Applied Machine Learning for Stochastic Local Volatility Calibration
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Updated: Nov 12, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
1Quantitative Modeling Department, Leonteq Securities AG, Zurich, Switzerland.
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.
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