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A method for pricing American options using semi-infinite linear programming


  • Soren Christensen


We introduce a new approach for the numerical pricing of American options. The main idea is to choose a finite number of suitable excessive functions (randomly) and to find the smallest majorant of the gain function in the span of these functions. The resulting problem is a linear semi-infinite programming problem, that can be solved using standard algorithms. This leads to good upper bounds for the original problem. For our algorithms no discretization of space and time and no simulation is necessary. Furthermore it is applicable even for high-dimensional problems. The algorithm provides an approximation of the value not only for one starting point, but for the complete value function on the continuation set, so that the optimal exercise region and e.g. the Greeks can be calculated. We apply the algorithm to (one- and) multidimensional diffusions and to L\'evy processes, and show it to be fast and accurate.

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  • Soren Christensen, 2011. "A method for pricing American options using semi-infinite linear programming," Papers 1103.4483,, revised Jun 2011.
  • Handle: RePEc:arx:papers:1103.4483

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    References listed on IDEAS

    1. Ernesto Mordecki, 2002. "Optimal stopping and perpetual options for Lévy processes," Finance and Stochastics, Springer, vol. 6(4), pages 473-493.
    2. Alexander Novikov & Albert Shiryaev, 2006. "On a Solution of the Optimal Stopping Problem for Processes with Independent Increments," Research Paper Series 178, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Martin Lauko & Daniel Sevcovic, 2010. "Comparison of numerical and analytical approximations of the early exercise boundary of the American put option," Papers 1002.0979,, revised Aug 2010.
    4. L. C. G. Rogers, 2002. "Monte Carlo valuation of American options," Mathematical Finance, Wiley Blackwell, vol. 12(3), pages 271-286.
    5. Christensen, Sören & Irle, Albrecht, 2009. "A note on pasting conditions for the American perpetual optimal stopping problem," Statistics & Probability Letters, Elsevier, vol. 79(3), pages 349-353, February.
    6. Dayanik, Savas & Karatzas, Ioannis, 2003. "On the optimal stopping problem for one-dimensional diffusions," Stochastic Processes and their Applications, Elsevier, vol. 107(2), pages 173-212, October.
    7. Alexander Novikov & Albert Shiryaev, 2004. "On an Effective Solution of the Optimal Stopping Problem for Random Walks," Research Paper Series 131, Quantitative Finance Research Centre, University of Technology, Sydney.
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