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Risk and risk-based capital of U.S. bank holding companies

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Abstract

This paper analyzes banks’ capital and risk-based capital (RBC) ratios as predictors of risk. Using quarterly data on U.S. bank holding companies (BHCs) from 1997 through 2010, we regress the capital and RBC ratios against six balance-sheet and market-based indicators of risk. Although the capital and RBC ratios are statistically significant predictors of BHCs’ levels of risk, we find the capital ratio is a statistically significantly better predictor of risk than the RBC ratio. This difference is strongest since the recent financial crisis beginning in 2007.

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Notes

  1. The factors that appear to have contributed to the housing boom include but are not limited to low interest rate policy by the Federal Reserve (Taylor 2008; White 2009), federal housing policy (Calomiris 2009; Justiniano et al. 2015), and the “global savings glut” of foreign capital investment (Bernanke 2005, 2012b, pp. 12–14).

  2. We refer to the Fed as the primary bank regulator. U.S. banks are also monitored by other agencies such as the Federal Deposit Insurance Corporation (FDIC) and the Office of the Comptroller of the Currency (OCC).

  3. We refer to the proposals issued by the Basel Committee on Bank Supervision (1988, 2004, 2010) as Basel I, II, and III, respectively.

  4. Some studies find that government deposit insurance caused excessive risk-taking behavior by S&Ls who gambled to make up for past losses in the knowledge that any losses to depositors would be covered by the FSLIC (Dotsey and Kuprianov 1990; Cebula 1993).

  5. A brief history of the Basel system can be found at http://www.bis.org/bcbs/history.htm.

  6. Unlike the other capital requirements, the SLR does appear to be a binding constraint for some BHCs. As of the time of this writing, however, it is too soon for the impact of the SLR to be fully known.

  7. See Board of Governors (2012) for a thorough discussion of CCAR calculations and methodology. For discussion of the differences between DFAST and CCAR, see Hirtle and Lehnert (2014).

  8. As Dowd (2014, pp. 5–7) points out, the VaR model is a poor measure of tail risk, easily manipulated, and provides no information about risks that may not have occurred over the past year.

  9. http://www.federalreserve.gov/bankinforeg/resolution-plans.htm.

  10. https://www.chicagofed.org/banking/financial-institution-reports/bhc-data.

  11. https://wrds-web.wharton.upenn.edu/wrds/.

  12. http://www.newyorkfed.org/research/banking_research/datasets.html.

  13. For further discussion of econometric models of risk and volatility, see Engle (2004).

  14. Demsetz and Strahan (1997, pp. 303–305) also use a more complex version of Eq. (3) that includes as independent variables other factors such as bond yield spreads and risk premiums. They find the results using the simple estimation in Eq. (3) are mostly the same as using the more complex measure.

  15. https://research.stlouisfed.org/fred2/.

  16. We lag our independent variables, control variables, and state variables one quarter because we expect that BHC assets, and the state of the economy at the start of a quarter will predict BHC performance at the end of the quarter. We elaborate on this more in Sect. 4. Our summary statistics incorporate the lagging we perform.

  17. We exclude outliers with very high capital or RBC ratios of 20 % or above and very low capital and RBC ratios of less than zero. Capital and RBC ratios above 20 % indicate that the BHC has very low leverage and probably does not operate like a typical BHC. Ratios below zero indicate the bank has negative equity and is probably going through bankruptcy at the time of its quarterly report.

  18. We specifically use year dummies to account for the year and quarter dummies to control for seasonality.

  19. Verbeek (2008, pp. 62–24) also states that a non-nested F-test is equivalent to a J-test, which is a more powerful test for evaluating differences between non-nested models, in the case of evaluating one additional regressor as in Eq. (6), Therefore, the t-tests we carry out are equivalent to running a J-test. The J-test is based on Davidson and MacKinnon (1981). According to Clarke (2001), choosing between non-nested linear models is carried out by running a J-test. If our models were nonlinear, discrete choice, or generalized, then alternative tests such as the Vuong test would be more appropriate for choosing between non-nested models.

  20. As a similar but different approach, we could put \({\textit{TOTCAP}}_{its}\) and \({\textit{CAP}}_{its}\) (but not \({\textit{RBC}}_{its})\) in Eq. (8). The estimate of \(\eta _{1}\) would remain the same but would have an opposite sign. We could also create a measure \({\textit{DIFCAP}}_\textit{its}= \textit{RBC}_{\textit{its}} - {\textit{CAP}}_{\textit{its}}\) to statistically evaluate the difference between \({\beta }_{1}\) and \({\beta }_{2 }\) from Eq. (6). We do this later as a robustness check as discussed in the next

  21. Wooldridge (2003, p. 141) provides additional details on the method we use.

  22. Clustering adjusts for non-independence of the residuals within a given BHC while using robust standard errors adjusts for heteroskedasticity. We use Stata 13 to produce our estimates for this study. Stata produces identical standard error estimates whether using clustered or cluster-robust standard errors for fixed effects panel data estimation. A technical discussion by Stock and Watson (2007) regarding standard errors fixed effects panel data regression provides the rationale for the equivalence of clustered and cluster-robust standard errors for fixed effects panel data estimation in Stata.

  23. Although it is not clear ex ante whether higher RBC should be predict higher or lower income due to the problem of regulator arbitrage, the coefficient estimates in Tables 2 and 3 show that CAP and RBC are both positively statistically significant predictors of INCOME. Since the coefficient estimate of RBC in Table 4 is negatively statistically significant, we conclude that RBC is worse than CAP as a predictor of INCOME.

  24. Specific results tables from robustness checks are available from the authors upon request.

  25. Results are similar when using quarterly data. We display only annual data because the lower variability between periods is more visually appealing. In addition, the coefficient values for CHARGEOFF are given on the right y-axis to be of comparable scale to the other variables.

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Acknowledgments

We thank two anonymous reviewers for helpful comments and suggestions. Thomas Hogan is a committee staff member in the United States Senate. The views presented here are those of the authors alone and do not reflect the views of any particular Senator or committee.

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  1. Johnson Center for Political Economy, Troy University, Troy, AL, 38062, USA

    Thomas L. Hogan

  2. West Texas A&M University, 2501 4th Avenue, Canyon, TX, 79106, USA

    Neil R. Meredith

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  1. Thomas L. Hogan
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Correspondence to Thomas L. Hogan.

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Hogan, T.L., Meredith, N.R. Risk and risk-based capital of U.S. bank holding companies. J Regul Econ 49, 86–112 (2016). https://doi.org/10.1007/s11149-015-9289-8

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