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Deep Calibration of Option Pricing Models
This dissertation investigates the calibration efficiency of short rate models using deep neural networks. The main focus is on the calibration of one-and-two factor Hull-White models to caplets and swaptions data, where the inputs are interest rate derivative prices or implied volatilities, and the...
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| Format: | Thesis |
| Language: | English |
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African Institute of Financial Markets and Risk Management
2023
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| _version_ | 1867613302127853568 |
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| access_status_str | Open Access |
| author | Dadah, Sahil |
| author2 | Ouwehand, Peter |
| author_browse | Dadah, Sahil Ouwehand, Peter |
| author_facet | Ouwehand, Peter Dadah, Sahil |
| author_sort | Dadah, Sahil |
| collection | Thesis |
| description | This dissertation investigates the calibration efficiency of short rate models using deep neural networks. The main focus is on the calibration of one-and-two factor Hull-White models to caplets and swaptions data, where the inputs are interest rate derivative prices or implied volatilities, and the outputs are the model parameters. A direct and indirect neural network calibration framework is adopted. The former method involves a direct inversion of the standard option pricing function using neural network. The indirect framework uses two consecutive steps; the first step estimates the option pricing function using a neural network. This is followed by applying the pre-trained model in a calibration procedure to fit the model parameters to a set of market observables. The neural networks are trained using simulated data and an optimum set of hyperparameters is obtained via the Bayesian optimization. The best set of hyperparameters is used to train the networks and tested on out-of-sample actual market yield curves data. It is shown that the direct method has substantial improvements in time with a sacrifice in accuracy (a mean relative error of 2.88%). On the other hand, using the indirect method, it is shown that the calibrated parameters reprice the set of options to a mean relative error of less than 0.1% (similar to numerical calibration), with a significant improvement in speed whose execution is twenty-six times faster compared to the conventional calibration procedures currently used. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37130 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:59.204Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | African Institute of Financial Markets and Risk Management |
| publisherStr | African Institute of Financial Markets and Risk Management |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37130 Deep Calibration of Option Pricing Models Dadah, Sahil Ouwehand, Peter Mathematical Finance This dissertation investigates the calibration efficiency of short rate models using deep neural networks. The main focus is on the calibration of one-and-two factor Hull-White models to caplets and swaptions data, where the inputs are interest rate derivative prices or implied volatilities, and the outputs are the model parameters. A direct and indirect neural network calibration framework is adopted. The former method involves a direct inversion of the standard option pricing function using neural network. The indirect framework uses two consecutive steps; the first step estimates the option pricing function using a neural network. This is followed by applying the pre-trained model in a calibration procedure to fit the model parameters to a set of market observables. The neural networks are trained using simulated data and an optimum set of hyperparameters is obtained via the Bayesian optimization. The best set of hyperparameters is used to train the networks and tested on out-of-sample actual market yield curves data. It is shown that the direct method has substantial improvements in time with a sacrifice in accuracy (a mean relative error of 2.88%). On the other hand, using the indirect method, it is shown that the calibrated parameters reprice the set of options to a mean relative error of less than 0.1% (similar to numerical calibration), with a significant improvement in speed whose execution is twenty-six times faster compared to the conventional calibration procedures currently used. 2023-03-02T09:22:04Z 2023-03-02T09:22:04Z 2022 2023-02-20T12:30:24Z Master Thesis Masters MPhil http://hdl.handle.net/11427/37130 eng application/pdf African Institute of Financial Markets and Risk Management Faculty of Commerce |
| spellingShingle | Mathematical Finance Dadah, Sahil Deep Calibration of Option Pricing Models |
| thesis_degree_str | Master's |
| title | Deep Calibration of Option Pricing Models |
| title_full | Deep Calibration of Option Pricing Models |
| title_fullStr | Deep Calibration of Option Pricing Models |
| title_full_unstemmed | Deep Calibration of Option Pricing Models |
| title_short | Deep Calibration of Option Pricing Models |
| title_sort | deep calibration of option pricing models |
| topic | Mathematical Finance |
| url | http://hdl.handle.net/11427/37130 |
| work_keys_str_mv | AT dadahsahil deepcalibrationofoptionpricingmodels |