A model for designing callable bonds and its solution using tabu search

Abstract

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 holding-period returns of a given bond design, which are used to compute a certainty equivalent return, viz., some target assets. The design specifications of the callable bond are then adjusted so that the certainty equivalent return is maximized. The resulting problem is multi-modal, and a tabu search procedure, implemented on a distributed network of workstations, is used to optimize the bond design. 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.