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. Preprints2020, 2020120426. https://doi.org/10.20944/preprints202012.0426.v1
yadav, K.; yadav, A. Formulation Of A Rational Option Pricing Model
using Artificial Neural Networks. Preprints 2020, 2020120426. https://doi.org/10.20944/preprints202012.0426.v1
yadav, K.; yadav, A. Formulation Of A Rational Option Pricing Model
using Artificial Neural Networks. Preprints2020, 2020120426. https://doi.org/10.20944/preprints202012.0426.v1
APA Style
yadav, K., & yadav, A. (2020). Formulation Of A Rational Option Pricing Model
using Artificial Neural Networks. Preprints. https://doi.org/10.20944/preprints202012.0426.v1
Chicago/Turabian Style
yadav, K. and Anubhuti yadav. 2020 "Formulation Of A Rational Option Pricing Model
using Artificial Neural Networks" Preprints. https://doi.org/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.
Keywords
Black Scholes Equation; Heston Model Calibration; Option Pricing; Stochastic Processes; Artificial Neural Networks
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
