Selman Erol
Tepper School of Business, Carnegie Mellon University
interventions and regulations in networks
Paper Types:
Interbank Networks in the Shadows of the Federal Reserve Act
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).
Best paper on financial institutions, Western Finance Association, 2020, sponsored by Elsevier
Regulating Clearing in Networks
Recent regulations in the U.S. and Europe incentivize the use of central counterparty clearing houses (CCP) to clear derivatives, arguably to create a less complex and more transparent interbank network that is less prone to financial instabilities. We construct a network model with endogenous exposures and show that the core and the periphery react asymmetrically to these regulations. The core values opacity more and adopts clearing less. Consequently, bilaterally netted exposures to the core increase. The regulation also makes the CCP more exposed to the core than the periphery was pre-regulation. This endogenous network reaction to the regulation creates the unanticipated effect of reducing financial stability through more frequent coordination failures that start at the core and spread to the periphery and the CCP. A novel dataset on U.S. counterparty exposures, before and after the regulations, confirm the model’s testable implications.
Insider Networks
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.
Civil Liberties and Social Structure
Governments use coercion to aggregate distributed information relevant to governmental objectives –from the prosecution of regime-stability threats to terrorism or epidemics–. A cohesive social structure facilitates this task, as reliable information will often come from friends and acquaintances. A cohesive citizenry can more easily exercise collective action to resist such coercion, however. We present an equilibrium theory where this tension mediates the joint determination of social structure and civil liberties. We show that segregation and unequal treatment sustain each other as coordination failures: citizens choose to segregate along the lines of an arbitrary trait only when the government exercises unequal treatment as a function of the trait, and the government engages in unequal treatment only when citizens choose to segregate based on the trait. We characterize when unequal treatment against a minority or a majority can be sustained, and how equilibrium social cohesiveness and civil liberties respond to the arrival of widespread surveillance technologies, shocks to collective perceptions about the likelihood of threats or the importance of privacy, or to community norms such as codes of silence.
Network Hazard and Bailouts
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.
Best job market paper runner-up prize, Finance Theory Group, 2016
Network Hazard
This paper introduces a novel form of moral hazard specific to networks and illustrates this concept using simple models from coordination games, epidemics, supply chains, and financial networks. In these models, agents form beneficial links that also propagate costly contagion. Endogenously, second-order contagion risk constrains the concentration of connections around central agents. Protective measures against contagion, such as vaccines, subsidies, or bailouts, mitigate contagion risk, subsequently increasing concentration. However, if these protective measures are imperfect or costly, shocks to central agents can result in greater harm and increased welfare variance, as evidenced in disease outbreaks, aggregate volatility, or financial crises.
Stability Pass-through to Shadow Banking
During the 1920-1921 recession, the Richmond Fed provided liquidity to its member banks to prevent a banking crisis. Using newly digitized data on interbank borrowing and deposits for Virginia state banks, we analyze how the Richmond Fed’s liquidity provision affected the interactions between the funding role and the payment role of the interbank system and financial stability. We show that the Richmond Fed’s liquidity provision enabled members to lend discount window liquidity to nonmembers that experienced large deposit outflows and prevented the mass withdrawal of interbank deposits. Interestingly, the banks with interbank borrowing reduced interbank deposits placed in lending banks, implying that these correspondents provided liquidity to nonmembers through both interbank loans and deposits. Our study shows that understanding the interaction between different types of networks is important to promote the stability of the banking system.
Network Hazard and Superspreaders
Higher availability and efficacy of protective measures against infectious diseases, such as vaccines, increases individuals’ propensity to socialize. Consequently, the number of visits to central points of interest (e.g., schools, gyms, grocery stores) and the rate of interactions with the agents employed therein (e.g., teachers, trainers, cashiers) increase. This opens more channels for the virus to transmit through the central agent or location. This leads to a manifestation of network hazard (Erol 2019). The infection rates can increase as protective measures become more effective and more available. Testable predictions of the theory are confirmed by the foot traffic data from 2019-2022 and historical COVID-19 vaccination and community transmission rates.
Network Reactions to Banking Regulations
Optimal regulatory restrictions on banks have to solve a delicate balance. Tighter regulations reduce the likelihood of banks’ distress. Looser regulations foster the allocation of funds toward productive investments. With multiple banks, optimal regulation becomes even more challenging. Banks form partnerships in the interbank lending market in order to face liquidity needs and to meet investment possibilities. We show that the interbank network can suddenly collapse when regulations are pushed beyond a critical level, with a discontinuous increase in systemic risk as the cross-insurance of banks collapses.
Contagion in Graphons
The analysis of threshold contagion processes in large networks is challenging. While the lack of accurate network data is often a major obstacle, finding optimal interventions is computationally intractable even in well-measured large networks. To obviate these issues we consider threshold contagion over networks sampled from a graphon—a flexible stochastic network formation model—and show that in this case the contagion outcome can be predicted by only exploiting information about the graphon. To this end, we exploit a second interpretation of graphons as graph limits to formally define a threshold contagion process on a graphon for infinite populations. We then show that contagion in large but finite sampled networks is well approximated by graphon contagion. This convergence result suggests that one can design interventions for large sampled networks by first solving the equivalent problem for an infinite population interacting according to the limiting graphon. We show that, under suitable regularity assumptions, the latter is a tractable problem and we provide analytical characterizations for the extent of contagion and for optimal seeding policies in graphons with both finite and infinite agent types.
Social and Economic Distancing
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.