Generating Volatility Surfaces using Variational Autoencoders
(2024) In Master's Theses in Mathematical Sciences FMAM05 20241Mathematics (Faculty of Engineering)
- Abstract
- This study presents an in-depth exploration into the utilization of Variational Autoencoders (VAEs) for modeling and completing implied volatility surfaces within the context of the index equities market, a crucial aspect of option pricing. Moreover, our study examines the predictive capabilities of neural networks concerning fluctuations in spot prices, with a specialized spot model calibrated to forecast changes in volatility surfaces based on spot price dynamics. Through comprehensive data processing and structuring of VAEs we created a model capable of generating accurate and nearly arbitrage-free volatility surfaces from as little as 10 points of information. This model also proved proficiency in generating volatility surfaces for... (More)
- This study presents an in-depth exploration into the utilization of Variational Autoencoders (VAEs) for modeling and completing implied volatility surfaces within the context of the index equities market, a crucial aspect of option pricing. Moreover, our study examines the predictive capabilities of neural networks concerning fluctuations in spot prices, with a specialized spot model calibrated to forecast changes in volatility surfaces based on spot price dynamics. Through comprehensive data processing and structuring of VAEs we created a model capable of generating accurate and nearly arbitrage-free volatility surfaces from as little as 10 points of information. This model also proved proficiency in generating volatility surfaces for previously unseen underlying assets. Applying changes in spot price as a conditional variable we successfully created a powerful risk management tool capable of forecasting volatility surfaces for various future scenarios.
Although our model can be improved upon, our findings underscore the robustness and generalizability of VAEs, showcasing their potential for broader application across various financial instruments and markets. (Less) - Popular Abstract (Swedish)
- Generering av volatilitetsytor är en väsentlig del i prissättning av optioner. Dagens modeller är mycket komplexa och saknar egenskapen att kunna representera alla möjliga ytor, därför ses neurala nätverk som ett alternativ. I denna rapport undersöker vi användbarheten av just Variational Autoencoders på aktiemarknaden.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9153880
- author
- Immo Barasciutti, Michael LU and Fossum, Axel LU
- supervisor
- organization
- course
- FMAM05 20241
- year
- 2024
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Variational Autoencoder (VAE), static arbitrage, implied volatility, volatility surface, index equity options, neural network, machine learning, quantitative finance
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3534-2024
- ISSN
- 1404-6342
- other publication id
- 2024:E22
- language
- English
- id
- 9153880
- date added to LUP
- 2024-06-28 16:05:17
- date last changed
- 2024-06-28 16:05:17
@misc{9153880, abstract = {{This study presents an in-depth exploration into the utilization of Variational Autoencoders (VAEs) for modeling and completing implied volatility surfaces within the context of the index equities market, a crucial aspect of option pricing. Moreover, our study examines the predictive capabilities of neural networks concerning fluctuations in spot prices, with a specialized spot model calibrated to forecast changes in volatility surfaces based on spot price dynamics. Through comprehensive data processing and structuring of VAEs we created a model capable of generating accurate and nearly arbitrage-free volatility surfaces from as little as 10 points of information. This model also proved proficiency in generating volatility surfaces for previously unseen underlying assets. Applying changes in spot price as a conditional variable we successfully created a powerful risk management tool capable of forecasting volatility surfaces for various future scenarios. Although our model can be improved upon, our findings underscore the robustness and generalizability of VAEs, showcasing their potential for broader application across various financial instruments and markets.}}, author = {{Immo Barasciutti, Michael and Fossum, Axel}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Generating Volatility Surfaces using Variational Autoencoders}}, year = {{2024}}, }