This paper shows how the intraday allocation and pricing of overnight loans of federal funds reflect the decentralized interbank market in which these loans are traded. A would-be bor-rower or lender typically finds a counterparty institution by direct bilateral contact. Once in contact, the two counterparties to a potential trade negotiate terms that reflect their incentives for borrowing or lending, as well as the attrac-tiveness of their respective options to forego a trade and to continue “shopping around. ” This over-the-counter (OTC) pricing and allocation mechanism is quite distinct from that of most centralized markets, such as an electronic limit order book market in which every order is anony-mously exposed to every other order with a cen-tralized order-crossing algorithm. While there is a significant body of research on the microstructure of specialist and limit order book markets, most OTC markets do not have comprehensive transaction-level data available for analysis. The federal funds mar-ket is a rare exception. We go beyond a previ-ous study of the microstructure of the federal funds market (Craig H. Furfine 1999) by model-ing how the likelihood of matching a particular borrower with a particular lender, as well as the interest rate that they negotiate, depend on their respective incentives to add or reduce balances and their ability to conduct further trading with other counterparties (proxied by the level of their past trading volumes). Our results are consistent with the thrust of search-based OTC financial

We model a simple market setting in which fragmentation of trade of the same asset across multiple exchanges improves allocative efficiency. Fragmentation reduces the inhibiting effect of price-impact avoidance on order submission. Although fragmentation reduces market depth on each exchange, it also isolates cross-exchange price impacts, leading to more aggressive overall order submission and better rebalancing of unwanted positions across traders. Fragmentation also has implications for the extent to which prices reveal traders’ private information. While a given exchange price is less informative in more fragmented markets, all exchange prices taken together are more informative.

We introduce a simple model of the “percolation ” of information of common interest through a large market, as agents encounter each other over time and reveal information to each other, some of which they may have received earlier from other agents. We are particularly interested in the evolution over time of the cross-sectional distribution in the population of the posterior probability assignments of the various agents. We provide a market example based on privately held auctions, and show how the rate of convergence of the cross-sectional distribution of information is determined by the frequency of auctions and by the number of agents bidding at each auction. Our results contribute to the literature on information transmission in markets. Hayek (1945) argues that markets allow information that is dispersed in a population to be revealed through prices. Grossman’s (1981) notion of a rational-expectations equilibrium formalizes this idea in a centralized market. A number of important markets, however, are decentralized. These include over-the-counter markets and private-auction markets. Wolinsky (1990) and Blouin and Serrano (2002) study information transmission in decentralized markets. 2 In contrast to these two papers, equilibrium behavior in our market example leads to full revelation of information

For globally systemically important banks (GSIBs) with US headquarters, we find significant reductions in market-implied probabilities of government bailout after the Global Financial Crisis (GFC), along with roughly 170 percent higher wholesale debt financing costs for these banks after controlling for insolvency risk. Since the GFC, bank creditors appear to expect much larger losses in the event that a GSIB approaches insolvency. In this sense, we estimate a decline of “too big to fail.” (G01, G12, G21, G28, G33, H81)