Importance sampling for backward SDEs

Thilo Moseler () and Christian Bender
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Thilo Moseler: Universität Konstanz

No 08-11, CoFE Discussion Paper from Center of Finance and Econometrics, University of Konstanz

Abstract: In this paper we explain how the importance sampling technique can be generalized from simulating expectations to computing the initial value of backward SDEs with Lipschitz continuous driver. By means of a measure transformation we introduce a variance reduced version of the forward approximation scheme by Bender and Denk [4] for simulating backward SDEs. A fully implementable algorithm using the least-squares Monte Carlo approach is developed and its convergence is proved. The success of the generalized importance sampling is illustrated by numerical examples in the context of Asian option pricing under di®erent interest rates for borrowing and lending.

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Date: 2008-09-01
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