Option Pricing with Machine Learning

Classic machine learning approaches for financial derivatives pricing

Option Pricing with Classic Machine Learning

This project explores the application of traditional machine learning algorithms to option pricing problems, bridging quantitative finance with modern data science techniques. The work demonstrates how classical ML methods can effectively capture complex market dynamics for accurate derivatives pricing.

Project Overview

Financial derivatives pricing has traditionally relied on mathematical models like Black-Scholes. This project investigates how machine learning algorithms can enhance or replace these classical approaches, particularly for complex scenarios where traditional models may fall short.

Key Features

  • Multiple ML Algorithms: Implementation of various classic machine learning techniques
  • Comparative Analysis: Systematic comparison with traditional option pricing models
  • Real Market Data: Training and validation on actual financial market datasets
  • Performance Metrics: Comprehensive evaluation using financial accuracy measures

Technical Implementation

  • Data Processing: Market data cleaning, feature engineering, and preprocessing
  • Model Training: Implementation of regression and ensemble methods
  • Validation Framework: Time-series cross-validation appropriate for financial data
  • Risk Analysis: Assessment of pricing accuracy under different market conditions

Machine Learning Approaches

  • Regression Models: Linear and polynomial regression for baseline pricing
  • Ensemble Methods: Random Forest and Gradient Boosting for complex patterns
  • Feature Engineering: Technical indicators and market microstructure features
  • Model Selection: Systematic evaluation and hyperparameter optimization

Applications

  • European Options: Standard call and put option pricing
  • Market Volatility: Capturing implied volatility dynamics
  • Risk Management: Portfolio-level risk assessment and hedging strategies
  • Backtesting: Historical performance analysis and strategy validation

Technical Stack

  • Languages: Python, R
  • Libraries: Scikit-learn, Pandas, NumPy, Matplotlib
  • Data Sources: Financial market APIs and historical datasets
  • Visualization: Comprehensive plotting for model interpretation

Research Insights

This work provides valuable insights into the effectiveness of classical machine learning in quantitative finance, demonstrating both the potential and limitations of data-driven approaches in financial modeling.

The project serves as a foundation for understanding how AI techniques can be integrated into traditional financial workflows, bridging academic research with practical industry applications.