Conditional Expectation expression in mean-field SDEs and its applications

TL;DR

This paper introduces a new method for calculating conditional expectations in jump-diffusion mean-field SDEs, improving numerical pricing accuracy for American options using advanced calculus techniques.

Contribution

It presents a novel formulation combining unconditioned expectations with weighting factors via Malliavin calculus, enhancing estimation in complex stochastic models.

Findings

Significant improvement in American put option pricing accuracy

Effective application of Malliavin calculus on Poisson space

Enhanced estimation techniques for jump-diffusion mean-field models

Abstract

This study developed a novel formulation of conditional expectations within the framework of a jump-diffusion mean-field stochastic differential equation. We introduce an integrated approach that combines unconditioned expectations with rigorously defined weighting factors, employing Malliavin calculus on Poisson space and directional derivatives to enhance estimation accuracy. \noindent The proposed method is applied to the numerical pricing of American put options in a jump-diffusion mean-field setting, addressing the challenges proposed by early-exercise features. Comprehensive numerical experiments demonstrate substantial improvements in pricing accuracy compared with conventional techniques.