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Discusses arbitrage pricing theory as a multifactor model for explaining rates of return on securities; and the use of principal components analysis to reduce the number of variables studies. Applies these ideas to returns on treasury bills and government bonds for 1,000 business days ending in March 1997 to obtain a set of three endogenous factors for the term structure of interest rates, forecasts returns for one‐day and 30‐day horizons and produces a time series of the forecast errors for eight short‐term interest rates. Compares the results with those from a single factor autoregessive forecasting model and finds that although their accuracy is similar for short horizons, the multifactor model is superior for longer horizons and shorter time to maturity.

This study attempts to determine whether the level and volatility of interest rates affect the equity returns of commercial banks. Short‐term, intermediate‐term, and long‐term interest rates are used. Volatility is defined as the conditional variance of respective interest rates and is generated by using the ARCH estimation procedure. Two sets of models are estimated. The basic models attempt to determine the effect of contemporaneous and lagged interest rate volatility on bank equity returns, while the extended models incorporate additional contemporaneous macroeconomic variables. Contemporaneous interest rate volatility has little explanatory power, while lagged volatilities do possess some explanatory power, with the lag length varying depending on the interest rate series used and the time period examined. The results from the extended model suggest that the long‐term interest rate affects bank equity returns more adversely than the short‐term or the intermediate‐term interest rates. The findings establish the relevance of incorporating macroeconomic variables and their volatilities in models determining bank equity returns.

This study employs a multivariate GARCH model to investigate the relative sensitivities of the first and the second moment of bank stock return distribution to the short‐term and long‐term interest rates and their respective volatilities. Three portfolios are formed representing the money center banks, large banks, and small banks, respectively. Estimation and testing of hypotheses are carried out for each of the three portfolios separately. The sample includes daily data over the 1988‐2000 period. Several hypotheses are tested within the multivariate GARCH specification. These include the hypotheses of: (i) insensitivity of bank stock return to the changes in the short‐term and long‐term interest rates, (ii) insensitivity of bank stock returns to the changes in the volatilities of short‐term and long‐term interest rates, and (iii) insensitivity of bank stock return volatility to the changes in the short‐term and long‐term interest rate volatilities. The findings indicate that short‐term and long‐term interest rates and their volatilities do exert significant and differential impacts on the return generation process of the three bank portfolios. The magnitudes and the direction of the effect are model‐specific namely that they depend on whether the short‐term or the long‐term interest rate level is included in the mean return equation. These findings have implications on bank hedging strategies against the interest rate risk, regulatory decisions concerning risk‐based capital requirement, and investor’s choice of a portfolio mix.

The selection of the optimal credit investigation policy is a sequential decision problem that Mehta solved using decision tree analysis. This paper explores the managerial implications of the credit investigation/credit granting policy. The credit policy selected effects the resources required for credit investigation, the scale of collection efforts, the level and breakdown of accounts receivable, cash budgets, and total sales and production costs.