OTC Intermediaries

A Structural Model of Interbank Network Formation and Contagion

“…There is an extensive literature on the effect of network structure on outcomes in financial markets, both theoretical (Acemoglu et al, 2015;Ballester et al, 2006;Elliott et al, 2014) and empirical (Denbee et al, 2017;Eisfeldt et al, 2018;Gofman, 2017;Iyer and Peydro, 2011) The empirical literature typically takes as given the financial network, and then studies how exogenous changes in the network's structure affect outcomes like risk and total surplus, as well as identifying systemically important banks within the network. By explicitly modelling the formation of the network, our contribution is to more clearly identify network effects and consider more realistic counterfactual scenarios.…”

“…This also enables us to better identify systemically important banks. Regarding counterfactuals, various papers (Eisfeldt et al (2018) and Gofman (2017), for example) adjust the network arbitrarily (usually by simulating a failure) and show the impact on market outcomes holding network structure otherwise fixed. In our model, network structure responds endogenously to such a change.…”

“…In this respect, our data has two important advantages over many of the data used in the literature. First, our dataset is the closest available representation of total exposures, when most other empirical assessments of interbank connections rely on a single instrument, such as CDS (Eisfeldt et al, 2018) or overnight loans (Denbee et al, 2017). Second, our data are on exposures, rather than simply market value, in that when banks report their exposures they account for collateral and regulatory adjustments.…”

“…For example, if our empirical exposures were uniquely debt, then we would be able to include a standard model of liquidity management on the demand-side (as in Denbee et al (2017)). If instead our empirical exposures were uniquely CDS contracts, then we would be able to include a model of credit risk management (as in Eisfeldt et al (2018)). Instead, we need to model the demand-side in a general way that is applicable across the range of financial products that feature in our data.…”

Networks in Production: Asset Pricing Implications

“…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.…”