An adjoint method for the exact calibration of Stochastic Local Volatility models

TL;DR

This paper introduces an adjoint semidiscretization approach for the exact calibration of stochastic local volatility models to local volatility models, ensuring identical option prices through solving a nonlinear system.

Contribution

It presents a novel adjoint semidiscretization of the forward Kolmogorov equation for precise calibration of SLV models, combining ADI time stepping and inner iteration techniques.

Findings

Effective calibration of SLV models demonstrated through numerical experiments.

Exact matching of European option prices between SLV and LV models achieved.

The method efficiently handles the non-linearity in the calibration process.

Abstract

This paper deals with the exact calibration of semidiscretized stochastic local volatility (SLV) models to their underlying semidiscretized local volatility (LV) models. Under an SLV model, it is common to approximate the fair value of European-style options by semidiscretizing the backward Kolmogorov equation using finite differences. In the present paper we introduce an adjoint semidiscretization of the corresponding forward Kolmogorov equation. This adjoint semidiscretization is used to obtain an expression for the leverage function in the pertinent SLV model such that the approximated fair values defined by the LV and SLV models are identical for non-path-dependent European-style options. In order to employ this expression, a large non-linear system of ODEs needs to be solved. The actual numerical calibration is performed by combining ADI time stepping with an inner iteration to handle the non-linearity. Ample numerical experiments are presented that illustrate the effectiveness of the calibration procedure.