Decentralized Exchange

Malamud, Semyon; Rostek, Marzena

doi:10.1257/aer.20140759

Cited by 152 publications

(82 citation statements)

References 37 publications

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“…2 Our paper is also related to the growing literature that studies equilibrium asset pricing and exchange in exogenously specified trading networks. Recent work includes Gofman (2010), Babus and Kondor (2012), Malamud and Rostek (2012), and Alvarez and Barlevy (2014). Atkeson, Eisfeldt, and Weill (2015) and Colliard and Demange (2014) develop hybrid models, blending ingredients from the search and the network literatures.…”

Section: Related Literaturementioning

confidence: 99%

Heterogeneity in Decentralized Asset Markets

Hugonnier¹,

Lester²,

Weill³

2015

SSRN Journal

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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 makes the model fully tractable and allows us to provide a full characterization of the unique equilibrium, in closed-form, both in and out of steady-state. Using this characterization, we first establish that the model generates aggregate trading patterns that are consistent with those observed in many over-the-counter asset markets. Then, we show that the model can replicate empirical regularities reported from micro-level data sets, including the relationships between the length of the intermediation chains through which assets are reallocated, the network centrality of the dealers involved in these chains, and the markup charged on the asset being passed along the chain. Finally, we show that heterogeneity magnifies the price impact of search frictions, and that this impact is more pronounced on price levels than on price dispersion. Hence, using observed price dispersion to quantify the effect of search frictions on price discounts or premia can be misleading. Julien Hugonnier Ecole Polytechnnique Fédérale de LausanneQuartier UNIL Dorigny Extranef #212 1015 Lausanne Switzerland and Swiss Finance Institute price discounts or premia can be misleading: price dispersion can essentially vanish while price levels are still far from their frictionless counterpart.Our model, which we formally describe in Section 2, starts with the basic building blocks of DGP. There is a measure one of investors who can hold either zero or one share of an asset in fixed supply. Investors have stochastic, time-varying utility types that generate heterogenous valuations for the asset. Each investor is periodically and randomly matched with another, and a transaction ensues if there are gains from trade, with prices being determined by Nash bargaining. Our point of departure from DGP is that we allow utility types to be drawn from an arbitrary distribution.Allowing for more than two types changes the nature of the analysis significantly, as it implies that individual investors now face ex ante uncertainty about the utility types of potential trading partners, and hence about the terms of trade. More precisely, the relevant state variable in our model is an infinite-dimensional object: the distributions of the utility types among investors that hold zero and one asset, respectively, over time.Despite this greater complexity, we show in Section 3 that the model remains fully tractable. In particular, we characterize the equilibrium, in closed form, both in and out of the steady state. This requires deriving explicit solutions for the joint distributions of asset holdings and utility types, and for investors' reservation values; both of these derivations are new to the literature. Moreover, in contrast to the usual guess-and-verify approach, we establish several elementary properties of reservation values directly-without making a priori assumptions on the direc...

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Section: Related Literaturementioning

confidence: 99%

Heterogeneity in Decentralized Asset Markets

Hugonnier¹,

Lester²,

Weill³

2015

SSRN Journal

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“…Our paper is also related to the growing literature that studies equilibrium asset pricing in exogenously specified trading networks. Recent work includes Gofman (2010), Babus and Kondor (Forthcoming), Alvarez and Barlevy (2014), Chang and Zhang (2015), and Malamud and Rostek (2017). Atkeson, Eisfeldt, and Weill (2015), Colliard and Demange (2014), Neklyudov and Sambalaibat (2017), and Colliard, Foucault, and Hoffmann (2018) develop hybrid models, blending ingredients from the search and the network literatures.…”

Section: Related Literaturementioning

confidence: 99%

Frictional Intermediation in Over-the-Counter Markets

Hugonnier

Lester

Weill

2019

The Review of Economic Studies

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We extend Duffie et al.’s (2005) search-theoretic model of over-the-counter (OTC) asset markets, allowing for a decentralized inter-dealer market with arbitrary heterogeneity in dealers’ valuations (or, equivalently, inventory costs). We develop a solution technique that makes the model fully tractable and allows us to derive, in closed form, theoretical formulas for key statistics analysed in empirical studies of the intermediation process in OTC markets. A calibration to the market for municipal bonds allows us to quantify important unobservable characteristics of this market, including the severity of search and bargaining frictions and the nature of heterogeneity across dealers. We use our calibrated model to study the effect of these market characteristics on total welfare and the distribution of gains from trade across customers and dealers.

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“…This paper is also closely related to the literature on the importance of sectoral shocks for economic aggregates. The multisector model developed in this 2 Recent contributions to the literature on networks and finance include Hou and Robinson (2006), Cohen, Frazzini, and Malloy (2008), , Carvalho (2010), Gofman (2011), Carvalho andGabaix (2013), Li and Schürhoff (2013), Ahern and Harford (2014), Aobdia, Caskey, and Ozel (2014), Carvalho and Voigtlander (2014), ), Farboodi (2014, Tahbaz-Salehi (2015a, 2015b), Carvalho and Grassi (2015), Neklyudov and Sambalaibat (2015), Babus and Parlatore (2016), Eisfeldt et al (2018), Babus (2016), Babus and Kondor (2016), Biggio and La'O (2016), Carvalho et al (2016), Pasten, Schoenle, and Weber (2016), Richmond (2016), Wu (2016), Babus and Hu (2017), Denbee et al (2017), Gofman (2017), Hollifield, Neklyudov, and Spatt (2017), Malamud and Rostek (2017), and Ozdagli and Weber (2017). Allen and Babus (2009) present a detailed review of network models applied to finance.…”

Section: A Related Literaturementioning

confidence: 99%

Networks in Production: Asset Pricing Implications

Herskovic

2018

The Journal of Finance

110

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In this paper, I examine asset pricing in a multisector model with sectors connected through an input‐output network. Changes in the network are sources of systematic risk reflected in equilibrium asset prices. Two characteristics of the network matter for asset prices: network concentration and network sparsity. These two production‐based asset pricing factors are determined by the structure of the network and are computed from input‐output data. Consistent with the model predictions, I find return spreads of 4.6% and −3.2% per year on sparsity and concentration beta‐sorted portfolios, respectively.

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Decentralized Exchange

Cited by 152 publications

References 37 publications

Heterogeneity in Decentralized Asset Markets

Heterogeneity in Decentralized Asset Markets

Frictional Intermediation in Over-the-Counter Markets

Networks in Production: Asset Pricing Implications

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