1. Consider the following portfolio optimization problem where r R n is the expected return vector,...

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1. Consider the following portfolio optimization problem

where r̂ ∈ Rn is the expected return vector, is the return covariance matrix, and μ is a target level of expected portfolio return. Assume that the random return vector r follows a simplified factor model of the form  

where F ∈ Rn,k, k << n, is a factor loading matrix, f̂ ∈ Rk is given, and f ∈ Rk is such that E{f} = 0 and E{f f T} = I. The above optimization problem is a convex quadratic problem that involves n decision variables. Explain how to cast this problem into an equivalent form that involves only k decision variables. Interpret the reduced problem geometrically. Find a closed-form solution to the problem.

2. Consider the following variation on the previous problemwhere is a tradeoff parameters that weigths the relevance in the objective of the risk term and of the return term. Due to the presence of the constraint x ≥ 0, this problem does not admit, in general, a closed-form solution.

Assume that r is specified according to a factor model of the formwhere F, f and f̂ are as in the previous point, and e is an idiosyncratic noise term, which is uncorrelated with and such that  Suppose we wish to solve the problem using a logarithmic barrier method of the type discussed in Section 12.3.1. Explain how to exploit the factor structure of the returns to improve the numerical performance of the algorithm. 

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Optimization Models

ISBN: 9781107050877

1st Edition

Authors: Giuseppe C. Calafiore, Laurent El Ghaoui

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