Working papers

  • Social and Economic Distancing

    with Guillermo Ordonez

    Dealing with pandemics, such as the recent COVID-19 virus, has highlighted the critical role of social distancing to avoid contagion and deaths. New technologies that allow replacing in-person for at-distance activities have blurred the mapping between social and economic distancing. In this paper we model how individuals react to social distancing guidelines by changing their network of economic relations, affecting total output, wealth inequality, and long-term growth.

    ssrn   pdf

  • Interbank Networks in the Shadows of the Federal Reserve Act

    with Haelim Anderson and Guillermo Ordonez

    Central banks provide public liquidity (through lending facilities and promises of bailouts) with the intent to stabilize the financial system. Even though this provision is restricted to member (regulated) banks, an interbank system can provide indirect access to nonmember (shadow) banks. We construct a model to understand how a banking network may change in the presence of central bank interventions and how those changes affect financial fragility. We provide evidence showing that the introduction of the Fed’s liquidity provision in 1913 increased systemic risk through three channels; it reduced aggregate liquidity, created a new source of financial contagion, and crowded out private insurance for smoothing cross-regional liquidity shocks (manifested through the geographic concentration of networks).

    ssrn   pdf

  • Network Hazard and Bailouts

    (Best job market paper runner-up prize, Finance Theory Group)

    This paper studies a model of firms with endogenous bilateral exposures and government bailouts. It is shown that the anticipation of bailouts makes firms less concerned with the counterparty choices of their counterparties. This “network hazard” gives rise to large central firms. Bailouts can mitigate contagion but they can not restore output losses. Consequently, idiosyncratic bad shocks to large central firms generate large welfare losses. As such, bailouts create welfare volatility and systemic risk. Surprisingly, moral hazard on risk-return dimension is mitigated by bailouts. Ex-ante regulations can induce discontinuous changes in the network.

    ssrn   pdf

  • Insider Networks

    with Michael Junho Lee

    How do insiders respond to regulatory oversight? History suggests that they form sophisticated networks to share information and circumvent regulation. We develop a theory of the formation and regulation of information transmission networks. We show that agents with sufficiently complex networks bypass any given regulatory environment. In response, regulators employ broad regulatory boundaries to combat gaming, giving rise to regulatory ambiguity. Tighter regulation induces agents to migrate transmission activity from existing social networks to a core-periphery insider network. A small group of agents endogenously arise as intermediaries for the bulk of information. We provide centrality measures that identify intermediaries.

    ssrn   pdf

  • Network Formation and Systemic Risk

    with Rakesh Vohra

    This paper introduces a simple model of endogenous network formation and systemic risk. In the model, firms form joint ventures called ‘links’ which are subsequently subjected to shocks that are either good or bad. Bad shocks incentivize default. Links yield full benefits only if the counterparty does not subsequently default on the project. Accordingly, defaults triggered by bad shocks render firms insolvent and defaults propagate via links. The model yields three insights. First, stable networks with ex-ante identical agents exhibit a core-periphery structure. Second, an increase in the probability of good shocks increases systemic risk. Third, the network formed critically depends on the correlation between shocks to links. As a consequence, an observer who misconceives the correlation will significantly underestimate the probability of systemwide default.

    ssrn   pdf

  • Contagion in Graphons

    with Francesca Parise and Alexander Teytelboym

    We consider a threshold contagion process over networks sampled from a graphon, which we interpret as a stochastic network formation model. We investigate whether the contagion outcome in the sampled networks can be predicted by only exploiting information about the graphon. To do so, we formally define a threshold contagion process on a graphon. Our main results show that contagion in large but finite sampled networks is well approximated by contagion in a graphon. We illustrate our results by providing analytical characterizations for the extent of contagion and for optimal seeding policies in graphons with finite and with infinite agent types.

    EC abstract