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We study liquidity conditions in the corporate bond market during the COVID-19 pandemic. We document that the cost of trading immediately via risky-principal trades dramatically increased at the height of the sell-off, forcing customers to shift toward slower agency trades. Exploiting eligibility requirements, we show that the Federal Reserve’s corporate credit facilities have had a positive effect on market liquidity. A structural estimation reveals that customers’ willingness to pay for immediacy increased by about 200 bps per dollar of transaction, but quickly subsided after the Fed announced its interventions. Dealers’ marginal cost also increased substantially but did not fully subside.

We develop a parsimonious model to study the equilibrium and socially optimal decisions of banks to enter, trade in, and possibly exit, an OTC market. Although we endow all banks with the same trading technology, banks’ optimal entry and trading decisions endogenously lead to a realistic market structure comprised of dealers and customers with distinct trading patterns. We decompose banks’ entry incentives into incentives to hedge risk and incentives to make intermediation profits. We show that dealer banks enter more than is socially optimal. In the face of large negative shocks, they may also exit more than is socially optimal when markets are not perfectly resilient.

We study the reaction of financial markets to aggregate liquidity shocks when traders face cognition limits. While each financial institution recovers from the shock at a random time, the trader representing the institution observes this recovery with a delay reflecting the time it takes to collect and process information about positions, counterparties and risk exposure. Cognition limits lengthen the market price recovery. They also imply that traders who find that their institution has not yet recovered from the shock place market sell orders, and then progressively buy back at relatively low prices, while simultaneously placing limit orders to sell later when the price will have recovered. This generates round trip trades, which raise trading volume. We compare the case where algorithms enable traders to implement this strategy to that where traders can place orders only when they have completed their information processing task.

We propose a dynamic competitive equilibrium model of limit order trading, based on the premise that investors cannot monitor markets continuously. We study how limit order markets absorb transient liquidity shocks, which occur when a significant fraction of investors lose their willingness and ability to hold assets. We characterize the equilibrium dynamics of market prices, bid-ask spreads, order submissions and cancelations, as well as the volume and limit order book depth they generate.

We study how a continuum of agents learn about disseminated information by observing others’ actions. Every period each agent observes a public and private noisy signal centered around the aggregate action taken by the population. The public signal represents an endogenous aggregate variable such as a price or a quantity. The private signal represents the information gathered through private communication and local interactions. We identify conditions such that the average learning curve is S-shaped: learning is slow initially, intensifies rapidly, and finally converges slowly to the truth. We show that increasing public information always slows down learning in the long run and, under some conditions, reduces welfare. Lastly, optimal diffusion of information requires that agents “strive to be different”: agents need to be rewarded for choosing actions away from the population average.