Why were banks better off in the 2001 recession?

In a sharp turnaround from their fortunes in the 1990-91 recession, banks came through the 2001 recession reasonably well. A look at industry and economy-wide developments in the intervening years suggests that banks fared better largely because of more effective risk management. In addition, they benefited from a decline in short-term interest rates and the relative mildness of the 2001 downturn.Bank profits ; Risk management ; Recessions ; Interest rates

Measurement and Estimation of Credit Migration Matrices

Credit migration matrices are cardinal inputs to many risk management applications. Their accurate estimation is therefore critical. We explore three approaches, cohort and two variants of duration—time homogeneous and non-homogeneous—and the resulting differences, both statistically through matrix norms and economically through credit portfolio and credit derivative models. We develop a testing procedure to assess statistically the differences between migration matrices using bootstrap techniques. The method can have substantial economic import: economic credit risk capital differences between economic regimes, recession vs. expansion, can be as large as difference implied by different estimation techniques. Ignoring the efficiency gain inherent in the duration methods by using the cohort method instead is more damaging that making a (possibly false) assumption of time-homogeneity.Credit risk, risk management, matrix norms, bootstrapping, credit derivatives

Businessmen's Expectations Are Neither Rational nor Adaptive

A framework which allows for the joint testing of the adaptive and rational expectations hypotheses is presented. We assume joint normality of expectations, realizations and variables in the information set, allowing for parsimonious interpretation of the data; conditional first moments are linear in the conditioning variables, and we can easily recover regression coefficients from them and test simple hypotheses by imposing zero restrictions on these coefficients. The nature of the data, which are responses to business surveys and are all categorical, requires simulation techniques to obtain full information maximum likelihood estimates. We use a latent variable model which allows for the construction of a simple likelihood function. However, this likelihood contains multi-(four)dimensional integrals, requiring simulators to evaluate. Simulated maximum-likelihood estimation is carried out using the Geweke-Hajivassilou-Keane (GHK) method, which is consistent and has low variance. The latter is crucial when maximizing the log-likelihood directly. Identification of the parameters is achieved by placing restrictions on the response thresholds and/or the variances. We find that we can reject both hypotheses. --

Metrics for Comparing Credit Migration Matrices

Credit migration or transition matrices, which characterize the expected changes in credit quality of obligors, are cardinal inputs to applications such as asset pricing and risk management. We propose a new metric for comparing these matrices (a mobility index) by first subtracting the identity matrix, focusing the analysis on the dynamics, and then taking the average of the singular values for the resulting matrix. This yields a metric which has an intuitively-appealing “size” related to the average probability of migration of the original matrix. We also propose a new mobility index performance criterion which is particularly relevant for credit migration matrices, namely that it be distribution discriminatory, i.e. sensitive to the distribution of off-diagonal probability mass. We demonstrate the advantages of the proposed metric over more traditional cell-by-cell distance metrics and eigenvalue-based mobility indices. We then apply these metrics to credit rating histories of S&P rated U.S. obligors from 1981-2001.credit migration matrix, matrix norm, mobility indices, singular values, risk management

The New Basel Capital Accord and Questions for Research

The New Basel Accord for bank capital regulation is designed to better align regulatory capital to the underlying risks by encouraging better and more systematic risk management practices, especially in the area of credit risk. We provide an overview of the objectives, analytical foundations and main features of the Accord and then open the door to some research questions provoked by the Accord. We see these questions falling into three groups: what is the impact of the proposal on the global banking system through possible changes in bank behavior; a set of issues around risk analytics such as model validation, correlations and portfolio aggregation, operational risk metrics and relevant summary statistics of a bank’s risk profile; issues brought about by Pillar 2 (supervisory review) and Pillar 3 (public disclosure).Bank capital regulation, risk management, credit risk, operational risk

Deposit Insurance and Risk Management of the U.S. Banking System: How Much? How Safe? Who Pays?

We examine the question of deposit insurance through the lens of risk management by addressing three key issues: 1) how big should the fund be; 2) how should coverage be priced; and 3) who pays in the event of loss. We propose a risk-based premium system that is explicitly based on the loss distribution faced by the FDIC. The loss distribution can be used to determine the appropriate level of fund adequacy and reserving in terms of a stated confidence interval and to identify risk-based pricing options. We explicitly estimate that distribution using two different approaches and find that reserves are sufficient to cover roughly 99.85% of the loss distribution corresponding to about a BBB+ rating. We then identify three risk-sharing alternatives addressing who is responsible for funding losses in different parts of the loss distribution. We show in an example that expected loss based pricing, while appropriately penalizing riskier banks, also penalizes smaller banks. By contrast, unexpected loss contribution based pricing significantly penalizes very large banks because large exposures contribute disproportionately to overall (FDIC) portfolio risk.Deposit insurance pricing, loss distribution, risk-based premiums.