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A General Mean-Variance Approximation to Expected Utility for Short Holding Periods

Journal of Financial and Quantitative Analysis 1981 16(3), 361
The mean-variance model is precisely consistent with the expected utility hypothesis only in the special cases of normally distributed security returns or quadratic utility functions. There is little evidence, however, that security returns follow normal distributions (see [13] for references) and quadratic preferences can be shown to generate implausible results, exhibiting increasing absolute risk aversion in the Pratt [ll]–Arrow [1, 2] sense and displaying negative marginal utility after some finite wealth level. In addition, Hakansson [4] has shown that single–period, mean-variance-efficient portfolios can have disastrous consequences over time—even when return distributions are stationary. Such criticisms of the mean-variance approach within the Von Neumann-Morgenstern framework have prompted several writers to suggest that investors maximize the expected value of utility functions with more “realistic” properties, while others have criticized the single-period focus of the model. One popular alternative utility function is the logarithmic function which exhibits decreasing absolute risk aversion and (conveniently) leads to myopic decision processes through time (i.e., investors treat each period as if it were the last, basing investment decisions on that period's wealth and return distributions only [8, 4]). (Other utility functions with constant relative risk aversion—such as the power function—also imply myopic decision rules within a multiperiod setting.)

A Dynamic Model of Investment and Capacity Utilization

Quarterly Journal of Economics 1981 96(3), 379
This paper develops a dynamic optimizing model of a firm with quasi-fixed factors subject to adjustment costs. The utilization rates of the quasi-fixed factors are chosen optimally by the firm, and the rates of investment in the quasi-fixed factors are based on the shadow prices of these factors, in the spirit of Tobin's q theory of investment. Capital investment is shown to be negatively related to capital utilization along the path to the steady state; however, in response to unanticipated demand shocks, capital utilization and investment are positively related.

A Methodological Note on the Estimation of Time Series

The Review of Economics and Statistics 1981 63(3), 471
ChowLin distributes a series, changing the frequency to a higher one while maintaining the sum over each period, using the Chow-Lin(1971) or related procedure. The newer procedure disaggregate.src is a better choice. Chow and Lin(1971), Best Linear Unbiased Interpolation, Distribution and Extrapolation of Time Series by Related Series, Review of Economics and Statistics, vol 53, 372-375. Fernandez(1981), A Methodological Note on the Estimation of Time Series, Review of Economics and Statistics, vol 63, 471-478. Litterman(1983), A Random Walk, Markov Model for the Distribution of Time Series, JBES, vol 1, 169-173.(This abstract was borrowed from another version of this item.)

A Fortran Program for Applying Sturm's Theorem in Counting Internal Rates of Return

Journal of Financial and Quantitative Analysis 1981 16(3), 381
The algorithm leading to a solution of the above question has been known at least since Kaplan's 1965 tutorial [5] on Sturm's theorem. The Sturm-Kaplan method has the power to count all zeros on the real axis between any two specified limits. A significant problem may arise, however, when one tries to generate the Sturmian functions which play a central part in the Sturm-Kaplan method. The rather arduous nature of the task derives from the necessity to perform several polynomial (synthetic) divisions. As the number of cash flows involved in the analysis increases, the time and effort required to determine the Sturmian functions increase as well.