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Improving the Monte Carlo estimation of boundary crossing probabilities by control variables

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  • Pötzelberger Klaus

    (Institute for Statistics and Mathematics, Augasse 2-6, A-1090 Vienna, Austria)

Abstract

We propose an efficient Monte Carlo approach to compute boundary crossing probabilities (BCP) for Brownian motion and a large class of diffusion processes, the method of adaptive control variables. For the Brownian motion the boundary b (or the boundaries in case of two-sided boundary crossing probabilities) is approximated by a piecewise linear boundary , which is linear on m intervals. Monte Carlo estimators of the corresponding BCP are based on an m-dimensional Gaussian distribution. Let N denote the number of (univariate) Gaussian variables used. The mean squared error for the boundary is of order , leading to a mean squared error for the boundary b of order with , if the difference of the (exact) BCP's for b and is . Typically, for infinite-dimensional Monte Carlo methods, the convergence rate is less than the finite-dimensional .

Suggested Citation

  • Pötzelberger Klaus, 2012. "Improving the Monte Carlo estimation of boundary crossing probabilities by control variables," Monte Carlo Methods and Applications, De Gruyter, vol. 18(4), pages 353-377, December.
  • Handle: RePEc:bpj:mcmeap:v:18:y:2012:i:4:p:353-377:n:4
    DOI: 10.1515/mcma-2012-0013
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    References listed on IDEAS

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    1. K. Borovkov & Alexander Novikov, 2004. "Explicit Bounds for Approximation Rates for Boundary Crossing Probabilities for the Wiener Process," Research Paper Series 115, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Sheldon Lin, X., 1998. "Double barrier hitting time distributions with applications to exotic options," Insurance: Mathematics and Economics, Elsevier, vol. 23(1), pages 45-58, October.
    3. G. O. Roberts & C. F. Shortland, 1997. "Pricing Barrier Options with Time–Dependent Coefficients," Mathematical Finance, Wiley Blackwell, vol. 7(1), pages 83-93, January.
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