Applied Machine Learning for Stochastic Local Volatility Calibration

Abstract

Stochastic volatility models are a popular choice to price and risk–manage financial derivatives on equity and foreign exchange. For the calibration of stochastic local volatility models a crucial step is the estimation of the expectated variance conditional on the realized spot. The spot is given by the model dynamics. Here we suggest to use methods from machine learning to improve the estimation process. We show examples from foreign exchange.