Quantization based recursive Importance Sampling

Details Quantization based recursive Importance Sampling Quantization based recursive Importance Sampling

2011-09-19

arxiv.org/abs/1109.4041v1

Mathematics

Probability

Scientific paper

We investigate in this paper an alternative method to simulation based recursive importance sampling procedure to estimate the optimal change of measure for Monte Carlo simulations. We propose an algorithm which combines (vector and functional) optimal quantization with Newton-Raphson zero search procedure. Our approach can be seen as a robust and automatic deterministic counterpart of recursive importance sampling by means of stochastic approximation algorithm which, in practice, may require tuning and a good knowledge of the payoff function in practice. Moreover, unlike recursive importance sampling procedures, the proposed methodology does not rely on simulations so it is quite generic and can come along on the top of Monte Carlo simulations. We first emphasize on the consistency of quantization for designing an importance sampling algorithm for both multi-dimensional distributions and diffusion processes. We show that the induced error on the optimal change of measure is controlled by the mean quantization error. We illustrate the effectiveness of our algorithm by pricing several options in a multi-dimensional and infinite dimensional framework.

Affiliated with

Also associated with

No associations

Rating

Quantization based recursive Importance Sampling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Quantization based recursive Importance Sampling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Quantization based recursive Importance Sampling will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-96924