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

Abstract

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

Suggested Citation

Lokvancic, Mahir, Machine Learning SABR Model of Stochastic Volatility With Lookup Table (July 20, 2020). Available at SSRN: https://ssrn.com/abstract=3589367 or http://dx.doi.org/10.2139/ssrn.3589367

Mahir Lokvancic (Contact Author)

Bloomberg L.P. ( email )

731 Lexington Avenue
New York, NY 10022
United States

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