Beta in linear risk tolerance economies

Article Abstract:

Estimates of future return-betas are plotted for power utility economies with linear risk tolerance, by employment of certain numerical analysis methods. Expected return-betas are then explained and expanded upon by development of covariance and coskewness equations. The plotted graphs and equations indicate that actual return distributions experienced within similar economies are well supported by the Mean Variance Capital Assets Pricing Model. The research also supports the contention that equations of expected returns containing covariance terms only are more accurate than those which contain both covariance and coskewness terms.

Case studies, Economic aspects, Investments, Risk management, Valuation, Investment analysis, Securities analysis, Assets (Accounting), Capital assets pricing model, Capital asset pricing model

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Sensitivity analysis for mean-variance portfolio problems

Article Abstract:

The performance of sensitivity analyses for Mean-Variance (MV) portfolio problems is shown. The analyses, which use Parametric Quadratic Programming (PQP), allow the examination of changes in variance, mean, and composition of the optimal portfolio. The results show that parametric changes in either the means or the right-hand constraints influence these changes. It is suggested that sensitivity analyses be used to study the relationship between inputs and the resulting optimal portfolio. It may also be used to reinforce the feedback process for the process of formulating the inputs.

Management, Portfolio management, Securities, Mathematical optimization, Optimization theory, Quadratic programming

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Estimating market model betas: a comparison of random coefficient methods and their ability to correctly identify random variation

Article Abstract:

An evaluation of market models that employ random coefficient methods suggests that these methods cannot distinguish between significance and insignificance with respect to market model betas. It appears that the maximum likelihood method of market modeling has not been adequately tested, and that these models cannot identify market occurrences as being random coefficient processes. . This conclusion is supported by empirical testing and simulation.

Stochastic processes

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