An artificial neural network representation of the SABR stochastic volatility model
Research output: Contribution to journal › Journal article › Research › peer-review
In this paper, the universal approximation theorem of artificial neural networks (ANNs) is applied to the stochastic alpha beta rho (SABR) stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al is considered, followed by the more accurate integration scheme of McGhee as well as a two-factor finite-difference scheme. The resulting ANN cal-culates 10 000 times faster than the finite-difference scheme while maintaining a high degree of accuracy. As a result, the ANN dispenses with the need for the commonly used SABR approximation.
Original language | English |
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Journal | Journal of Computational Finance |
Volume | 25 |
Issue number | 2 |
ISSN | 1460-1559 |
DOIs | |
Publication status | Published - 2021 |
Bibliographical note
Publisher Copyright:
© 2021 Infopro Digital Risk (IP) Limited.
- artificial neural network, SABR approximation, SABR integration scheme, stochastic alpha beta rho (SABR) model, stochastic volatility, universal approximation theorem
Research areas
ID: 306673106