Nonparametric Partial Importance Sampling for Financial Derivative Pricing

TL;DR

This paper introduces a nonparametric importance sampling algorithm for financial derivative pricing that adapts to high-dimensional problems by focusing on low-dimensional subspaces, achieving significant efficiency improvements.

Contribution

It proposes a novel nonparametric estimation method for the optimal proposal distribution in importance sampling, combined with PCA to handle high-dimensional problems efficiently.

Findings

Significant reduction in variance for derivative pricing

Effective application to path-dependent and multi-asset options

Demonstrated asymptotic optimality and computational efficiency

Abstract

Importance sampling is a promising variance reduction technique for Monte Carlo simulation based derivative pricing. Existing importance sampling methods are based on a parametric choice of the proposal. This article proposes an algorithm that estimates the optimal proposal nonparametrically using a multivariate frequency polygon estimator. In contrast to parametric methods, nonparametric estimation allows for close approximation of the optimal proposal. Standard nonparametric importance sampling is inefficient for high-dimensional problems. We solve this issue by applying the procedure to a low-dimensional subspace, which is identified through principal component analysis and the concept of the effective dimension. The mean square error properties of the algorithm are investigated and its asymptotic optimality is shown. Quasi-Monte Carlo is used for further improvement of the method. It is easy to implement, particularly it does not require any analytical computation, and it is computationally very efficient. We demonstrate through path-dependent and multi-asset option pricing problems that the algorithm leads to significant efficiency gains compared to other algorithms in the literature.