Applied Machine Learning for Stochastic Local Volatility Calibration

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 b...

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Detalles Bibliográficos
Autor principal: Hakala, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861308/
https://www.ncbi.nlm.nih.gov/pubmed/33733093
http://dx.doi.org/10.3389/frai.2019.00004
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author Hakala, Jürgen
author_facet Hakala, Jürgen
author_sort Hakala, Jürgen
collection PubMed
description 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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spelling pubmed-78613082021-03-16 Applied Machine Learning for Stochastic Local Volatility Calibration Hakala, Jürgen Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2019-05-17 /pmc/articles/PMC7861308/ /pubmed/33733093 http://dx.doi.org/10.3389/frai.2019.00004 Text en Copyright © 2019 Hakala. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Artificial Intelligence
Hakala, Jürgen
Applied Machine Learning for Stochastic Local Volatility Calibration
title Applied Machine Learning for Stochastic Local Volatility Calibration
title_full Applied Machine Learning for Stochastic Local Volatility Calibration
title_fullStr Applied Machine Learning for Stochastic Local Volatility Calibration
title_full_unstemmed Applied Machine Learning for Stochastic Local Volatility Calibration
title_short Applied Machine Learning for Stochastic Local Volatility Calibration
title_sort applied machine learning for stochastic local volatility calibration
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861308/
https://www.ncbi.nlm.nih.gov/pubmed/33733093
http://dx.doi.org/10.3389/frai.2019.00004
work_keys_str_mv AT hakalajurgen appliedmachinelearningforstochasticlocalvolatilitycalibration

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