Fast Computation of Path Integrals of Killed Processes Using Confined Stochastic Bridges

TL;DR

This paper introduces a new stochastic method for efficiently computing path integrals of killed processes with negligible bias, avoiding full path simulation and reducing computational costs, applicable to physics, chemistry, and finance.

Contribution

The paper presents a novel approach linking stochastic bridges and killed processes to evaluate path integrals without simulating entire paths, significantly improving efficiency and accuracy.

Findings

Method achieves negligible bias in path integral evaluation.

Explicit density derivation for Brownian bridges in n-balls.

Numerical examples demonstrate superior efficiency over Euler-Maruyama.

Abstract

Expectations of path integrals of killed stochastic processes play a central role in several applications across physics, chemistry, and finance. Simulation-based evaluation of these functionals is often biased and numerically expensive due to the need to explicitly approximate stochastic paths and the challenge of correctly modeling them in the neighborhood of the killing boundary. We consider It\^{o} processes killed at the boundary of some set in the $n$-dimensional space and introduce a novel stochastic method with negligible bias and lower computational cost to evaluate path integrals without simulated paths. Our approach draws a connection between stochastic bridges and killed processes to sample only exit times and locations instead of the full path. We apply it to a Wiener process killed in the $n$-ball and explicitly derive the density of the Brownian bridge confined to the $n$-ball for $n = 1, 2, 3$. Finally, we present two numerical examples that demonstrate the efficiency and negligible bias of the novel procedure compared to an evaluation using the standard Euler-Maruyama method.