We simulate the impact on the nonbank liabilities of banks in a multiplex interbank environment arising from changes in currency exposure. Currency shocks as a source of financial contagion in the banking sector have not, so far, been considered. Our model considers two sources of contagion: shocks to nonbank assets and exchange rate shocks. Interbank loans can mature at different times. We demonstrate that a dominant currency can be a significant source of financial contagion. We also find evidence of asymmetries in losses stemming from large currency depreciations versus appreciations. A variety of scenarios are considered allowing for differences in the sparsity of the banking network, the relative size and number of banks, changes in nonbank assets and equity, the possibility of bank breakups, and the dominance of a particular currency. Policy implications are also drawn.

We analyze the effects on financial stability of the interplay between climate transition risk and market conditions, such as recovery rate and asset price volatility. To this end, we extend the framework of the climate stress-test of the financial system by including an ex-ante network valuation of financial assets which accounts for asset price volatility as well as for endogenous recovery rate on interbank assets. Moreover, we also consider the dynamics of indirect contagion of banks and investment funds, which are key players in the low carbon transition, via exposures to the same asset classes. We derive some analytical results and we apply the model to a unique supervisory dataset in a range of climate policy scenarios and market conditions. In the event of a disorderly low-carbon transition, stronger market conditions allow to reach more ambitious climate policies at the same level of financial risk.

The coronavirus (COVID-19) pandemic halted economic activity worldwide, hurting firms and pushing many of them toward bankruptcy. This paper discusses four central issues that have emerged in the academic and policy debates related to firm financing during the downturn. First, the economic crisis triggered by the pandemic is radically different from past crises, with important consequences for optimal policy responses. Second, it is important to preserve firms’ relationships with key stakeholders (e.g., workers, suppliers, customers, and creditors) to avoid inefficient bankruptcies and long-term detrimental economic effects. Third, firms can benefit from “hibernation,” incurring the minimum bare expenses necessary to withstand the pandemic while using credit to remain alive until the crisis subdues. Fourth, the existing legal and regulatory infrastructure is ill-equipped to deal with an exogenous systemic shock like a pandemic. Financial sector policies can help channel credit to firms, but they are hard to implement and entail different trade-offs.

In November 2011, the Financial Stability Board, in collaboration with the International Monetary Fund, published a list of 29 "systemically important financial institutions" (SIFIs, now referred to as "globally systemically important banks" or G-SIBs), institutions whose failure, by virtue of "their size, complexity, and systemic interconnectedness", could have dramatic negative consequences for the global financial system. While "size" and "interconnectedness" have been the subject of much quantitative analysis, less attention has been paid to measuring "complexity." Yet without a consistent way to measure complexity, there is little guarantee that the designated SIFIs capture the complexity that the FSB is concerned about, and little hope of mitigating the consequences that the FSB warns of. In this paper we propose the structure of an individual firm's majority-control hierarchy as a proxy for institutional complexity. We demonstrate as a proof-of-concept how this method might be used by bank supervisors, particularly the Federal Reserve under its authority as consolidated supervisor, using a data set containing information on the majority-control hierarchies of many of the designated SIFIs. Our mathematical intrafirm network representation (and various associated metrics we propose) provides a uniform way to compare firms with often very disparate organizational structures – one that is distinct from a simple size comparison.

In financial stability, it is essential to know the determinants of interest rates in interbank markets because they are important vehicles for liquidity allocation among banks and are relevant for monetary policy transmission. Recent research indicates that banks with excess liquidity exercise their market power by rationing liquidity during periods of financial stress. This confirms the value of knowing the banks connections and identifying liquidity spreaders in such markets to manage contagion risk, liquidity hoarding and to preserve financial stability. In addition to well studied bank features such as size, liquidity and credit risk, we study which network metrics relate to interest rates during different periods. Using transaction level data on unsecured and secured lending, we apply an approach that employs network theory, econometric models and machine learning to analyze the structural properties of the secured and unsecured interbank markets in Mexico. Our findings support the “too-interconnected-to-fail” hypothesis. In the secured interbank market, PageRank shows a relationship with interest rates, while metrics associated with the notion of influence and systemic risk (Katz and DebtRank) are relevant in the unsecured interbank market. In general, a bank with high centrality lends at higher rates and gets funding at lower rates.