# Question

Reconsider Prob. 13.1-4 and its quadratic programming model.

(a) Display this model [including the values of R(x) and V(x)] on an Excel spreadsheet.

(b) Use Solver (or ASPE) and its GRG Nonlinear solving method to solve this model for four cases: minimum acceptable expected return = 13, 14, 15, 16.

(c) Repeat part b while using ASPE and its Quadratic solving method.

(d) For typical probability distributions (with mean μ and variance σ2) of the total return from the entire portfolio, the probability is fairly high (about 0.8 or 0.9) that the return will exceed μ – σ, and the probability is extremely high (often close to 0.999) that the return will exceed μ – 3σ. Calculate μ – σ and μ – 3σ for the four portfolios obtained in part (b). Which portfolio will give the highest μ among those that also give μ – σ ≥ 0?

(a) Display this model [including the values of R(x) and V(x)] on an Excel spreadsheet.

(b) Use Solver (or ASPE) and its GRG Nonlinear solving method to solve this model for four cases: minimum acceptable expected return = 13, 14, 15, 16.

(c) Repeat part b while using ASPE and its Quadratic solving method.

(d) For typical probability distributions (with mean μ and variance σ2) of the total return from the entire portfolio, the probability is fairly high (about 0.8 or 0.9) that the return will exceed μ – σ, and the probability is extremely high (often close to 0.999) that the return will exceed μ – 3σ. Calculate μ – σ and μ – 3σ for the four portfolios obtained in part (b). Which portfolio will give the highest μ among those that also give μ – σ ≥ 0?

## Answer to relevant Questions

The management of the Albert Hanson Company is trying to determine the best product mix for two new products. Because these products would share the same production facilities, the total number of units produced of the two ...Reconsider the linearly constrained convex programming model given in Prob. 13.4-7. (a) Use the separable programming technique presented in Sec. 13.8 to formulate an approximate linear programming model for this problem. ...Reconsider the linearly constrained convex programming model given in Prob. 13.6-5. Starting from the initial trial solution (x1, x2) ≥ (0, 0), use one iteration of the Frank-Wolfe algorithm to obtain exactly the same ...Consider the following linearly constrained convex programming problem: Maximize f(x) = 3x1 + 4x2 – x31 – x32, subject to x1 +x2 ≤ 1 and x1 ≥ 0, x2 ≥ 0. Reconsider the quadratic programming model given in Prob. 13.7-4. Beginning with the initial trial solution (x1, x2) = (1/2, 1/2), use the automatic procedure in your IOR Tutorial to apply SUMT to this model with r = 1, ...Post your question

0