Deep Calibration of Option Pricing Models
| dc.contributor.advisor | Ouwehand, Peter | |
| dc.contributor.author | Dadah, Sahil | |
| dc.date.accessioned | 2023-03-02T09:22:04Z | |
| dc.date.available | 2023-03-02T09:22:04Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-02-20T12:30:24Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | Dadah, S. (2022). <i>Deep Calibration of Option Pricing Models</i>. (). ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. Retrieved from http://hdl.handle.net/11427/37130 | en_ZA |
| dc.identifier.chicagocitation | Dadah, Sahil. <i>"Deep Calibration of Option Pricing Models."</i> ., ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2022. http://hdl.handle.net/11427/37130 | en_ZA |
| dc.identifier.citation | Dadah, S. 2022. Deep Calibration of Option Pricing Models. . ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management. http://hdl.handle.net/11427/37130 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Dadah, Sahil AB - 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. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Mathematical Finance LK - https://open.uct.ac.za PY - 2022 T1 - Deep Calibration of Option Pricing Models TI - Deep Calibration of Option Pricing Models UR - http://hdl.handle.net/11427/37130 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37130 | |
| dc.identifier.vancouvercitation | Dadah S. Deep Calibration of Option Pricing Models. []. ,Faculty of Commerce ,African Institute of Financial Markets and Risk Management, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37130 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | African Institute of Financial Markets and Risk Management | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.subject | Mathematical Finance | |
| dc.title | Deep Calibration of Option Pricing Models | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MPhil |