Abstract: In reality, we find assets traded in the transparent centralized market and opaque decentralized market. To explain the traders' choices of venues, we develop a model of dynamic learning and dynamic market choice between the centralized market and decentralized markets. We find that when traders' value correlation is moderately heterogeneous, and asset values are insensitive to shocks to fundamentals or shocks are predictable, switching between centralized and decentralized markets can be the optimal market choice. When asset values are sensitive to volatile fundamentals, assets are traded only in the centralized market. We provide empirical evidence in support of the model predictions. The model allows us to explore the impact of introducing transparency designs in the opaque decentralized market on traders' market choices and welfare. We find that post-trade transparency makes the choice of a decentralized market persistent. Regardless of its impact on market structure, post-trade transparency improves welfare. Surprisingly, pre-trade transparency may decrease welfare as it increases traders' incentives to choose a decentralized market earlier and hurts centralized market welfare.
Discussant: Chaojun Wang, University of Pennsylvania
Abstract: This paper studies equilibrium order book formation in a limit-order market by building a search-theoretic model where the shape of the order book and its spread are determined jointly in equilibrium. The model characterizes liquidity as a function of differences in valuation between sellers and buyers, beliefs about the probability distribution of the arrival of buyers and sellers, exchange fees, and preference parameters like patience and beliefs about the expected lifespan of information. The efficiency of the market is characterized as a function of these parameters. We demonstrate how to extract the model’s parameters from order book data and, using a sample of data from Coinbase’s Bitcoin/U.S. Dollar exchange, characterize market liquidity as a function of these factors. We derive the optimal timing of frequent batch auctions in our model and show how this timing can be calculated for all limit-order markets.
Abstract: We model investors' allocation of order flow across over-the-counter dealers jointly with dealers' costly acquisition of expertise that can be used to take advantage of investors across transactions. Ceteris paribus, investors prefer to allocate their order flow to dealers expected to intermediate large volumes of transactions and to acquire low levels of expertise, whereas dealers' benefits from acquiring expertise grow with the number of transactions they intermediate. Our model's equilibrium rationalizes why the most sought-after dealers often are those with the best data, technology, and skills, despite the significant adverse selection concerns triggered by their superior expertise.
Discussant: Batchimeg Sambalaibat, Princeton University
Abstract: In over-the-counter (OTC) securities markets, interdealer markets are an important venue through which dealers can offload positions and share risk amongst themselves. Contrary to the popular conception that search frictions matter the most in OTC markets, we find that in the interdealer market for U.S. corporate bonds, information frictions are most relevant. Large dealers face large and informed customers and pay more than small dealers to transact in the interdealer market, despite on average providing liquidity to other dealers. Large dealers tend to trade through interdealer brokers (IDBs) to mitigate information leakage, but interdealer markets are still far from efficient.
Discussant: Xingtan Zhang, Cheung Kong Graduate School of Business
