This papaer assesses some of the costs and benefits of aloowing banks to pay interest on demand deposits. With the help of a simple short-run model of the financial sector, it is shown that, barring a possible but unlikely perverse reaction, the deposit rate will tend to move in the same direction as other rates. This will moderate changes in the money supply accompanying a given change in interest rates; conversely, a given change in the excess demand for money will cause larger fluctuations in the level of interest rates. This can either be good or bad; if the excess demand for money corresponds to an excess demand for real output, the wider fluctuations in the interest rate will be more effective in eliminating it.

Theoretically the money stock could be controlled by targeting, either a reserve aggregate or the Federal funds rate. In practice, however, one strategy might be more effective than the other if the relationship between its operating variable and the money stock was more predictable.

An equity-linked life insurance policy with an asset value guarantee (ELPAVG) is an insurance policy whose benefit payable on death or at maturity consists of the greater of some guaranteed amount and the value of a reference portfolio which is defined by the deemed investment of a predetermined component of the policy premium in a portfolio of common stocks or mutual fund–the reference fund. In an earlier paper we demonstrated that the benefit payable under an ELPAVG could be decomposed into the known guaranteed amount and an immediately exercisable call option to purchase the reference portfolio for an exercise price equal to the guaranteed amount. The principles of the option pricing model were then employed to derive the equilibrium premium for both a single premium ELPAVG contract and a periodic premium contract. It was further noted that the hedging arguments, which are the core of most of the recent theory of option pricing, could be employed to derive an investment strategy for the insurance company which would eliminate the risks associated with the sale of ELPAVGs: this is an important result, for ELPAVGs may pose a significant threat to the solvency of insurance companies since the risks of loss under different contracts are not independent, but are commonly related to the overall performance of the reference fund. Actuaries have responded to this threat by attempting to determine a level of reserves sufficient to reduce the probability of ruin to an acceptable level. On the other hand, adoption of the riskless investment strategy in theory eliminates the need to hold any reserves except against mortality risk.

This study examines the observable impact an electric utility's announcement of its decision to invest in nuclear power has upon stockholders' expected returns. The response of shareholders is examined via residual analysis and by a dummy variable switching regression technique.

The purpose of this paper is to provide evidence concerning investor reactions to off-balance sheet disclosures of concancellable leases as reflected in security prices. A valuation model is defined based upon the work of Modigliani and Miller which expresses the market value of the firm's common stock as a function of lease indebtedness. Data for the empirical analysis are obtained from Compustat and SEC form 10 K's. Crosssectional regressions are run by risk class on samples of 620 firms reporting rent expense, 432 firms disclosing lease commitments, and 139 firms reporting present values of so-called “financing” leases.

Recent theoretical articles on leasing are summarized as a basis for the distillation of a conceptually correct framework for analyzing leasing versus purchase decisions. In order to make a theoretically valid comparison, the analysis must be kept within the framework of comparable and consistent risk class assumptions. When this is done, competition in the leasing market produces indifference in the leasing versus purchase analysis.

Since the seminal article by Black and Scholes on the pricing of corporate liabilities, the importance in finance of contingent claims has become widely recognized. The key to the valuation of such claims has been found to lie in the solution to certain partial differential equations, the best known of which is that derived by Black and Scholes in their original article from the assumption that the value of the asset underlying the contingent claim follows a geometric Brownian motion.

The main focus of this study concerns the pricing of default-free bonds in a risky economy inhabited by risk-averse consumers. The methodology of the paper draws upon recent work in the fields of intertemporal asset pricing and valuation by arbitrage principles. We develop a general equilibrium model for the expected rates of return on “created financial assets” (such as bonds) dependent upon the risk attitudes of investors and the uncertain real investment opportunities available.

This paper develops a comprehensive approach to project selection and financing decisions, when firms' securities are traded in competitive markets and investors prefer “more to less.” The technique is based on direct inferences from observed security prices.

The essence of short-term financial planning is to determine an asset and liability mix that minimizes the cost of financing the firm's cash surpluses and deficits over the planning horizon. Seasonal and other effects cause uncertainty in forecasted cash requirements, liquidation, and termination costs. We develop a stochastic linear programming model that is computationally feasible for a firm's financial planning over several periods with all three types of uncertainty when there is a rich structure over the set of possible asset choices. The models' solution is facilitated using a recent novel algorithm for finitely distributed simple recourse SLPR problems developed by Wets and coded by Collins, Kallberg and Kusy. The algorithm uses a “working basis” that has the same dimension as the corresponding (approximate) “mean” linear program.

The Black-Scholes option pricing formula assumes that the variance of the return on the underlying stock is known with certainty. In practice an estimate of the variance, based on a sample of historical stock prices, is used. The estimation error in the variance induces error in the option price. Since the option price is a nonlinear function of the variance, an unbiased estimate of the variance does not produce an unbiased estimate of the option price. For reasonable parameter values, it is shown that the magnitude of the bias is not large.

This paper analyzes the effect of limited information and estimation risk on optimal portfolio choice when the joint probability distribution of security returns is multivariate normal and the underlying parameters (means and variance-covariance matrix) are unknown. We first consider the case of limited, but sufficient information (the number of observations per security exceeds the number of securities or the prior distribution of the underlying parameters is “sufficiently” informative). We show that for a general family of conjugate priors, the admissible set of portfolios, taking estimation risk into account, may be obtained by the traditional mean-variance analysis. As a result of estimation risk the optimal portfolio choice differs from that obtained by traditional analysis.

This paper examines empirically the relationship among the stability of security and portfolio betas and (1) the length of the sample period used to calculate betas, (2) beta adjustment techniques, and (3) beta magnitudes. Beta values are forecast using four models: (1) a naive model which assumes the beta value in period t + 1 is the same as in period t, (2) Blume's regression model, (3) a regression model used by Merrill Lynch, Pierce, Fenner and Smith, and (4) a Bayesian procedure suggested by Vasicek.

Until recently, most interest in econometrics was directed toward the data analysis problem. However, as opportunities for controlled experimentation in economics and business become more common, econometricians' interest in the statistical theory of experimental design is a natural consequence. Design techniques have and are being applied in marketing research, and in the contexts of such policy areas as the negative income tax and manpower experiments, the housing subsidy and health insurance subsidy experiments, and others. Experimentation in economics and business is an extremely complex research undertaking compared to the experimentation in other fields (such as agriculture, physics or chemistry) for which most of the design theory was developed. So, control experimentation in business and economics tends to be expensive in dollars, time and research talent. All this suggests the importance of carefully designing experiments to maximize their information output (efficient design) and the need for econometricians to extend the design theory in a number of directions of particular interest to economists and business researchers. This paper provides a solution to the problem of experimental designs for various multiple equation regression models frequently encountered by business and economic researchers.

A general proof using matrices is given proving the equivalence of the Chow test (analysis of covariance) and an appropriate adaptation of the dummy variable technique. Implications of hypothesis testing in the linear regression framework are reviewed for each method. The dummy variable approach is found to have the following advantages: (a) it is more convenient in testing hypotheses regarding the equality of subvectors of the parameter vectors from separate regressions, in particular not requiring the running of new regressions as the Chow test approach sometimes does; and (b) a more general form of hypothesis can be tested, namely that corresponding regression parameters differ by a constant other than zero.

This study investigates the ability of daily technical indicators to predict the direction of change in the Standard and Poor's 500 Index (as measured by price relatives). Two sets of variables widely used by technical analysts are employed, and are designated “regular” variables and “percentage” variables. Examples of relevant “regular” variables are: volume; proportion of stocks advancing, declining, and remaining unchanged; off-lot purchases, sales, and short-sales; and new highs and new lows.

Recommendations provided by brokerage houses are often classified as to investment term (near,…) and objective (growth,…). An extensive set of recommendations made during the years 1964-1970 was examined. Issues addressed included availability of descriptors and stocks categorized by descriptors: differences in risk; the presence and pattern of risk-adjusted returns for a 19-month period surrounding the recommendation; the differential impact on trading activity; the reaction of the market to the recommendations; and parallels between the results and previous research efforts by other authors.