A Short Report on Importance Sampling for Rare Event Simulation in Diffusions

TL;DR

This paper explores importance sampling techniques for rare event simulation in diffusion processes, demonstrating log-efficiency via large deviations and stochastic control, with practical parameter estimation and validation.

Contribution

It introduces a spectral parameterization and cross-entropy method to approximate optimal controls, linking importance sampling with stochastic control and large deviations.

Findings

The importance sampling estimator is shown to be log-efficient.

The cross-entropy method effectively estimates control parameters.

Numerical example validates the approach's efficiency.

Abstract

In this manuscript, we investigate importance sampling methods for rare-event simulation in diffusion processes. We show, from a large-deviation perspective, that the resulting importance sampling estimator is log-efficient. This connection is established via a stochastic optimal control formulation, and the associated Hamilton--Jacobi--Bellman (HJB) equation is derived using dynamic programming. To approximate the optimal control, we adopt a spectral parameterization and employ the cross-entropy method to estimate the parameters by solving a least-squares problem. Finally, we present a numerical example to validate the effectiveness of the cross-entropy approach and the efficiency of the resulting importance sampling estimator.