Rare event simulation for processes generated via stochastic fixed point equations

TL;DR

This paper develops a novel importance sampling method for efficiently estimating tail probabilities of solutions to stochastic fixed point equations, with proven optimality and practical numerical demonstrations.

Contribution

It introduces a new importance sampling algorithm for tail probability estimation in stochastic fixed point equations, proving its efficiency and optimality.

Findings

The estimator is consistent and strongly efficient.

The algorithm's running time is precisely characterized.

Numerical examples demonstrate ease of implementation.

Abstract

In a number of applications, particularly in financial and actuarial mathematics, it is of interest to characterize the tail distribution of a random variable $V$ satisfying the distributional equation $V\stackrel{\mathcal{D}}{=}f(V)$, where $f(v)=A\max\{v,D\}+B$ for $(A,B,D)\in(0,\infty)\times {\mathbb{R}}^2$. This paper is concerned with computational methods for evaluating these tail probabilities. We introduce a novel importance sampling algorithm, involving an exponential shift over a random time interval, for estimating these rare event probabilities. We prove that the proposed estimator is: (i) consistent, (ii) strongly efficient and (iii) optimal within a wide class of dynamic importance sampling estimators. Moreover, using extensions of ideas from nonlinear renewal theory, we provide a precise description of the running time of the algorithm. To establish these results, we develop new techniques concerning the convergence of moments of stopped perpetuity sequences, and the first entrance and last exit times of associated Markov chains on $\mathbb{R}$. We illustrate our methods with a variety of numerical examples which demonstrate the ease and scope of the implementation.