Theoretical and Empirical Validation of Heston Model

TL;DR

This paper combines theoretical derivations and empirical testing of the Heston model for option pricing, using market data and machine learning for parameter estimation to evaluate its practical effectiveness.

Contribution

It provides a comprehensive validation of the Heston model through theoretical analysis and empirical calibration with real market data, incorporating machine learning techniques.

Findings

Heston model effectively captures market volatility.

Machine learning improves parameter estimation accuracy.

Model calibration shows robustness in real-world data.

Abstract

This study focuses on the application of the Heston model to option pricing, employing both theoretical derivations and empirical validations. The Heston model, known for its ability to incorporate stochastic volatility, is derived and analyzed to evaluate its effectiveness in pricing options. For practical application, we utilize Monte Carlo simulations alongside market data from the Crude Oil WTI market to test the model's accuracy. Machine learning based optimization methods are also applied for the estimation of the five Heston parameters. By calibrating the model with real-world data, we assess its robustness and relevance in current financial markets, aiming to bridge the gap between theoretical finance models and their practical implementations.