Title: Conditional Sampling for Option Pricing under the LT Method
Authors: Achtsis, Nico
Cools, Ronald
Nuyens, Dirk #
Issue Date: 9-Jun-2011
Conference: 3rd International Conference on Numerical Methods for Finance edition:3 location:Limerick, Ireland date:8-10 June 2011
Abstract: Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods are often used in pricing complex derivatives. The merit of QMC is that, theoretically at least, higher convergence rates can be obtained than regular MC. The payo ff function is usually high-dimensional and non-smooth, eliminating the advantage of using QMC. Imai & Tan (2006) introduced the LT method which minimizes the effective dimension of the problem by transforming the normal variates using an orthogonal transformation, thereby improving the QMC method. We will present an extension to their method for valuing options that have a barrier feature on an
underlying asset, incorporating and extending an idea from Staum & Glasserman (2001). These options have a payo ff that depends on whether the asset does or does not cross a certain level during the life of the option. If the probability of (not) hitting is large enough, then much more paths have to be sampled for reliable results. Our extension aims to reduce the required number of paths.
Publication status: published
KU Leuven publication type: IMa
Appears in Collections:Numerical Analysis and Applied Mathematics Section
# (joint) last author

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