Hostname: page-component-848d4c4894-nr4z6 Total loading time: 0 Render date: 2024-05-31T21:38:32.623Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  06 April 2009

Extract

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.)

Type
Research Article
Copyright
Copyright © School of Business Administration, University of Washington 1981

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1]Arrow, K. J.The Role of Securities in the Optimal Allocation of Risk-Bearing.” Review of Economic Studies, Vol. 31 (04 1964), pp. 9196.CrossRefGoogle Scholar
[2]Arrow, K. J.Aspects of the Theory of Risk-Bearing. Lectures, Helsinki (1964).Google Scholar
[3]Breiman, Leo. “Investment Policies for Expanding Business Optimal in the Long-Run Sense.” Naval Research Logistics Quarterly, Vol. 7 (1960), pp. 647651.CrossRefGoogle Scholar
[4]Hakansson, Nils H.Capital Growth and the Mean-Variance Approach to Portfolio Selection.” Journal of Financial and Quantitative Analysis, Vol. 6 (1971), pp. 517557.CrossRefGoogle Scholar
[5]Kalberg, J. G., and Ziemba, W. T.. “Comparisons of Alternative Utility Functions in Portfolio Selection Problems.” Faculty of Commerce Working Paper No. 609, University of British Columbia, Vancouver (10 1978).Google Scholar
[6]Kalberg, J. G., and Ziemba, W. T.. “On the Robustness of the Arrow-Pratt Risk Aversion Measure.” Economic Letters, Vol. 2 (1979), pp. 2126.CrossRefGoogle Scholar
[7]Levy, H., and Markowitz, H. M.. “Approximating Expected Utility by a Function of Mean and Variance.” American Economic Review, Vol. 69 (06 1979), pp. 308317.Google Scholar
[8]Mossin, Jan. “Optimal Multiperiod Portfolio Policies.” Journal of Business Vol. 41, No. 2 (04 1968), pp. 215219.CrossRefGoogle Scholar
[9]Ohlson, James A. “The Asymtotic Validity of Quadratic Utility as the Trading Interval Approaches Zero.” In Stochastic Optimization Models in Finance, Ziemba, W. T., and Vickson, R. G., eds. New York: Academic Press (1975).Google Scholar
[10]Ohlson, J. A., and Ziemba, W. T.. “Portfolio Selection in a Lognormal Market When the Investor Has a Power Utility Function.” Journal of Financial and Quantitative Analysis (03 1976), pp. 5771.CrossRefGoogle Scholar
[11]Pratt, John. “Risk Aversion in the Small and in the Large.” Econometrica, Vol. 32 (0104 1964), pp. 122136.CrossRefGoogle Scholar
[12]Pulley, Lawrence B. “Mean-Variance Approximations to Expected Logarithmic Utility.” Working paper.Google Scholar
[13]Pulley, Lawrence B. “Mean-Variance Approximations to the Growth-Optimal Decision Rule: An Empirical Investigation.” Unpublished Ph.D. Thesis, University of Virginia (05 1980).Google Scholar
[14]Rubinstein, Mark. “The Strong Case for the Generalized Logarithmic Utility Function as the Premier Model of Financial Markets.” Journal of Finance, Vol. 31 (05 1976), pp. 555571.CrossRefGoogle Scholar
[15]Samuelson, Paul A.The Fundamental Approximation Theorem of Portfolio Analysis in Terms of Means, Variances, and Higher Moments.” Review of Economic Studies, Vol. 36 (10 1970), pp. 537542.CrossRefGoogle Scholar
[16]Thorp, Edward O. “Portfolio Choice and the Kelly Criteria.” In Stochastic Optimization Models in Finance, Ziemba, and Vickson, , eds. New York: Academic Press (1975).Google Scholar
[17]Tsiang, S. C. “The Rationale of the Mean-Standard Deviation Analysis, Skewness, Preference, and the Demand for Money.” American Economic Review (06 1972), pp. 354371. See also comments by Borch and Levy, and reply by Tsiang AER (June 1974).Google Scholar
[18]Young, William E., and Trent, Robert N.. “Geometric Mean Approximation of Individual Securities and Portfolio Performance.” Journal of Financial and Quantitative Analysis (06 1969), pp. 179199.CrossRefGoogle Scholar