Search Theory in Financial Markets
January, 2016
Benjamin Lester
Summary.
Search theoretic models have become a popular tool for studying a variety of topics in finance for
at least three reasons.
1. First, a number of financial markets—often called over-the-counter or “OTC” markets—are
actually best described by a typical search and bargaining model; in these markets, investors
have to locate a counterparty themselves, and once they find a suitable trading partner the
two are free to trade at any mutually agreeable price. Hence, these models represent the most
natural framework to study prices and allocations in OTC markets, and how they depend on
the level of frictions in the environment.
2. Second, even in markets in which certain agents or institutions exist to intermediate most
trades, understanding which agents or the nature of these institutions often requires understanding the underlying frictions. In other words, understanding why a specific market microstructure has evolved often requires imagining a world without that microstructure, i.e.,
a world where an investor has to find someone on his own to buy his shares of IBM. Hence,
search theoretic models represent a natural starting point for theories of endogenous market
microstructure.
3. Lastly, another important source of inefficiency in financial markets—especially during the
recent crisis—is information frictions. While such frictions can sometimes be challenging
to study in standard, competitive models, search and matching models offer a more tractable
framework for understanding how asymmetric information at the microeconomic level ultimately affects outcomes at the macroeconomic level. The reason is simple: in a search and
matching framework, we model bilateral interactions explicitly. In studying these interactions, we can utilize all of the results and techniques from the voluminous game theoretic
literature that studies the effect of information frictions in two-player games. Hence, search
theoretic models represent a natural framework for studying the effects of information frictions on market outcomes.
In this mini-course, we will study several classes of models, and we will highlight all three of the
advantages of search theoretic models described above.
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Outline.
The structure of the course will be as follows.
Lecture 1: Basic Models of Decentralized Asset Markets. In the first half of the class, we will
develop the simplest possible model of a “pure” (or un-intermediated) decentralized asset market,
where investors with one of two valuations for an asset are randomly matched with each other and
engage in Nash bargaining if there are gains from trade. This benchmark model will be based on:
• Duffie et al. [2007].
In the second half of the class, we will introduce intermediaries or “marketmakers” into the framework developed above. One of the primary purposes of this model will be to develop a theory
of asset prices and the bid-ask spread based on search and bargaining frictions. The benchmark
model will be based on
• Duffie et al. [2005]
Lecture 2: More General Models of Decentralized Asset Markets. In this class, we will generalize the simple models of Duffie, Garleanu, and Pedersen to allow for arbitrary heterogeneity in
valuations across investors. We will then use this model to study the effect of search frictions on
asset prices, trading times, trading networks, and the nature and extent of misallocation relative to
a frictionless benchmark. This general model will be based on:
• Hugonnier et al. [2014].
If we have time, we will discuss a few examples of how models of decentralized asset markets
have been applied to real-world issues. Suggested readings include:
• Vayanos and Weill [2008].
• Gavazza [2011].
• Afonso and Lagos [2014].
Lecture 3: Adverse Selection in Search Models (part 1). In this class, we will first introduce
the standard model of directed (or competitive) search, as formulated in:
• Burdett et al. [2001].
Then we will study how to introduce adverse selection into the competitive search paradigm, as in:
• Guerrieri et al. [2010].
Applications of this framework to financial markets can be found in
• Guerrieri and Shimer [2014]
• Chang [2012].
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Lecture 4: Adverse Selection in Search Models (part 2).
an alternative model of search frictions due to:
In the final class, we will introduce
• Burdett and Judd [1983]
Then we will show how to introduce adverse selection into this model, as in:
• Lester et al. [2015]
References
Gara Afonso and Ricardo Lagos. Trade dynamics in the market for federal funds. Econometrica,
(forthcoming), 2014.
Kenneth Burdett and Kenneth L Judd. Equilibrium price dispersion. Econometrica, 51(4):955–69,
1983.
Kenneth Burdett, Shouyong Shi, and Randall Wright. Pricing and matching with frictions. Journal
of Political Economy, 109(5):1060–1085, 2001.
Briana Chang. Adverse selection and liquidity distortion in decentralized markets. Working Paper,
University of Wisconsin, Madison, 2012.
Darrell Duffie, Nicolae Gârleanu, and Lasse H. Pedersen. Over-the-Counter Markets. Econometrica, 73(6):1815–1847, 2005.
Darrell Duffie, Nicolae Gârleanu, and Lasse H. Pedersen. Valuation in over-the-counter markets.
Review of Financial Studies, 20(6):1865, 2007.
Alessandro Gavazza. The role of trading frictions in real asset markets. The American Economic
Review, 101(4):1106–1143, 2011.
Veronica Guerrieri and Robert Shimer. The American Economic Review, 104(7), 2014.
Veronica Guerrieri, Robert Shimer, and Randall Wright. Adverse selection in competitive search
equilibrium. Econometrica, 78(6):1823–1862, 2010.
Julien N Hugonnier, Benjamin R Lester, and Pierre-Olivier Weill. Heterogeneity in decentralized
asset markets. Swiss Finance Institute Research Paper, (14-67), 2014.
Benjamin Lester, Ali Shourideh, Venky Venkateswaran, and Ariel Zetlin-Jones. Screening and
adverse selection in frictional markets. working paper, Federal Reserve Bank of Philadelphia,
2015.
Dimitri Vayanos and Pierre-Olivier Weill. A Search-Based Theory of the On-the-Run Phenomenon. Journal of Finance, 63(3):1361–1398, 2008.
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