Knowledge that Transforms

In this paper we extend the multigood futures pricing model of Grauer and Litzenberger [9] to a dynamic discrete time setting. We then test the model using data on futures prices for corn, wheat, and soybeans. The parameter estimates we obtain are similar to those obtained by other researchers using stock return data. The model itself is rejected and we offer some suggestions as to which assumption may be violated. We also give an interpretation to the Hansen‐Singleton nonlinear instrumental variables estimation technique used in our empirical work.

The purpose of this note is to examine, theoretically, why the market risk premium (R^_ g\ raa y influence tests of asset pricing models with higher moments.When moments of higher order than the variance are added to a pricing model developed within the usual two-fund separation assump- tions, the market risk premium enters the pricing equation in a nonlinear fashion and is implicit in the estimation of each moment's coefficient.Unless this nonlinearity is recognized, incorrect conclusions regarding the tests of such models may result.

This study analyzes the interaction between the optimal level of investment and debt financing. For this purpose, a model is structured in which a firm, facing an uncertain price, has to decide on its optimal level of investment and debt. The amount of investment sets a limit on output whose optimal level is determined after price is realized. The debt involved is risky (there exists a possibility of bankruptcy). The analysis proves that investment and its optimal financing have to be simultaneously determined and that a negative relationship exists between operating and financial leverage. We also demonstrate that as the tax rate increases, optimal capacity decreases and optimal leverage increases. An analysis of the impact of changes in the expected price shows that under some conditions, an increase in expected price would lead to an increase in optimal investment (firm size) and a decrease in optimal debt.

Applying Fishburn's [4] conditions for convex stochastic dominance, exact linear programming algorithms are proposed and implemented for assigning discrete return distributions into the first‐ and second‐order stochastic dominance optimal sets. For third‐order stochastic dominance, a superconvex stochastic dominance approach is defined which allows classification of choice elements into superdominated, mixed, and superoptimal sets. For a choice set of 896 security returns treated previously in the literature, 454, 25, and 13 distributions are in the first‐, second‐, and third‐order convex stochastic dominance optimal sets, respectively. These optimal sets compare with admissible first‐, second‐, and third‐order stochastic dominance sets of 682, 35, and 19 distributions, respectively. The applicability of superconvex stochastic dominance for continuous distributions defined over a bounded interval is then shown. The difficulties in identifying the elements of the superdominated set for distributions defined over the entire real line are demonstrated in the determination of the dominated choices for a set of normally distributed mutual fund returns previously examined by Meyer [9]. Specifically, we find that the dominated set determined by Meyer is too large.

Vasicek and Fong [11] developed exponential spline functions as models of the interest rate term structure and claim such models are superior to polynomial spline models. It is found empirically that i) exponential spline term structure estimates are no more stable than estimates from a polynomial spline model, ii) data transformations implicit in the exponential spline model frequently condition the data so that it is difficult to obtain approximations in which one can place confidence, and iii) the asymptotic properties of the exponential spline model frequently are unrealistic. Estimation with exponential splines is no more convenient than estimation with polynomial splines and gives substantially identical estimates of the interest rate term structure as well.

This study analyzes the interaction between the optimal level of investment and debt financing. For this purpose, a model is structured in which a firm, facing an uncertain price, has to decide on its optimal level of investment and debt. The amount of investment sets a limit on output whose optimal level is determined after price is realized. The debt involved is risky (there exists a possibility of bankruptcy). The analysis proves that investment and its optimal financing have to be simultaneously determined and that a negative relationship exists between operating and financial leverage. We also demonstrate that as the tax rate increases, optimal capacity decreases and optimal leverage increases. An analysis of the impact of changes in the expected price shows that under some conditions, an increase in expected price would lead to an increase in optimal investment (firm size) and a decrease in optimal debt.

Utility‐based models of asset pricing may be estimated with or without assuming a distribution for security returns; both approaches are developed and compared here. The chief strength of a parametric estimator lies in its computational simplicity and statistical efficiency when the added distributional assumption is true. In contrast, the nonparametric estimator is robust to departures from any particular distribution, and it is more consistent with the spirit underlying utility‐based asset pricing models since the distribution of asset returns remains unspecified even in the empirical work. The nonparametric approach turns out to be easy to implement with precision nearly indistinguishable from its parametric counterpart in this particular application. The application shows that log utility is consistent with the data over the period 1926–1981.

Previous research has documented a negative relation between common stock returns and inflation. Recently, Fama [3] and Geske and Roll [6] have argued that this relation results from a more fundamental one between real activity and expected inflation. Stock returns, they argue, signal changes in real activity, which in turn affect expected inflation. However, unlike Fama, Geske and Roll argue that changes in real activity result in changes in money supply growth, which in turn affect expected inflation. Empirical tests have analyzed separately each link in the proposed causal chain. In this article, we investigate simultaneously the relations among stock returns, real activity, inflation, and money supply changes using a vector autoregressive moving average (VARMA) model. Our empirical results strongly support Geske and Roll's reversed causality model.

Rational restrictions are derived for the values of American options on futures contracts. For these options, the optimal policy, in general, involves premature exercise. A model is developed for valuing options on futures contracts in a constant interest rate setting. Despite the fact that premature exercise may be optimal, the value of this American feature appears to be small and a European formula due to Black serves as a useful approximation. Finally, a model is developed to value these options in a world with stochastic interest rates. It is shown that the pricing errors caused by ignoring the location of the interest rate (relative to its long‐run mean) range from −5% to 7%, when the current rate is ±200 basis points from its long‐run value. The role of interest rate expectations is, therefore, crucial to the valuation. Optimal exercise policies are found from numerical methods for both models.