Machine Learning SABR Model of Stochastic Volatility With Lookup Table
27 Pages Posted: 8 Jun 2020 Last revised: 22 Jul 2020
Date Written: July 20, 2020
We present an embarrassingly simple method for supervised learning of SABR model’s European option price function based on lookup table or rote machine learning. Performance in time domain is comparable to generally used analytic approximations utilized in financial industry. However, unlike the approximation schemes based on asymptotic methods – universally deemed fastest – the methodology admits arbitrary calculation precision to the true pricing function without detrimental impact on time performance apart from memory access latency. The idea is plainly applicable to any function approximation or supervised learning domain with low dimension.
Keywords: Derivative Pricing, Option Pricing, Interest Rates, Machine Learning, Supervised Learning, Functional Approximation, Artificial Neural Network, Stochastic Volatility, SABR.
JEL Classification: C13, C15, C63, C65, D40, G12, G13
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