AUTHOR=Hakala Jürgen TITLE=Applied Machine Learning for Stochastic Local Volatility Calibration JOURNAL=Frontiers in Artificial Intelligence VOLUME=2 YEAR=2019 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2019.00004 DOI=10.3389/frai.2019.00004 ISSN=2624-8212 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.