Abstract
Deposit insurers are particularly concerned about high-cost failures. When the factors driving such failures differ systematically from the determinants of low- and moderate-cost failures, a new estimation technique is required. Using a sample of more than 1,000 bank failures in the U.S. between 1984 and 2003, I present a quantile regression approach that illustrates the sensitivity of the dollar value of losses in different quantiles to my explanatory variables. These findings suggest that reliance on standard econometric techniques results in misleading inferences, and that losses are not homogeneously driven by the same factors across the quantiles. I also find that liability composition affects time to failure.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The Depositor Preference Act (1993) shifts the burden of bank failure from taxpayers to uninsured depositors. Several states had such laws in place prior to 1993 (Osterberg 1996). Such laws gives depositors claims on a failed bank’s assets superior to those of other creditors.
In unreported regressions, I performed sensitivity tests that restrict the sample until 1996 because the Fed funds variable is grouped together with repurchase agreements during the 1997–2003 period. Since repos are treated very differently from Fed funds in a bank receivership, examining a potential bias is important. However, the results from these additional sensitivity tests for both the quantile regressions and the hazard rate models are virtually identical and can be obtained from the author on request.
Note that reliance on Call Report data on a quarterly basis from the report immediately prior to failure hampers separating out insured and uninsured deposits. The Call Report item containing information on deposit accounts with balances over 100,000 USD was only reported in June Call Reports prior to 1991 (Maechler and McDill 2006).
Quantiles divide the cumulative distribution function of a random variable into a number of equally sized segments. Quantiles are the general case of splitting a population into segments. For instance, quartiles divide a population into four segments with equal proportions of the reference population in each segment.
CAMEL is an acronym for components of a rating system employed to assess bank soundness: Capital adequacy, Asset quality, Management, Earnings, and Liquidity. The system has been augmented by adding a component that captures Sensitivity to market risk in 1997. The system is therefore now referred to as CAMELS rating system.
Neither the Consultative Document Pillar 3, (Basel Committee on Banking Supervision 2001a), nor the Working Paper on Pillar 3—Market discipline, (Basel Committee on Banking Supervision 2001b) mention disclosure rules with respect to financial institutions’ liability/deposit structure regarding their status of deposit insurance.
References
Ashcraft A (2003) Are banks really special? New evidence from the FDIC-induced failure of healthy banks. Federal Reserve Bank of New York Staff Reports, No. 176
Barth JR, Bartholomew PF, Bradley MG (1990) Determinants of thrift institution resolution costs. J Finance 45:731–754
Basel Committee on Banking Supervision (2001a) Consultative document: pillar 3 (market discipline). Bank for International Settlements, Basel
Basel Committee on Banking Supervision (2001b) Working paper on pillar 3—market discipline. Bank for International Settlements, Basel
Billet MT, Garfinkel JA, O, Neal ES (1998) The cost of market versus regulatory discipline in banking. J Financ Econ 48:333–358
Blalock J, Curry T, Elmer P (1991) Resolution costs of thrift failures. FDIC Bank Rev 4:15–26
Bovenzi JF, Murton AJ (1988) Resolution costs of bank failures. FDIC Bank Rev 1:1–13
Brown RA, Epstein S (1992) Resolution costs of bank failures: an update of the FDIC historical loss model. FDIC Bank Rev 5:1–16
Cole RA, Gunther JW (1995) Separating the likelihood and timing of bank failure. J Bank Financ 19:1073–1089
Davenport A, McDill K (2006) The depositor behind the discipline: a micro-level case study of Hamilton Bank. J Financ Serv Res 30:93–109
Goldberg LG, Hudgins SC (1996) Response of uninsured depositors to impending S&L failures: evidence of depositor discipline. Q Rev Econ Finance 36:311–325
Goldberg LG, Hudgins SC (2002) Depositor discipline and changing strategies for regulating thrift institutions. J Financ Econ 63:263–274
Gup BE (1995) Targeting fraud: uncovering and deterring fraud in financial institutions. Probus, Chicago
Hall JR, King TB, Meyer AP, Vaughan MD (2002) Did FDICIA enhance market discipline? A look at evidence from the jumbo-CD market. Federal Reserve Bank of St. Louis Supervisory Policy Analysis Working Paper No. 2002-04
Hirschhorn E, Zervos D (1990) Policies to change the priority of claimants: the case of depositor preference laws. J Financ Serv Res 4:111–125
James C (1991) The losses realized in bank failures. J Finance 46:1223–1242
Jones KD, Oshinsky RC (2007) Effects of industry consolidation and deposit insurance reform on the resiliency of the U.S. bank insurance fund. Federal Deposit Insurance Corporation, Mimeo
Jordan JS (2000) Depositor discipline at failing banks. N Engl Econ Rev, March/April, pp 15–28
Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46:33–50
Koenker R, Hallock KF (2001) Quantile regression. J Econ Perspect 15:143–156
Maechler AM, McDill K (2006) Dynamic depositor discipline in US banks. J Bank Financ 30:1871–1891
Marino JA, Bennett RL (1999) The consequences of national depositor preference. FDIC Bank Rev 12:19–38
McDill K (2004) Resolution costs and the business cycle. FDIC Working Paper 2004-01
Osterberg WP (1996) The impact of depositor preference laws. Federal Reserve Bank of Cleveland Economic Review 12:1–12
Osterberg WP, Thomson JB (1994) Underlying determinants of closed-bank resolution costs. Federal Reserve Bank of Cleveland Working Paper 9403
Park S, Peristiani S (1998) Market discipline by thrift depositors. J Money Credit Bank 30:347–364
Pennacchi GG (2005) Risk-based capital standards, deposit insurance, and procyclicality. J Financ Intermed 14:432–465
Shibut L (2002) Should bank liability structure influence deposit insurance pricing? FDIC Working Paper 2002–01
Shibut L, Critchfield T, Bohn S (2003) Differentiating among critically undercapitalized banks and thrifts. FDIC Bank Rev 15:1–38
Author information
Authors and Affiliations
Corresponding author
Additional information
This research was undertaken during my stay as a visiting scholar at the Department of Finance at the University of Illinois at Urbana-Champaign. This study has benefited from valuable guidance by George Pennacchi. I am indebted to Christopher James, Rosalind Bennett, Lynn Shibut, and Philip Shively for sharing their extensive expertise, and to the editor, Haluk Unal, and an anonymous referee for their helpful comments. I also would like to thank Roger Koenker, Carlos Ramirez, Masaki Yamada, Martin Cihak, Evangelos Benos, Simon Wolfe, conference participants at the 6th Annual Bank Research Conference in Arlington, Virginia, and seminar participants at the University of Southampton for their suggestions. Sandra Sizer provided editorial assistance. All remaining errors are my own.
Rights and permissions
About this article
Cite this article
Schaeck, K. Bank Liability Structure, FDIC Loss, and Time to Failure: A Quantile Regression Approach. J Finan Serv Res 33, 163–179 (2008). https://doi.org/10.1007/s10693-008-0028-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10693-008-0028-5