Stochastic Optimization Algorithms for Pricing American Put Options Under Regime-Switching Models

G. Yin, J. W. Wang, Q. Zhang and Y. J. Liu
Additional contact information
G. Yin: Wayne State University
J. W. Wang: CitiGroup Inc.
Q. Zhang: University of Georgia
Y. J. Liu: Missouri Southern State University

Journal of Optimization Theory and Applications, 2006, vol. 131, issue 1, No 3, 37-52

Abstract: Abstract This work provides a Markov-modulated stochastic approximation based approach for pricing American put options under a regime-switching geometric Brownian motion market model. The solutions of pricing American options may be characterized by certain threshold values. Here, a class of Markov-modulated stochastic approximation (SA) algorithms is developed to determine the optimal threshold levels. For option pricing in a finite horizon, a SA procedure is carried out for a fixed time T. As T varies, the optimal threshold values obtained via SA trace out a curve, called the threshold frontier. Numerical experiments are reported to demonstrate the effectiveness of the approach. Our approach provides us with a viable computational tool and has advantage in terms of the reduced computational complexity compared with the variational or quasivariational inequality methods for optimal stopping.

Keywords: Markov-modulated stochastic optimization; regime switching; American put option (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-006-9134-4

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