We formulate the problem of designing callable bonds as a non-linear, global, optimization problem. The data of the model are obtained from simulations of solution manual for discrete mathematics and its applications pdf-period returns of a given bond design, which are used to compute a certainty equivalent return, viz.
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The design specifications of the callable bond are then adjusted so that the certainty equivalent return is maximized. The model is compared with the classical portfolio immunization model, and the tabu search solution technique is compared with simulated annealing for solving the global optimization program. It is shown that the global optimization model yields higher returns than portfolio immunization. It is also shown that tabu search is computationally more efficient than simulated annealing in solving the model, and it produces better solutions. Check if you have access through your login credentials or your institution.
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