Reviews previous research on the efficiency and performance of financial institutions and uses Siems and Barr’s (1998) data envelopment analysis (DEA) model to evaluate the relative productive efficiency of US commercial banks 1984‐1998. Explains the methodology, discusses the input and output measures used and relates bank performance measures to efficiency. Describes the CAMELS rating system used by bank examiners and regulators; and finds that banks with high efficiency scores also have strong CAMELS ratings. Summarizes the other relationship identified and recommends the use of DEA to help analysts and policy makers understand organizations in greater depth, regulators and examiners to develop monitoring tools and banks to benchmark their processes.
SummaryThe dramatic rise in bank failures over the last decade has led to a search for leading indicators so that costly bailouts might be avoided. While the quality of a bank’s management is generally acknowledged to be a key contributor to institutional collapse, it is usually excluded from early-warning models for lack of a metric. This paper describes a new approach for quantifying a bank’s managerial efficiency, using a data-envelopment-analysis model that combines multiple inputs and outputs to compute a scalar measure of efficiency. This new metric captures an elusive, yet crucial, element of institutional success: management quality. New failure-prediction models for detecting a bank’s troubled status which incorporate this explanatory variable have proven to be robust and accurate, as verified by in-depth empirical evaluations, cost sensitivity analyses, and comparisons with other published approaches.
This paper develops a tractable two-country DSGE model with sticky prices à la Calvo (1983) and local-currency pricing. We analyze the capital investment decision in the presence of adjustment costs of two types, the capital adjustment cost (CAC) specification and the investment adjustment cost (IAC) specification. We compare the investment and trade patterns with adjustment costs against those of a model without adjustment costs and with (quasi-) flexible prices. We show that having adjustment costs results into more volatile consumption and net exports, and less volatile investment. We document three important facts on U.S. trade: a) the S-shaped cross-correlation function between real GDP and the real net exports share, b) the J-curve between terms of trade and net exports, and c) the weak and S-shaped cross-correlation between real GDP and terms of trade. We find that adding adjustment costs tends to reduce the model's ability to match these stylized facts. Nominal rigidities cannot account for these features either.
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