Abstract
American put option pricing is a challenging, complex problem, and existing methods to address this problem are computationally intensive. In this paper, a self-adaptive evolutionary computation method is used for computing American put option price. The proposed method essentially transforms a discrete time exercisable American option to a continuous time exercisable option. The performance of the proposed method is compared with that of plain European Monte Carlo and Binomial Lattice option values. Further, in pricing American options this method exhibited better results with considerable improvements over that of conventional Monte-Carlo simulation method. It is argued that the proposed method effectively computes the upper bound on the American put options.
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