Are the Causes of Bank Distress Changing? Can Researchers Keep Up?

King, Thomas B.; Nuxoll, Daniel A.; Yeager, Timothy J.

doi:10.2139/ssrn.631241

Cited by 30 publications

(14 citation statements)

References 36 publications

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“…The sample is described in detail in a later section of this article. 5 For reviews of the literature, see Demirgüç-Kunt (1989), Jagtiani et al (2003), and King et al (2005). 6 See Whalen (1991), Cole and Gunther (1998), Kolari et al (2002), and Jagtiani et al (2003).…”

Section: Methodsmentioning

confidence: 99%

Troubled Banks: Why Don't They All Fail?

Oshinsky

Olin

2005

SSRN Journal

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Section: Methodsmentioning

confidence: 99%

Troubled Banks: Why Don't They All Fail?

Oshinsky

Olin

2005

SSRN Journal

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“…In sum, we suggest that future hazard rate studies should attempt to model dierent degrees of distress more explicitly. Monitoring and promoting the stability and soundness of banking systems is one of the major concerns of international policy makers (see for example the Bank for International Settlements (1999;2004), the European Central Bank (2005), the International Monetary Fund (Caprio and Klingebiel, 1996;Dell'Ariccia et al, 2005), the Bank of England (Hoggarth et al, 2004), Deutsche Bundesbank (2005) or the Federal Deposit Insurance Scheme (King et al, 2005)). Beginning with the work of Sinkey (1975), Martin (1977) and Altman (1977) on failures of U.S. banks numerous studies therefore seek to predict probabilities of bank default on the basis of nancial data.…”

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confidence: 99%

“…The authors compare supervisory CAMEL bank ratings to the market's evaluation inherent in subordinated debt risk spreads of the parent banking rm. 3 For the case of U.S. banks, CAMEL ratings result from both o-site nancial information as well as expert evaluations formed during on-site inspections (King et al, 2005). Hence, the authors hypothesize that condential CAMEL supervisory ratings contain additional information compared to evaluations of other market monitors, such as rating agencies.…”

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confidence: 99%

“…However, whether estimated probabilities account for the ordinal nature of the observed event of interest depends on the previously discussed denition of distress in the rst place. While U.S. supervisors' CAMEL ratings may include valuable private information, it subsequently represents an addition to explanatory variables in a dichotomous failure model instead of a more dierentiated denition of bank distress events on the left-hand side (King et al, 2005). Second, the approach by DeYoung et al (2001) to regress public nancial information on condential CAMEL ratings is infeasible for most European banking markets since, in contrast to the U.S., the vast majority of banking rms are not traded publicly.…”

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confidence: 99%

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Slippery Slopes of Stress: Ordered Failure Events in German Banking

Kick

Koetter

2007

SSRN Journal

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Outright bank failures without prior indication of nancial instability are very rare. Supervisory authorities monitor banks constantly. Thus, they usually obtain early warning signals that precede ultimate failure and, in fact, banks can be regarded as troubled to varying degrees before outright closure. But to our knowledge virtually all studies that predict bank failures neglect the ordinal nature of bank distress. Exploiting the distress database of the Deutsche Bundesbank we distinguish four dierent distress events that banks experience. Only the worst entails a bank to exit the market. Weaker orders of distress are, rst, compulsory notications of the authorities about potential problems, second, corrective actions such as warnings and hearings and, third, actions by banking pillar's insurance schemes. Since the four categories of hazard functions are not proportional, we specify a generalized ordered logit model to estimate the respective probabilities of distress simultaneously. Our model estimates each set of probabilities with high accuracy and conrms, rst, the necessity to account for dierent kinds of distress events and, second, the violation of the proportional odds assumption implicit in most limited dependent analyses of bank failure.Keywords: Bank, failure, distress, generalized ordered logit JEL: C35, G21, G33, K23, L50 Non-technical summary Bank insolvencies are dierent from failures of non-nancial rms because they may disrupt trust in the banking system as a whole. This explains why banks are subject to more regulatory scrutiny compared to most other industries. Supervisors therefore monitor banks on an ongoing basis to ensure the nancial safety and soundness of individual banks and the system. To this end, they use among other things o-site monitoring systems, which rely mostly on bank-specic nancial data reported to the authorities. These so-called hazard rate models predict the probability of default of individual banks on the basis of historical nancial and default data.Obviously, the denition of default is crucial to such analyses. On the one hand, banks are subject to clear-cut failure denitions, such as minimum capital requirements stipulated in laws and regulations. On the other hand, the systemic dangers of bank defaults in conjunction with an ongoing supervision process suggest that regulators, and practitioners, may regard a bank as troubled much earlier than ultimate default in the form of (forced) market exit occurs. We argue in this paper that most bank hazard studies neglect this ordinal nature of what we prefer to dene as distress rather than default.We address the issue by using data on a number of distress events that German universal banks from all three pillars experienced between 1994 and 2004. We categorize these events into four groups of ascending degrees of distress, ranging from early warning information issued by banks to regulators to absorbing events such as closure and restructuring mergers. To account for this pecking order of events, we specify a genera...

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“…CAMEL(S) theory may be viewed as a progressive update of the theory of bank liquidity market failure that includes the major risks in the entire bank balance sheet (King et al, 2006). The CAMEL framework essentially provides bank regulators with off-site surveillance basis for bank rating (Gilbert et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

Size Effect, Seasonality, Attitude to Risk and Performance of Egyptian Banks

Alber¹

2013

IBR

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This paper aims at analyzing the effects of "size", "seasonality" and "attitude to risk" on the performance Egyptian banks. This has been conducted using a sample of 10 banks, and covering the period from the first quarter2003 to the fourth quarter 2011.Results indicate that, hypotheses regarding the significance of differences between performance indicators, according to "size", "seasonality" and "attitude to risk" on the performance Egyptian banks could be accepted.Also, robustness check assures the significance of these effects, where indicators of both capital adequacy and earnings are affected by both of size and attitude to risk, while asset quality is affected by size and seasonality.

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Are the Causes of Bank Distress Changing? Can Researchers Keep Up?

Cited by 30 publications

References 36 publications

Troubled Banks: Why Don't They All Fail?

Troubled Banks: Why Don't They All Fail?

Slippery Slopes of Stress: Ordered Failure Events in German Banking

Size Effect, Seasonality, Attitude to Risk and Performance of Egyptian Banks

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