Option Pricing Using Ensemble Learning

TL;DR

This paper explores the use of ensemble learning for option pricing, demonstrating improved accuracy and robustness through novel experimental strategies and integration of financial theory with machine learning techniques.

Contribution

It introduces a new experimental approach with parameter transfer and a scoring evaluation mechanism that combines financial theory and machine learning for enhanced option pricing.

Findings

Ensemble learning outperforms classical models in accuracy and robustness.

Parameter transfer improves simulation realism and stability.

The interaction between sliding window and noise reveals patterns relevant to data science.

Abstract

Ensemble learning is characterized by flexibility, high precision, and refined structure. As a critical component within computational finance, option pricing with machine learning requires both high predictive accuracy and reduced structural complexity-features that align well with the inherent advantages of ensemble learning. This paper investigates the application of ensemble learning to option pricing, and conducts a comparative analysis with classical machine learning models to assess their performance in terms of accuracy, local feature extraction, and robustness to noise. A novel experimental strategy is introduced, leveraging parameter transfer across experiments to improve robustness and realism in financial simulations.Building upon this strategy, an evaluation mechanism is developed that incorporates a scoring strategy and a weighted evaluation strategy explicitly emphasizing the foundational role of financial theory. This mechanism embodies an orderly integration of theoretical finance and computational methods. In addition, the study examines the interaction between sliding window technique and noise, revealing nuanced patterns that suggest a potential connection relevant to ongoing research in machine learning and data science.