Applying Deep Learning to Calibrate Stochastic Volatility Models ...
Stochastic volatility models, where the volatility is a stochastic process, can capture most of the essential stylized facts of implied volatility surfaces and give more realistic dynamics of the volatility smile/skew. However, they come with the significant issue that they take too long to calibrat...
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Online Access: | https://dx.doi.org/10.48550/arxiv.2309.07843 https://arxiv.org/abs/2309.07843 |
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ftdatacite:10.48550/arxiv.2309.07843 2023-11-05T03:41:36+01:00 Applying Deep Learning to Calibrate Stochastic Volatility Models ... Sridi, Abir Bilokon, Paul 2023 https://dx.doi.org/10.48550/arxiv.2309.07843 https://arxiv.org/abs/2309.07843 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computational Finance q-fin.CP Artificial Intelligence cs.AI Mathematical Finance q-fin.MF Pricing of Securities q-fin.PR Risk Management q-fin.RM FOS Economics and business FOS Computer and information sciences Article article CreativeWork Preprint 2023 ftdatacite https://doi.org/10.48550/arxiv.2309.07843 2023-10-09T11:05:20Z Stochastic volatility models, where the volatility is a stochastic process, can capture most of the essential stylized facts of implied volatility surfaces and give more realistic dynamics of the volatility smile/skew. However, they come with the significant issue that they take too long to calibrate. Alternative calibration methods based on Deep Learning (DL) techniques have been recently used to build fast and accurate solutions to the calibration problem. Huge and Savine developed a Differential Machine Learning (DML) approach, where Machine Learning models are trained on samples of not only features and labels but also differentials of labels to features. The present work aims to apply the DML technique to price vanilla European options (i.e. the calibration instruments), more specifically, puts when the underlying asset follows a Heston model and then calibrate the model on the trained network. DML allows for fast training and accurate pricing. The trained neural network dramatically reduces Heston ... Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
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language |
unknown |
topic |
Computational Finance q-fin.CP Artificial Intelligence cs.AI Mathematical Finance q-fin.MF Pricing of Securities q-fin.PR Risk Management q-fin.RM FOS Economics and business FOS Computer and information sciences |
spellingShingle |
Computational Finance q-fin.CP Artificial Intelligence cs.AI Mathematical Finance q-fin.MF Pricing of Securities q-fin.PR Risk Management q-fin.RM FOS Economics and business FOS Computer and information sciences Sridi, Abir Bilokon, Paul Applying Deep Learning to Calibrate Stochastic Volatility Models ... |
topic_facet |
Computational Finance q-fin.CP Artificial Intelligence cs.AI Mathematical Finance q-fin.MF Pricing of Securities q-fin.PR Risk Management q-fin.RM FOS Economics and business FOS Computer and information sciences |
description |
Stochastic volatility models, where the volatility is a stochastic process, can capture most of the essential stylized facts of implied volatility surfaces and give more realistic dynamics of the volatility smile/skew. However, they come with the significant issue that they take too long to calibrate. Alternative calibration methods based on Deep Learning (DL) techniques have been recently used to build fast and accurate solutions to the calibration problem. Huge and Savine developed a Differential Machine Learning (DML) approach, where Machine Learning models are trained on samples of not only features and labels but also differentials of labels to features. The present work aims to apply the DML technique to price vanilla European options (i.e. the calibration instruments), more specifically, puts when the underlying asset follows a Heston model and then calibrate the model on the trained network. DML allows for fast training and accurate pricing. The trained neural network dramatically reduces Heston ... |
format |
Article in Journal/Newspaper |
author |
Sridi, Abir Bilokon, Paul |
author_facet |
Sridi, Abir Bilokon, Paul |
author_sort |
Sridi, Abir |
title |
Applying Deep Learning to Calibrate Stochastic Volatility Models ... |
title_short |
Applying Deep Learning to Calibrate Stochastic Volatility Models ... |
title_full |
Applying Deep Learning to Calibrate Stochastic Volatility Models ... |
title_fullStr |
Applying Deep Learning to Calibrate Stochastic Volatility Models ... |
title_full_unstemmed |
Applying Deep Learning to Calibrate Stochastic Volatility Models ... |
title_sort |
applying deep learning to calibrate stochastic volatility models ... |
publisher |
arXiv |
publishDate |
2023 |
url |
https://dx.doi.org/10.48550/arxiv.2309.07843 https://arxiv.org/abs/2309.07843 |
genre |
DML |
genre_facet |
DML |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2309.07843 |
_version_ |
1781698036167081984 |