Derivative pricing as a transport problem: MPDATA solutions to Black-Scholes-type equations

TL;DR

This paper introduces a novel numerical framework using MPDATA to solve Black-Scholes-type equations by transforming them into advection problems, achieving high accuracy and enabling solutions for American options.

Contribution

It develops a transport-based numerical approach for option pricing, connecting financial models with transport phenomena and demonstrating its effectiveness for European and American options.

Findings

Achieves second-order accuracy in time and space.

Successfully compares MPDATA solutions with analytical formulas.

Extends to American options via free boundary problem solution.

Abstract

We discuss in this note applications of the Multidimensional Positive Definite Advection Transport Algorithm (MPDATA) to numerical solutions of partial differential equations arising from stochastic models in quantitative finance. In particular, we develop a framework for solving Black-Scholes-type equations by first transforming them into advection-diffusion problems, and numerically integrating using an iterative explicit finite-difference approach, in which the Fickian term is represented as an additional advective term. We discuss the correspondence between transport phenomena and financial models, uncovering the possibility of expressing the no-arbitrage principle as a conservation law. We depict second-order accuracy in time and space of the embraced numerical scheme. This is done in a convergence analysis comparing MPDATA numerical solutions with classic Black-Scholes analytical formulae for the valuation of European options. We demonstrate in addition a way of applying MPDATA to solve the free boundary problem (leading to a linear complementarity problem) for the valuation of American options. We finally comment on the potential the MPDATA framework has with respect to being applied in tandem with more complex models typically used in quantitive finance.