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"Bank failures"
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How big banks fail and what to do about it
Dealer banks--that is, large banks that deal in securities and derivatives, such as J. P. Morgan and Goldman Sachs--are of a size and complexity that sharply distinguish them from typical commercial banks. When they fail, as we saw in the global financial crisis, they pose significant risks to our financial system and the world economy.How Big Banks Fail and What to Do about Itexamines how these banks collapse and how we can prevent the need to bail them out.
In sharp, clinical detail, Darrell Duffie walks readers step-by-step through the mechanics of large-bank failures. He identifies where the cracks first appear when a dealer bank is weakened by severe trading losses, and demonstrates how the bank's relationships with its customers and business partners abruptly change when its solvency is threatened. As others seek to reduce their exposure to the dealer bank, the bank is forced to signal its strength by using up its slim stock of remaining liquid capital. Duffie shows how the key mechanisms in a dealer bank's collapse--such as Lehman Brothers' failure in 2008--derive from special institutional frameworks and regulations that influence the flight of short-term secured creditors, hedge-fund clients, derivatives counterparties, and most devastatingly, the loss of clearing and settlement services.
How Big Banks Fail and What to Do about Itreveals why today's regulatory and institutional frameworks for mitigating large-bank failures don't address the special risks to our financial system that are posed by dealer banks, and outlines the improvements in regulations and market institutions that are needed to address these systemic risks.
eBook
Failure and Rescue in an Interbank Network
This paper is concerned with systemic risk in an interbank market, modelled as a directed graph of interbank obligations. This builds on the modelling paradigm of Eisenberg and Noe [Eisenberg L, Noe TH (2001) Systemic risk in financial systems.
Management Sci.
47(2):236-249] by introducing costs of default if loans have to be called in by a failing bank. This immediately introduces novel and realistic effects. We find that, in general, many different clearing vectors can arise, among which there is a greatest clearing vector, arrived at by letting banks fail in succession until only solvent banks remain. Such a collapse should be prevented if at all possible. We then study situations in which consortia of banks may have the means and incentives to rescue failing banks. This again departs from the conclusions of the earlier work of Eisenberg and Noe, where in the absence of default losses there would be no incentive for solvent banks to rescue failing banks. We conclude with some remarks about how a rescue consortium might be constructed.
This paper was accepted by Wei Xiong, finance.
Journal Article
Too big to fail
The potential failure of a large bank presents vexing questions for policymakers. It poses significant risks to other financial institutions, to the financial system as a whole, and possibly to the economic and social order. Because of such fears, policymakers in many countries--developed and less developed, democratic and autocratic--respond by protecting bank creditors from all or some of the losses they otherwise would face. Failing banks are labeled \"too big to fail\" (or TBTF). This important new book examines the issues surrounding TBTF, explaining why it is a problem and discussing ways of dealing with it more effectively. Gary Stern and Ron Feldman, officers with the Federal Reserve, warn that not enough has been done to reduce creditors' expectations of TBTF protection. Many of the existing pledges and policies meant to convince creditors that they will bear market losses when large banks fail are not credible, resulting in significant net costs to the economy. The authors recommend that policymakers enact a series of reforms to reduce expectations of bailouts when large banks fail.
eBook
Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis
For decades, the prediction of bank failure has been a popular topic in credit risk and banking studies. Statistical and machine learning methods have been working well in predicting the probability of bankruptcy for different time horizons prior to the failure. In recent years, bank efficiency has attracted much interest from academic circles, where low productivity or efficiency in banks has been regarded as a potential reason for failure. It is generally believed that low efficiency implies low-quality management of the organisation, which may lead to bad performance in the competitive financial markets. Previous papers linking efficiency measures calculated by Data Envelopment Analysis (DEA) to bank failure prediction have been limited to cross sectional analyses. A dynamic analysis with the updated samples is therefore recommended for bankruptcy prediction. This paper proposes a nonparametric method, Malmquist DEA with Worst Practice Frontier, to dynamically assess the bankruptcy risk of banks over multiple periods. A total sample of 4426 US banks over a period of 15 years (2002–2016), covering the subprime financial crisis, is used to empirically test the model. A static model is used as the benchmark, and we introduce more extensions for comparisons of predictive performance. Results of the comparisons and robustness tests show that Malmquist DEA is a useful tool not only for estimating productivity growth but also to give early warnings of the potential collapse of banks. The extended DEA models with various reference sets and orientations also show strong predictive power.
Journal Article
Déjà Vu All Over Again: The Causes of U.S. Commercial Bank Failures This Time Around
In this study, we analyze why commercial banks failed during the recent financial crisis. We find that traditional proxies for the CAMELS components, as well as measures of commercial real estate investments, do an excellent job in explaining the failures of banks that were closed during 2009, just as they did in the previous banking crisis of 1985–1992. Surprisingly, we do not find that residential mortgage-backed securities played a significant role in determining which banks failed and which banks survived. Our results offer support for the CAMELS approach to judging the safety and soundness of commercial banks, but call, into serious question the current system of regulatory risk weights and concentration limits on commercial real estate loans.
Journal Article