Benjamin Lester

Trading Dynamics in Decentralized Markets with Adverse Selection

The authors study a dynamic, decentralized lemons market with one-time entry and characterize its set of non-stationary equilibrium. This framework offers a theory of how a market suffering from adverse selection recovers over time endogenously; given an initial fraction of lemons, the model provides sharp predictions about how prices and the composition of assets evolve over time. Comparing economies in which the initial fraction of lemons varies, the authors study the relationship between the severity of the lemons problem and market liquidity. They use this framework to understand how asymmetric information contributed to the breakdown in trade of asset{backed securities during the recent financial crisis, and to evaluate the efficacy of one policy that was implemented in attempt to restore liquidity.

Directed search with multi-vacancy firms

I construct a directed search model in which firms decide whether to enter a market and how many positions to create. Within this framework, the number of firms and the size of each firm are determined endogenously, wages play an allocative role in the matching process, and the frictions inherent in this process derive from the equilibrium behavior of workers and firms. I characterize the (unique) equilibrium. Comparative statics generate testable implications for cross-sectional variation in matching efficiency, as well as the dynamic behavior of vacancies and unemployment. Moreover, allowing for ex-ante heterogeneity across firms, the model can easily and naturally generate the observed relationship between firm size, wages, profitability, and hiring.

Information, Liquidity and Asset Prices

We study economies with multiple assets that are valued both for their return and liquidity. Exchange occurs in decentralized markets with frictions making a medium of exchange essential. Some assets are better suited for this role because they are more liquid - more likely to be accepted in trade - even if they have a lower return. The reason assets are more or less likely to be accepted is modeled using informational frictions, or recognizability. While everyone understands e.g. what currency is and what it is worth, some might be less sure about other claims. In our model, agents who do not recognize assets do not accept them in trade. Recognizability is endogenized by letting agents invest in information, potentially generating multiple equilibria with different liquidity. We discuss implications for asset pricing and for monetary policy. In particular, we show explicitly that what may look like a cash-in-advance constraint is not invariant to policy interventions or other changes in the economic environment

Competing with asking prices

In many markets, sellers advertise their good with an asking price. This is a price at which the seller will take his good off the market and trade immediately, though it is understood that a buyer can submit an offer below the asking price and that this offer may be accepted if the seller receives no better offers. Despite their prevalence in a variety of real world markets, asking prices have received little attention in the academic literature. We construct an environment with a few simple, realistic ingredients and demonstrate that using an asking price is optimal: it is the pricing mechanism that maximizes sellers’ revenues and it implements the efficient outcome in equilibrium. We provide a complete characterization of this equilibrium and use it to explore the positive implications of this pricing mechanism for transaction prices and allocations.

Heterogeneity in Decentralized Asset Markets

We study a search and bargaining model of an asset market, where investors’ heterogeneous valuations for the asset are drawn from an arbitrary distribution. Our solution technique renders the analysis fully tractable and allows us to provide a full characterization of the equilibrium, in closed-form, both in and out of steady-state. We use this characterization for two purposes. First, we establish that the model can naturally account for a number of stylized facts that have been documented in empirical studies of over-the-counter asset markets. In particular, we show that heterogeneity among market participants implies that assets are reallocated through “intermediation chains,�? ultimately producing a core-periphery trading network and non-trivial distributions of prices and trading times. Second, we show that the model generates a number of novel results that underscore the importance of heterogeneity in decentralized markets. We highlight two: First, heterogeneity magnifies the price impact of search frictions; and second, search frictions have larger effects on price levels than on price dispersion. Hence, quantifying the price discount or premium created by search frictions based on observed price dispersion can be misleading.

Competing for Order Flow in OTC Markets

We develop a model of a two-sided asset market in which trades are intermediated by dealers and are bilateral. Dealers compete to attract order flow by posting the terms at which they execute trades-- which can include prices, quantities, and execution speed--and investors direct their orders toward dealers that offer the most attractive terms. We characterize the equilibrium in a general setting, and illustrate how the model can account for several important trading patterns in over-the-counter markets which do not emerge from existing models. We then study two special cases which allow us to highlight the differences between these existing models, which assume investors engage in random search for dealers and then use ex post bargaining to determine prices, and our model, which utilizes the concept of competitive search in which dealers post terms of trade. Finally, we calibrate our model, illustrate that it generates reasonable quantitative outcomes, and use it to study how trading frictions affect the per-unit trading costs that investors pay in equilibrium.

Subsidizing Price Discovery

When markets freeze, not only are gains from trade left unrealized, but the process of information production through prices, or price discovery, is disrupted as well. Though this latter effect has received much less attention than the former, it constitutes an important source of inefficiency during times of crisis. We provide a formal model of price discovery and use it to study a government program designed explicitly to restore the process of information production in frozen markets. This program, which provided buyers with partial insurance against acquiring low-quality assets, reveals a fundamental trade-off for policymakers: while some insurance encourages buyers to bid for assets when they otherwise would not, thus promoting price discovery, too much insurance erodes the informational content of these bids, which hurts price discovery.

Screening and Adverse Selection in Frictional Markets

We incorporate a search-theoretic model of imperfect competition into a standard model of asymmetric information with unrestricted contracts. We characterize the unique equilibrium and use our characterization to explore the interaction between adverse selection, screening, and imperfect competition. We show that the relationship between an agent’s type, the quantity he trades, and the price he pays is jointly determined by the severity of adverse selection and the concentration of market power. Therefore, quantifying the effects of adverse selection requires controlling for market structure. We also show that increasing competition and reducing informational asymmetries can decrease welfare.

Excess Reserves and Monetary Policy Normalization

In response to the Great Recession, the Federal Reserve resorted to several unconventional policies that drastically altered the landscape of the federal funds market. The current environment, in which depository institutions are flush with excess reserves, has forced policymakers to design a new operational framework for monetary policy implementation. We provide a parsimonious model that captures the key features of the current federal funds market, along with the instruments introduced by the Federal Reserve to implement its target for the federal funds rate. We use this model to analyze the factors that determine rates and volumes as well as to identify the conditions such that monetary policy implementation will be successful. We also calibrate the model and use it as a quantitative benchmark for applied analysis, with a particular emphasis on understanding how the market is likely to respond as policymakers raise the target rate.

A Model of the Federal Funds Market: Yesterday, Today, and Tomorrow

The landscape of the federal funds market changed drastically in the wake of the Great Recession as large-scale asset purchase programs left depository institutions awash with reserves, and new regulations made it more costly for these institutions to lend. As traditional levers for implementing monetary policy became less effective, the Federal Reserve introduced new tools to implement the target range for the federal funds rate, changing this landscape even more. In this paper, we develop a model that is capable of reproducing the main features of the federal funds market, as observed before and after 2008, in a single, unified framework. We use this model to quantitatively evaluate the evolution of interest rates and trading volume in the federal funds market as the supply of aggregate reserves shrinks. We find that these outcomes are highly sensitive to the dynamics of the distribution of reserves across banks.