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Computational Methods for Pricing American Put Options

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Abstract

This work develops computational methods for pricing American put options under a Markov-switching diffusion market model. Two methods are suggested in this paper. The first method is a stochastic approximation approach. It can handle option pricing in a finite horizon, which is particularly useful in practice and provides a systematic approach. It does not require calibration of the system parameters nor estimation of the states of the switching process. Asymptotic results of the recursive algorithms are developed. The second method is based on a selling rule for the liquidation of a stock for perpetual options. Numerical results using stochastic approximation and Monte Carlo simulation are reported. Comparisons of different methods are made.

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Authors and Affiliations

  1. Department of Mathematics, Wayne State University, Detroit, Michigan

    Y. J. Liu & G. Yin

  2. Department of Mathematics, University of Georgia, Athens, Georgia

    Q. Zhang

Authors
  1. Y. J. Liu
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  2. G. Yin
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  3. Q. Zhang
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Additional information

Communicated by C. T. Leondes

This research was supported in part by the National Science Foundation and in part by the Wayne State University Research Enhancement Program.

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Liu, Y.J., Yin, G. & Zhang, Q. Computational Methods for Pricing American Put Options. J Optim Theory Appl 127, 389–410 (2005). https://doi.org/10.1007/s10957-005-6551-8

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  • DOI: https://doi.org/10.1007/s10957-005-6551-8

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