The Term Auction Facility (TAF) was designed by the Federal Reserve during the financial crisis to inject emergency short-term funds into banks, as a supplement to the lender of last resort discount window offerings. We describe how the Federal Reserve altered the design of the Term Auction Facility (TAF) over the course of the financial crisis and examine the utilization of this stand-alone facility. Most specifically we detail the impact of the greatly increased offering amounts in all auctions after October 2008, which resulted in the facility no longer auctioning scarcely available funds. We also document significantly different usage of the facility by FDIC-insured community and non-community banks, consistent with the notion of a two-tiered banking system in the U.S. Community banks were far less likely to use the facility than larger, non-community banks.

This paper provides a theory of debt and hedging based on human capital. We distinguish human capital from physical capital in two ways: (1) human capital is inalienable and can exercise a one-sided option to leave the firm and (2) human capital is not perfectly replaceable. We show that a firm may reach the first best solution while issuing debt or equity to outsiders provided that either the insiders receive a senior claim or that the firm hedges. We then show that given asymmetric information concerning costs the only viable solution has the firm issuing debt to outsiders and hedging.

We examine the effects of structural change in the U.S. banking industry, as well as key regulatory changes, including recently enacted deposit insurance reform legislation, on the resiliency of the FDIC-administered bank insurance fund (BIF) by estimating and comparing the probability of BIF insolvency over time. We do this using a Markov-switching model that relies on historical patterns of BIF disbursements to define the probability of switching among three “states” of the banking industry's financial health. Monte Carlo simulations are then performed to project the financial condition of the BIF over a 50-year period. Our results indicate that the insolvency risk to the bank insurance fund has increased significantly due to industry consolidation, and is mainly due to the concentration of deposits in the 10 largest U.S. banking companies. We also find that recent deposit insurance reforms will cause only a marginal reduction in the risk of BIF insolvency. The increased risk associated with a more concentrated industry structure simply dominates the reform effect.

Assume data on Nj stock (asset) returns are available for p stocks, allowing us to construct approximate density functions f(xj) for (j=1, 2, …, p) from p empirical cumulative distribution functions (ECDFs). Our portfolio choice is designed to rank ECDF-induced, ill-behaved f(xj) densities subject to multiple modes, asymmetric fat tails, dips, turns, and numerous overlaps. Older portfolio theory assumes that parameters like the mean, variance, and percentiles fully describe f(xj). All six of our algorithms avoid (expected) utility theory. The only available algorithm by Anderson for order-k Stochastic Dominance (SDk) needs a trapezoidal approximation. Our new exact algorithm for SDk is based on ECDFs and overcomes pairwise comparisons. We include algorithms for statistical inference using the bootstrap and one for “pandemic proof” out-of-sample portfolio performance comparisons from our R package ‘generalCorr’. We suggest a test for “zero cost profitable arbitrage” and illustrate our algorithms in action by using two sets of recent 169-month stock returns. We do not claim to suggest new optimal portfolios.

We examine how community banks respond to liquidity shocks created by natural disasters. We address community banks’ responses to liquidity shocks due to their focused geographic and economic presence, which coincide with their communities’ exposure to the disasters and the ability of the local banks to meet their needs. We find that community banks respond to liquidity shocks by managing their balance sheet, rather than any single balance sheet account. In particular, we find that they respond to the liquidity needs of their communities by increasing loans as deposits are withdrawn.

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.

We estimate the effect of the spring 2023 failures of Silicon Valley Bank and Signature Bank on the “connectedness” of US bank stock return volatilities using the forecast error variance decomposition framework of Diebold and Yilmaz (2012, 2014) and Lastrapes and Wiesen (2021). Using split-sample and time-varying VAR methods, we find that those failures significantly increased spillovers across a sample of surviving regional banks, but had only small and temporary effects on spillovers across systemically important too-big-to-fail banks. Our main findings imply that regulatory policy toward systemically important banks has been credible but that additional oversight of regional banks should be considered.

Using global data on syndicated loans, we show that any negative effect of stronger creditor rights on loan spreads, as identified in the prior literature (Qian and Strahan, 2007; Bae and Goyal, 2009), disappears once we include a single country characteristic: country size. This finding is robust to several identification methods, both global samples and within-country changes in creditor rights, different panel spans, and hundreds of control variables. We identify that key origins of the effect of country size on loan pricing are ethnic fractionalization and within-country heterogeneity in economic preferences, which create country risk.

This paper compares three methods for assessing the contagion of risk among ten Globally Significant International Banks, known as GSIBs, listed on the New York Stock Exchange with daily and weekly data sets from 2007 to 2020, based on Machine Learning and Network Analysis. In particular we identify the banks which are the largest net sources or transmitters of risk, and net receptors of risk. We also examine the response of regulatory actions, in the form of fines and BIS Bin Classification for capital adequacy. Under alternative risk measures, of Range Volatility (RV) of share prices, Credit Default Swap (CDS) premia, and Conditional Value at Risk (ΔCoVar), there is a stronger and significant connection between Contagion and the BIS Bin classifications relative to the connections between Contagion and banking fines, either in the amount or frequency of the fines. These results show that BIS bin classifications respond positively to underlying signals of increased contagion in the form of Range Volatility (RV) and ΔCoVar measures but not to CDS risk premia.

This paper develops and estimates a dynamic stochastic general equilibrium (DSGE) model representing several key characteristics of Japan, namely, a large open economy, with large fiscal deficits and increasing amounts of debt held by domestic residents, through recurring sets of adverse shocks to productivity and positive shocks to government spending. We compare optimal simple rules for consumption tax rates, a Taylor rule with negative interest rates, and a quantitative easing rule, for reducing government debt held by the banking system, as well as optimizing welfare. In times of crisis, we show that the QE policy rule outperforms optimally-derived simple tax-rate rules or Taylor rules with negative interest rates for mitigating the costs of post-crisis adjustment and debt overhang.