This paper simulates the effects of credit risk, changes in capital requirements and price shocks on the Brazilian banking system. We perform the analysis within the context of a model that integrates data on bilateral exposures in the interbank market with information about the liquidity profile of each financial institution. Asset prices in the model are determined endogenously as a function of the total volume of fire sales, thus creating the possibility that marking to market may trigger new rounds of fire sales and downward asset price spirals. The simulation results show that the Brazilian banking system is robust, as relatively large increases in the NPL ratio lead to only modest losses in the system. We also compute the contribution of each financial institution to systemic losses under severe shocks and find that contributions from medium-sized banks can be significant. However, if shocks become more severe, only large banks will contribute significantly to systemic losses.

We investigate the effectiveness of bail-in mechanisms in mitigating systemic risk and welfare costs to society during resolution processes. To perform this study, we define a network model of mutually exposed banks and use it to simulate the effects of shocks to these banks using granular data of the Brazilian banking system, its interbank exposures and credit register operations. In the simulations, we compare the outcomes of these initial shocks after resolution processes with and without bail-ins. The simulations show that by avoiding the liquidation of banks and the resulting interruption in their credit provision, bail-ins would be effective in preventing the amplification of losses imposed on the real sector. Analyzing the effects that the liquidation of Brazilian Domestic Systemically Important Banks (D-SIBs) would cause to credit provision to economic sectors, we find bailing in these banks would produce a relevant decrease in credit crunches. However, in our sample, only a few banks could benefit from bail-ins due to insufficiency of bail-inable instruments.

We examine how COVID-19 and digitalization have changed bank lending behavior. Using microdata from Brazil, we investigate the determinants of these changes at the bank branch level and by credit type. Branches in areas more affected by COVID-19 reduced loan issuances and experienced lower credit revenues. These branches could not adjust their costs in the short term due to this decline in lending, resulting in increased marginal costs. We also find that branches of more digitalized banks were less sensitive to local borrowers’ conditions and could expand their clientele. These branches extended credit to borrowers in remote localities less affected by COVID-19, positioning themselves better than branches of less digitalized banks. Our research highlights the critical role of digitalization in distressed periods, as it enables banks to respond more swiftly and effectively, favoring financial stability.

Capturing financial network linkages and contagion in stress test models are important goals for banking supervisors and central banks responsible for micro- and macroprudential policy. However, granular data on financial networks is often lacking, and instead the networks must be reconstructed from partial data. In this paper, we conduct a horse race of network reconstruction methods using network data obtained from 25 different markets spanning 13 jurisdictions. Our contribution is two-fold: first, we collate and analyze data on a wide range of financial networks. And second, we rank the methods in terms of their ability to reconstruct the structures of links and exposures in networks.