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An improved simulation method for pricing high-dimensional American derivatives

Author

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  • Boyle, Phelim P.
  • Kolkiewicz, Adam W.
  • Tan, Ken Seng

Abstract

In this paper, we propose an estimator for pricing high-dimensional American-style options and show that asymptotically its upper bias converges to zero. An advantage of the proposed estimator is that when combined with low discrepancy sequences, it exhibits a superior rate of convergence. Numerical examples are conducted to demonstrate its efficiency.

Suggested Citation

  • Boyle, Phelim P. & Kolkiewicz, Adam W. & Tan, Ken Seng, 2003. "An improved simulation method for pricing high-dimensional American derivatives," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 62(3), pages 315-322.
    • Handle: RePEc:eee:matcom:v:62:y:2003:i:3:p:315-322
    DOI: 10.1016/S0378-4754(02)00248-3
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    References listed on IDEAS

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    1. Boyle, Phelim P & Evnine, Jeremy & Gibbs, Stephen, 1989. "Numerical Evaluation of Multivariate Contingent Claims," The Review of Financial Studies, Society for Financial Studies, vol. 2(2), pages 241-250.
    2. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    3. Broadie, Mark & Glasserman, Paul, 1997. "Pricing American-style securities using simulation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1323-1352, June.
    4. Broadie, M. & Glasserman, P., 1997. "A Sotchastic Mesh Method for Pricing High-Dimensional American Options," Papers 98-04, Columbia - Graduate School of Business.
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    Citations

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    Cited by:

    1. Berridge, S.J. & Schumacher, J.M., 2002. "An Irregular Grid Approach for Pricing High Dimensional American Options," Other publications TiSEM 416a6d43-3466-47e0-b656-d, Tilburg University, School of Economics and Management.
    2. Calypso Herrera & Florian Krach & Pierre Ruyssen & Josef Teichmann, 2021. "Optimal Stopping via Randomized Neural Networks," Papers 2104.13669, arXiv.org, revised Dec 2023.
    3. Jin, Xing & Yang, Cheng-Yu, 2016. "Efficient estimation of lower and upper bounds for pricing higher-dimensional American arithmetic average options by approximating their payoff functions," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 65-77.
    4. Berridge, S.J. & Schumacher, J.M., 2004. "Pricing High-Dimensional American Options Using Local Consistency Conditions," Discussion Paper 2004-19, Tilburg University, Center for Economic Research.
    5. Doan, Viet_Dung & Gaikwad, Abhijeet & Bossy, Mireille & Baude, Françoise & Stokes-Rees, Ian, 2010. "Parallel pricing algorithms for multi-dimensional Bermudan/American options using Monte Carlo methods," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(3), pages 568-577.

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