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Brenda Gonzàlez-Hermosillo is currently an Economist in the Western Hemisphere Department (she was in the Monetary and Exchange Affairs Department when the paper was written); Ceyla Pazarbaşioğlu is an Economist in the Monetary and Exchange Affairs Department; and Robert Billings is an Economist in the Department of Finance, Canada. The authors wish to thank John Green, Malcolm Knight, Alfredo Leone, Sunil Sharma, Monetary and Exchange Affairs Department seminar participants, and especially William E. Alexander, for useful comments. The authors are also grateful to staff at the Federal Reserve Bank of Dallas, especially Susan Tetley, and to Federico Rubli Kaiser and Alejandro Diaz de Leon at the Bank of Mexico for providing valuable information and comments. Kiran Sastry provided able research assistance.
Some of the most recent contributions to the literature on early-warning systems of bank failure have adopted similar approaches. See, for example, Cole and Gunther (1993), Wheelock and Wilson (1994), and Lane, Looney, and Wansley (1986).
Interestingly, all the banks that had been privatized in the early 1990s receive some sort of support from the government.
For a detailed description of these programs, see Banco de Mexico (1996) and International Monetary Fund (1996).
The Fondo Bancario de Protección al Ahorro, the deposit guarantee fund, is financed by contributions from banks proportional to their captation directa (which includes various types of deposits and bankers’ acceptances). Mexico has a system of implicit full deposit guarantees.
Cole, Comyn, and Gunther (1995) also suggest a broader approach. In their framework, “failure” includes not only those institutions that are declared equity insolvent but also those for which regulators mandate prompt corrective action,
Reported nonperforming loans, in accordance with Mexican accounting rules, include only unpaid interest (not principal) and hence underestimate the magnitude of the nonperforming loans. Mexican banks are expected to report nonperforming loans according to the U.S. Generally Accepted Accounting Principles (GAAP) by March 1997.
This ratio also likely underestimates the actual size of the banking system’s fragility because the Sistema de Información Estadística (SIES) generally excludes data for banks after they have received financial support.
Although the main interest is to predict the probability that an institution will require financial assistance from third parties, empirical analyses, in practice, are typically limited to predicting the probability that a hank will receive financial support (intervention in our definition) because the observed-state variables correspond to whether a bank in fact received such support.
This specification is suggested in Schmidt and Witte (1989) and is also applied in Cole and Gunther (1993).
We tested several other functional forms, including those based on the Weibull, normal, and exponential distributions. We found that the logistic distribution best describes the banking difficulties in Mexico during the period under study. Similarly, Cole and Gunther (1993) also find that the logistic distribution best describes the banking difficulties in the United States in 1985-92.
Banks’ balance sheet data are on a consolidated basis that includes foreign currency assets and liabilities.
Similarly, Grenadier and Hall (1995) proxy credit risk by the actual amounts of bank loans gone bad.
The effect of the lending variables on bank survival time may be ambiguous in certain circumstances, even though these variables are expected to be positively related to the likelihood of eventual bank failure. One such situation would occur if banks with a particular class of loans find it easier to restructure their troubled loans. For example, widely available programs to promote restructuring of certain categories of bank loans (i.e., Mexico’s debt-relief programs to bank debtors that included restructuring of mortgage loans and consumer loans) could extend banks’ survival time.
Given that Mexico borrowed more than US$20 billion in resources from abroad, part of which was directed toward the program of support offered to ailing banks, the actual endowment supporting the deposit guarantee fund was probably much higher than the banks’ contributions to it.
Real interest rates are constructed by subtracting the year-over-year inflation rate from the current annual nominal rate of return.
Although the interbank interest rate is considered more representative of domestic liquidity pressures, we were unable to obtain data on this variable for the earlier years of this study.
The beginning of this period roughly corresponds to the onset of the program of privatization of Mexican banks. Several new banks obtained licenses to operate at different limes during the period of study. Thus, from an empirical standpoint, banks’ “beginning of life” roughly corresponds to the time when they were privatized, whereas new banks entered the system in later periods.
When more than one type of direct intervention occurs for the same bank at different times, the estimation is based on the first type of intervention. When the banks were grouped according to the different types of intervention, the means and standard deviations of the explanatory variables revealed no significant differences.
As discussed in the data section, alternative proxies were used for some of the variables. This section reports the variables that give the “best fit,” that is, the best predictive power for the model. The use of alternative variables does not change the results qualitatively, and these results can be made available upon request.
A type I error occurs when the null hypothesis is rejected when it is in fact true (predicting no failure when a bank in fact fails). A type II error occurs when the null hypothesis is accepted when it is in fact false (predicting failure when a bank in fact does not fail).
Clearly, this constitutes a “bare-bones” CAMEL-type of model. In practice, bank supervisors examine an array of other financial indicators for clues about the financial condition of banks. However, this simple CAMEL-type specification is useful because it sheds light on the marginal contribution of macroeconomic and banking sector variables in explaining banks’ likelihood of survival and expected survival time.
The coefficients in the survival regressions cannot be interpreted as elasticities (as in linear models). The contribution of a given factor can only he determined by applying the regression coefficients to a change in the values of a specific factor while holding the other factors at their average values.
Because banks, once they have been intervened, are no longer part of our sample (given the availability of information in SIES), the overall index of the system would improve immediately after several banks have been intervened (fragile banks are treated as “removed” from the system). For Mexico, given that there were several “waves” of intervention, (he index none(heless deteriorated again after some of the interventions that occured in the early part of 1995.
The shape of this function is not surprising, given that the data best fit a logistic survival function with a hazard function that has precisely this shape. However, the model is useful in identifying the turning points in the Mexican case.