads in OTC markets Large orders and price impacts Design of exchanges, and exchange competition.

Modeling price formation
Consider a specialist market Specialists offer bid price b (offer to buy) and ask price a. Traders arrive and can buy or sell at these prices. After trading, world ends, stock pays d ~ U[0,1]. First consider traders coming to sell… Two types of traders, equally likely to arrive Smart trader: knows d, sells only if b>d Dumb trader: doesn’t know d, sells at any b. Specialists don’t know d, but they understand the environment and quote a price that ensure they will just break even on average.

Bid prices in market Specialist quotes a price b
With probability 1/2, dumb trader shows up Trader sells the stock for b Specialist makes a profit d-b With probability 1/2, smart trader shows up Trader sells the stock if b>d Specialist makes a profit (really a loss) d-b

Specialist market If dumb trader arrives, sells for b, specialist gets E[d]=1/2 If smart trader arrives, only sells if d<b, I.e. with pr=b b If d>b, smart trader will not sell If d>b, smart trader will sell 1 E[Profit] = (1/2)b - b = - (1/2)b E[Profit] = 0

E[Profit] = (1/2) * [ 1/2 - b ] + (1/2) * b * (- 1/2* b) = 0
Bid Prices in Market What is the expected profit for specialist If dumb trader: expected profit is 1/2 - b If smart trader: expected profit is b * [ -(1/2)b ] Specialist break-even condition E[Profit] = (1/2) * [ 1/2 - b ] + (1/2) * b * (- 1/2* b) = 0 Solving for the competitive bid price 1/2 - b = 1/2* b2 1-2b-b2 =0 b = 0.414

Ask prices in the market
Now suppose traders may also show up to buy, and specialist quotes an “ask” price. Two types of buyers, equally likely to arrive Smart traders: know d and buy if d>a Dumb traders: don’t know d and buy at any a Specialists quote an ask price that ensures they will just break even in expectation Will the ask be above or below the bid?

Ask prices in the market
Specialist quotes an ask a With probability 1/2, dumb trader arrives Buys at a Specialist profit is a-d With probability 1/2, smart trader arrives Buys if a<d

Solving for ask prices Specialist expected profit
If dumb trader: profit is a-1/2 If smart trader: profit is -(1-a)*(1-a)/2. Solving for the break-even ask price 2a - 1 = (1-a)2 a = 0.586 Compare to the bid b = 0.414 The “spread” is a-b, here 0.172

Price Formation & Dynamics
Efficient market theory Current prices equal E[Value | Current Info]. True in this theory, except an offer to buy or sell convey NEW information. So each “buy” trade raises the price and each “sell” trade lowers the price. Specialists charge a “spread” a>b to protect themselves from private information of the traders. More liquid market means lower spreads, and probably less movement of prices with each trade.

Competition and Spreads
What makes spreads larger or smaller? More informed traders => larger spreads Less specialist competition => larger spreads Competition and spreads? If specialist has no competition, can set a=1,b=0. Trades with probability 1/2, but makes an expected profit of 1/2 on each trade. Extreme example, but can more generally specialist can increase spread and trade only with the dumb money. Competition prevents this by forcing spreads to be narrower - but requires traders to be able to “shop”.

Theory of Exchanges View exchange as a “double auction”
Buyers put in demand curves Sellers put in supply curves In “one-shot” case, would collect all offers Compute aggregate demand and supply. Find market clearing price, execute trades. In practice, trading takes places in real time This means that orders don’t all come in at once. And trades get executed as the opportunity arises Why trade in real time, rather than an auction every hour or day or week?

Dynamics of the order book
Traders put in buy and sell orders Limit order: offer to buy or sell at some price p Market order: buy or sell at best offered price. Example of the “order book” w/ limit orders Orders to buy at 80, 90, 100 Order to sell at 110, 120, 130.

Example 130 120 There is currently no trade to execute b/c best sell offer is 110 and best buy offer is 100. 110 100 90 80

Dynamics of the order book
Current order book Orders to buy at 80, 90, 100 Order to sell at 110, 120, 130. Buy order comes in at 120. “Crossed” with the best sell order (110). Updated order book Orders to sell at 120, 130

Example 130 120 110 100 90 80

Price impact of large trades
What happens if there is a large trade Large buy order can “eat up” the supply curve If there is little “liquidity”, maybe big price impact. Example of order book Buy orders 80,85,90,95,100 Sell orders 105,110,115,120,125,130,135 Average of best buy/sell offers: 102.5 Buy order for four units at best sell offers => Buy orders 80, 85, 90, 95, 100 Sell orders 125, 130, 135 Average of best buy/sell offers: 112.5

Efficient markets? Theory of efficient markets assumes roughly that p = E[Value| Current Info]. If a seller has to liquidate a large holding of stock for reasons that aren’t informative about the value of the company, shouldn’t matter for the price. But in practice, doesn’t always work this way. Example: de-listing of stock from S&P Index funds all sell on a given day. This is understood well in advance, so no “news” But stock price generally falls and often takes a substantial amount of time to recover!

Problems for Large Traders
Limit orders make it difficult for large traders to get a good price… Example: Buyer and seller each willing to hold two units, but have decreasing marginal values. Buyer has values 35 and 32 Seller has values 30 and 25 Efficient to trade both units Buyer ideally offers 55 for two units.

Large trades, cont. Example, cont. Buyer values 35 and 32
Seller values 30 and 25 If buyer offers 30 and 25, seller can trade one unit at 30 and won’t want to trade a second unit. To get the seller to sell both units, buyer must offer 30 and 30, and spend 60 for 2 units. More profitable to offer 25 for one unit, and make profit 35-25=10, than pay 60 for two units and make profit =7.

More problems Large traders suffer from “front-running”
Example of order book Buy orders 80,85,90,95,100 Sell orders 105,110,115,120,125,130,135 Large trader submits buy order for four units Should pay 105, 110, 115 and 120 for these units. Front-running strategy Front-runner jumps ahead and submits buy order for 3 units, then offers to sell 3 units at 120. Front-runner buys units at 105, 110, 115, sells at 120 Large trader pays 120 for all units It pays to be fast in a financial market!

Strategies for large trades
Large traders try to avoid this Execute trades slowly, e.g. order one unit (pay 105), then one more unit (pay 110), and so forth. But this slows things down, and seems inefficient. Alternative to take the trade “upstairs”: find a large seller, or pay an intermediary to assemble or liquidate the position slowly. New exchanges try to improve the market design to facilitate large trades…

Innovations in exchanges
“Icebergs” and hidden orders Allows traders to submit offers that are entered in the order book, but “hidden” from view. Makes it harder for predatory traders to front-run, and can allow large traders flexibility. “Dark pools” Orders submitted to broker (e.g. Goldman Sachs) are “crossed” before being submitted publicly to the exchange. Traders cannot see what is going on in this “dark” exchange, which benefits from seeing the prices and being able to access the liquidity in the public exchanges. Many interesting market design questions around the design of public and dark exchanges…

Market Design and Toxic Assets

Today’s Lecture Securitization and markets for loans
How credit markets and securitization work Why securitization: informational theories Economics of secured credit markets Leverage theory & Feedback effects Application to real estate and secondary markets The panic of & market failure How the market failed, explanations and implications Attempts to “restart” and “redesign” these markets

Increase in consumer lending

Consumer Lending Traditional consumer lending (by banks)
Bank takes deposits from consumers Bank lends the money out to borrowers Bank collects payments on loans As payments come in, can originate new loans. Modern consumer lending (by banks & others) Lender raises money (maybe deposits, but maybe not) Lender originates loans to borrowers Lender resells loans to secondary market Cash from loan sales can be used to originate new loans.

Traditional Lending (in pictures)
Borrowers Step 1 Step 2 $ $ $ Step 0 Lender Depositors

Securitization (in pictures)
Borrowers Step 3: $ (via a loan “servicer”) Step 1 IOU IOU $ Step 2 $ Trust $ Step 0 $ Lender Depositors $: Step 3 Step 2 Investors Insurers Insurance (CDS) $

“Securitization” Securitization process Key features of the market
Lender sells a “pool” of loans to the trust. Trust sells financial claims on the loan pool. Sale of the claims used to pay the lender. Trust collects loan payments & pays claim holders. Key features of the market Pooling of many loans (rather than resale of single loans) Tranching of pool payments to create securities Why these features? Risk-sharing & information.

Pooling Pooling can diversify risk Pooling can lower transaction costs
Suppose each loan promises $1 but defaults with prob = 0.1 So E[Payment]= $0.90, but Pr[Payment = 0] = 0.1. Consider 1% claim on 100 loans with independent default E[Payment] = 0.90, but Pr[Payment = 0] = (0.1)100. As the pool gets large, if defaults are really independent, then each fractional investor is likely to get close to $0.90 Pooling can lower transaction costs If each loan sold separately, investors want to inspect each one to cherry-pick the pool => sale process very costly. If loans are pooled, everyone gets a representative claim on the pool & harder to cherry-pick. (Analogy to de Beers!)

Tranching Consider pool of loans made by bank
Loans promise $100, but may deliver as little as $80. Investors think all outcomes btwn 80 and 100 equally likely. Suppose the bank knows what the outcome will be (v). Suppose bank tries to sell the whole pool If investors offer 100, bank will sell, but E[Ret. to Inv]=90. If investors offer 90, bank will sell if v<90, E[Ret. to Inv]=85 In equilibrium investors cannot offer p>80 and break even. So, investors offer 80, and bank only sells if v=80. The market doesn’t work to allow resale.

Tranching Alternative “tranching” structure From theory to practice
Bank sells claim on the first $80 in loan payments, and keeps all additional loan payments. Investors will pay $80 b/c claim is a sure thing. So finance the pool with a combination of debt (sold to investors) and equity (held by the bank). From theory to practice Typically several tranches, which take losses in order: “junior” tranche is like equity, “senior” more like debt. Key feature: junior tranches are “information sensitive”, but senior tranches are less so --- doesn’t matter if pool will return 80, 85, 90, 95, investors get paid regardless!

Ratings Agencies Rating agencies (Moody’s, S&P, Fitch)
Usually grade corporate debt: AAA,AA,A, etc. Higher grades “safe”, lower more likely to default. Also grade “securitization” proposals: most senior tranches might be AAA, equity tranche maybe B. Concerns about ratings agencies Investors relied “blindly” on high ratings, but maybe… Gaming: tranches designed to “just” make the grade. Bad models: underestimated default from falling house prices, or the possibility of mass (correlated) defaults. Focus on probability of default rather than whether defaults would occur in “bad” states of the world (subtle).

Secured Lending & Collateral
What happens when borrowers default? If a loan is unsecured (like a credit card) Lender can try to harass the borrower, but not much else the lender can do to recover value. If a loan is secured (like a mortgage) Lender gets the underlying collateral. So default is less costly for the lender, who can charge a lower interest rate. If collateral is sufficiently valuable, default won’t even occur because if the borrower can’t make payments, she can sell the asset and use the proceeds to pay off the loan.

Collateral & Feedback effects
With secured lending, there can be feedback effects between the credit market and the asset market. Example with housing market When prices are rising, lenders expect that even if they lend a large fraction of the purchase price, the house will become more valuable and so default won’t happen. And if home buyers can borrow a lot and at low rates, they can pay more, so prices go up => positive feedback. When prices are falling, the reverse can happen…. Lenders will lend a smaller fraction of the purchase price, buyers have a harder time getting cash, prices fall more…. Downward spiral can be exacerbated because borrowers who can’t make payments default and houses are sold at auction increasing supply of houses for sale.

The Leverage Cycle On the way up… On the way down
Asset prices expected to rise Lenders offer generous credit Buyers can spend more Prices do rise, and so loans get repaid… On the way down Asset prices expected to fall Lenders tighten credit Buyers can’t spend as much Prices do fall, and so loans don’t get repaid, and so.. Additional “forced sales” bring prices down further.

Housing Bubbles & Crashes
Many financial crises triggered by real estate US Savings and Loan collapse (early 90s) Japanese bank failures in (early 90s) Sweden and Finland bank failures (early 90s) US and other recent problems Why real estate (rather than, say, business failures) Owners of real estate tend to be highly levered, as compared to, say, owners of businesses. Maybe why stock market crashes in 1987 and 2001 didn’t trigger broader financial crisis … although these events themselves may have been exacerbated by investor leverage.

Investor Leverage Buyers of loans in the secondary market also used leverage… sometimes lot of it! In January 2007, it was possible to buy a AAA mortgage-backed security, and borrow 98% of the purchase price. These rates were available, however, only if the investor rolled the loan over every night (in the “repo” market) Feedback and the leverage cycle, again… If a small number of optimistic investors can borrow 50:1, they can have large buying power, and drive up asset prices --- particularly if it is hard to “short” the asset. Once prices start to fall, however, the optimists will be wiped out & credit will tighten, so prices fall a lot --- it’s harder to borrow & optimists are out of the market. This can lead to a crash in the secondary market.

Feedback & Panics Bank runs - the old-fashioned kind
Consumers have deposited money at bank and can come at any time to withdraw. A deposit is like a loan that is rolled over every second. Bank has lent the money out, keeping only a small reserve, so if more than a few investors withdraw, bank can’t pay. If consumers believe the bank has made some bad loans, there can be a race to get money out. Government solution: deposit “insurance” Many bank runs in the 19th century and Great Depression, following which US Gov’t decided to insure deposits. If investors demand their money, and bank can’t pay, govt will. So long as investors are confident in FDIC, no runs.

Feedback & Panics Bank runs - the modern kind
Suppose a financial institutions is borrowing short term (e.g. buying mortgage securities with overnight financing). If investors get nervous, they pull their money, knowing that the gov’t may not be there to “insure” repo loans. Plus, there are some additional twists.. If institution is a broker (Lehman, Bear, Morgan Stanley), its customers (e.g. hedge funds), may also run, so franchise value of the institution disappears. Small number of “core” institutions are interdependent: e.g. they may have sold each other insurance, so if bank A fails, and owes money to bank B, bank B may fail.

Stepping back (macro picture)
From , there was a lot of money chasing investment opportunities. Savings glut in China, Middle East, etc. Fed had interest rates low so investors wanted better investments than treasuries. Lots of this money found its way into housing. Home ownership increased from 65% to 69%. House prices increased by XX%. Leverage increased a lot: many new purchases at zero percent down, and home equity loans let homeowners increase size of mortgage relative to house value. Didn’t everyone see problems coming? Why not?

The panic of 2007… Housing market started to turn:
bad loans (esp. subprime from ) began to default, even tranches viewed as “safe” become risky. Everyone realized that investors holding securities based on bad loans were going to take losses. Loss of confidence in shadow banks many of which were holding these securities, and many of which were reliant on repo financing Triggering feedback effect and panic Slow-motion run/collapse of the repo market Prices being offered for mortgage-backed securities dropped sharply, making it hard for investors to liquidate position without taking big losses.

… continuing into 2008 Positions of finacial institutions deteriorated
Failure of Bear Stearns in Feb 2008. Takeover of Fannie Mae, Freddie Mac, IndyMac. Collapse of WaMu, Lehmann, AIG, Merrill, Wachovia, etc. Government interventions Initially, offered loans to replace lost repo financing, and responded in ad hoc way to individual failures. Eventually proposed TARP Idea: buy “bad assets” from the banks, restoring confidence Actual: buy equity shares in the banks, restoring solvency. Plus Fed eventually buys $1+ trillion of mortgage-backed securities essentially taking on the role of lender to prevent further economic collapse, especially in housing.

Market Design Problems
Market for “toxic assets” Once housing prices started to fall, seemingly “safe” securities became risky, market shut down. Why did the market shut down? Asymmetric information? Anticipation of bailout? Could it have been re-started? TARP proposal was for Treasury to buy large quantities of toxic assets. Initially considered a SAA or treasury-style auction. Later considered setting up funds managed by professional investors who would buy in various ways.

Market for Credit Default Swaps CDS are insurance contracts that pay off if a given loan (or bond) defaults. Example. I buy a Lehmann bond that promises $1, and pay AIG to insure it. I pay AIG premiums and if the Lehmann defaults, AIG makes me whole. Markets for CDS settlements? When Lehmann goes under, it takes a while to sort our who gets what, but ideally want to “settle” the CDS contracts right away based on value of the bonds. But what is it? Should CDS markets be bilateral or exchange-based? CDS contracts are bilateral - concern is spillover effects and non-transparency. New proposals call for exchanges where parties would trade against the exchange.

Funding markets for financial institutions Part of the problem in the crisis was the heavy reliance on short-term repo financing. Is it possible to have better markets/models for financing financial institutions? Insured deposits? Loans that can be re-paid with stock certificates? Or maybe just limits on leverage? Difficult regulatory problem because gov’t ultimately bears a lot of the risk, but doesn’t want to overly constrain financial institutions.

Macroeconomics How does the recession link to the crisis?
Starting in and accelerating in fall 2008, consumer spending, employment, and investment dropped. Various theories to explain this Harder for consumers and firms to get credit Loss of consumer confidence and wealth Loss of business confidence, and uncertainty Government creating additional uncertainty… Of course, in 2009, the survivors in the financial markets actually did great … Partly because of gov’t lending them lots of money at cheap rates, and partly because market dislocations created a lot of opportunities…

Platforms and Exchanges

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