This repository contains a collection of Jupyter notebooks focused on deep hedging and option pricing models. Each notebook explores different methods and techniques used in the field of quantitative finance, particularly in the context of hedging and pricing financial derivatives.
- Introduction to Deep Hedging
- Longstaff-Schwarz Method Using Feedforward Neural Networks
- Pricing American Options Using the Cox-Ross-Rubinstein (CRR) Model
- Bermudian Put Option Pricing
Notebook: Introduction to deep hedging.ipynb
Description: This notebook introduces the concept of deep hedging, a modern approach to hedging financial derivatives using deep learning techniques. The notebook covers:
- Overview of traditional hedging methods and their limitations.
- Introduction to deep hedging and its advantages.
- Implementation of a deep hedging model using TensorFlow/PyTorch.
- Example scenarios and results analysis.
Key Sections:
- Introduction: Background on hedging and the need for deep hedging.
- Model Architecture: Detailed explanation of the neural network used for hedging.
- Training and Evaluation: Steps to train the model and evaluate its performance.
- Case Studies: Practical examples demonstrating the application of the deep hedging model.
Notebook: longstaff-schwarz-using-fnn.ipynb
Description: This notebook applies the Longstaff-Schwarz method for valuing American options using feedforward neural networks (FNN). It includes:
- Introduction to the Longstaff-Schwarz method.
- Explanation of the neural network approach to implement this method.
- Code for building, training, and evaluating the model.
- Comparison with traditional methods.
Key Sections:
- Overview of Longstaff-Schwarz Method: Fundamentals and significance in option pricing.
- Neural Network Implementation: Step-by-step guide to implementing FNN for the method.
- Model Training: Techniques and parameters used in training the model.
- Results and Analysis: Evaluation of model performance and comparison with traditional methods.
Notebook: Princig american option using CRR model.ipynb
Description: This notebook focuses on pricing American options using the Cox-Ross-Rubinstein (CRR) binomial tree model. It covers:
- Detailed explanation of the CRR model.
- Implementation of the binomial tree algorithm.
- Code for pricing American options using this method.
- Analysis of results and comparison with other models.
Key Sections:
- Introduction to CRR Model: Theory and mathematics behind the CRR model.
- Algorithm Implementation: Code snippets and detailed steps to implement the model.
- Pricing American Options: Applying the model to price American options.
- Performance Evaluation: Analyzing the accuracy and efficiency of the model.
Notebook: Bermudian Put.ipynb
Description: This notebook deals with the pricing of Bermudian put options. It includes:
- Introduction to Bermudian options and their characteristics.
- Implementation of a pricing model for Bermudian put options.
- Detailed code examples and explanations.
- Comparison with European and American options pricing.
Key Sections:
- Bermudian Options Overview: Explanation of Bermudian options and their unique features.
- Pricing Model Implementation: Step-by-step guide to pricing Bermudian put options.
- Code and Examples: Practical examples with detailed code.
- Comparative Analysis: Differences between Bermudian, European, and American options.
Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
This repository is licensed under the MIT License. See the LICENSE file for more details.
For any questions or suggestions, feel free to open an issue.