Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Formulation Of A Rational Option Pricing Model using Artificial Neural Networks

Version 1 : Received: 15 December 2020 / Approved: 17 December 2020 / Online: 17 December 2020 (16:22:34 CET)

How to cite: yadav, K.; yadav, A. Formulation Of A Rational Option Pricing Model using Artificial Neural Networks. Preprints 2020, 2020120426 (doi: 10.20944/preprints202012.0426.v1). yadav, K.; yadav, A. Formulation Of A Rational Option Pricing Model using Artificial Neural Networks. Preprints 2020, 2020120426 (doi: 10.20944/preprints202012.0426.v1).

Abstract

This paper inquires on the options pricing modeling using Artificial Neural Networks to price Apple(AAPL) European Call Options. Our model is based on the premise that Artificial Neural Networks can be used as functional approximators and can be used as an alternative to the numerical methods to some extent, for a faster and an efficient solution. This paper provides a neural network solution for two financial models, the BlackScholes-Merton model, and the calibrated-Heston Stochastic Volatility Model, we evaluate our predictions using the existing numerical solutions for the same, the analytic solution for the Black-Scholes equation, COS-Model for Heston’s Stochastic Volatility Model and Standard Heston-Quasi analytic formula. The aim of this study is to find a viable time-efficient alternative to existing quantitative models for option pricing.

Subject Areas

Black Scholes Equation; Heston Model Calibration; Option Pricing; Stochastic Processes; Artificial Neural Networks

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