A partial Fourier transform method for a class of hypoelliptic Kolmogorov equations

TL;DR

This paper introduces a numerical method combining partial Fourier transform and finite differences to solve hypoelliptic Kolmogorov equations, demonstrating second-order spatial convergence and applying it to finance-related PDEs.

Contribution

The paper develops a novel partial Fourier transform approach for hypoelliptic equations with non-commuting operators, achieving second-order convergence and extending to finance applications.

Findings

Second-order convergence in spatial mesh size.

Exponential convergence of inverse Fourier transform approximation.

Successful application to a finance PDE modeling hedging error.

Abstract

We consider hypoelliptic Kolmogorov equations in $n+1$ spatial dimensions, with $n\geq 1$, where the differential operator in the first $n$ spatial variables featuring in the equation is second-order elliptic, and with respect to the $(n+1)$st spatial variable the equation contains a pure transport term only and is therefore first-order hyperbolic. If the two differential operators, in the first $n$ and in the $(n+1)$st co-ordinate directions, do not commute, we benefit from hypoelliptic regularization in time, and the solution for $t>0$ is smooth even for a Dirac initial datum prescribed at $t=0$. We study specifically the case where the coefficients depend only on the first $n$ variables. In that case, a Fourier transform in the last variable and standard central finite difference approximation in the other variables can be applied for the numerical solution. We prove second-order convergence in the spatial mesh size for the model hypoelliptic equation $\frac{\partial u}{\partial t} + x \frac{\partial u}{\partial y} = \frac{\partial^2 u}{\partial x^2}$ subject to the initial condition $u(x,y,0) = \delta (x) \delta (y)$, with $(x,y) \in \mathbb{R} \times\mathbb{R}$ and $t>0$, proposed by Kolmogorov, and for an extension with $n=2$. We also demonstrate exponential convergence of an approximation of the inverse Fourier transform based on the trapezium rule. Lastly, we apply the method to a PDE arising in mathematical finance, which models the distribution of the hedging error under a mis-specified derivative pricing model.