TY - UNPB
T1 - An Artificial Neural Network Representation of the SABR Stochastic Volatility Model
AU - McGhee, William A
PY - 2018/12/14
Y1 - 2018/12/14
N2 - In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate integration scheme of McGhee [2011] as well as a two factor finite difference scheme. The resulting ANN calculates 10,000 times faster than the finite difference scheme whilst maintaining a high degree of accuracy. As a result, the ANN dispenses with the need for the commonly used SABR Approximation.
AB - In this article, the Universal Approximation Theorem of Artificial Neural Networks (ANNs) is applied to the SABR stochastic volatility model in order to construct highly efficient representations. Initially, the SABR approximation of Hagan et al. [2002] is considered, then a more accurate integration scheme of McGhee [2011] as well as a two factor finite difference scheme. The resulting ANN calculates 10,000 times faster than the finite difference scheme whilst maintaining a high degree of accuracy. As a result, the ANN dispenses with the need for the commonly used SABR Approximation.
KW - Stochastic Volatility
KW - SABR Model
KW - SABR Approximation
KW - SABR Integration Scheme
KW - Artificial Neural Network
KW - Universal Approximation Theorem
U2 - 10.2139/ssrn.3288882
DO - 10.2139/ssrn.3288882
M3 - Working paper
BT - An Artificial Neural Network Representation of the SABR Stochastic Volatility Model
ER -