Simulation of the drawdown and its duration in L\'{e}vy models via stick-breaking Gaussian approximation

TL;DR

This paper introduces a new simulation algorithm for expected drawdown functionals in exponential Lévy models, leveraging Gaussian approximation and Wasserstein bounds, significantly improving computational efficiency especially with high jump activity.

Contribution

It presents a novel simulation method for Lévy processes using Gaussian approximation and Wasserstein bounds, reducing computational complexity for high jump activity scenarios.

Findings

Algorithm achieves up to two orders of magnitude efficiency gain.

Numerical results align well with theoretical bounds.

Method effectively handles discontinuous payoffs.

Abstract

We develop a computational method for expected functionals of the drawdown and its duration in exponential L\'evy models. It is based on a novel simulation algorithm for the joint law of the state, supremum and time the supremum is attained of the Gaussian approximation of a general L\'evy process. We bound the bias for various locally Lipschitz and discontinuous payoffs arising in applications and analyse the computational complexities of the corresponding Monte Carlo and multilevel Monte Carlo estimators. Monte Carlo methods for L\'evy processes (using Gaussian approximation) have been analysed for Lipschitz payoffs, in which case the computational complexity of our algorithm is up to two orders of magnitude smaller when the jump activity is high. At the core of our approach are bounds on certain Wasserstein distances, obtained via the novel SBG coupling between a L\'evy process and its Gaussian approximation. Numerical performance, based on the implementation in the dedicated GitHub repository, exhibits a good agreement with our theoretical bounds.