Parallel-in-Time Iterative Methods for Pricing American Options

TL;DR

This paper introduces parallel-in-time iterative methods with novel preconditioners for efficiently solving all-at-once Hamilton-Jacobi-Bellman equations in American option pricing, enabling simultaneous computation across time steps.

Contribution

The paper proposes new parallel-in-time preconditioners for all-at-once HJB equations, improving efficiency in American option pricing beyond traditional sequential methods.

Findings

Methods demonstrate robust convergence in numerical tests

Significant reduction in computational time observed

Applicable to complex underlying asset models

Abstract

For pricing American options, %after suitable discretization in space and time, a sequence of discrete linear complementarity problems (LCPs) or equivalently Hamilton-Jacobi-Bellman (HJB) equations need to be solved in a sequential time-stepping manner. In each time step, the policy iteration or its penalty variant is often applied due to their fast convergence rates. In this paper, we aim to solve for all time steps simultaneously, by applying the policy iteration to an ``all-at-once form" of the HJB equations, where two different parallel-in-time preconditioners are proposed to accelerate the solution of the linear systems within the policy iteration. Our proposed methods are generally applicable for such all-at-once forms of the HJB equation, arising from option pricing problems with optimal stopping and nontrivial underlying asset models. Numerical examples are presented to show the feasibility and robust convergence behavior of the proposed methodology.