Welcome to our project repository on implementing the Cox-Ross-Rubinstein (CRR) option pricing model. This README serves as a guide to understanding the purpose, implementation, and potential applications of our project.
In this project, we delve into the Cox-Ross-Rubinstein (CRR) option pricing model, which is a discrete-time, binomial tree-based model for pricing options. The CRR model provides a framework for valuing options by simulating possible future price movements of the underlying asset over discrete time steps. This allows for the calculation of option prices at each node of the binomial tree, ultimately leading to the determination of the fair value of the option.
The primary objective of this project is to demonstrate the implementation and application of the Cox-Ross-Rubinstein option pricing model. By understanding and implementing the CRR model, users can gain insights into option pricing theory and its practical implications in financial markets.
The project is implemented using Python programming language, leveraging libraries such as NumPy and pandas for numerical computation and data manipulation. We construct a binomial tree representing possible price paths of the underlying asset and use the CRR algorithm to calculate option prices at each node.
- Clone the repository to your local machine.
- Ensure you have Python installed along with the required libraries mentioned.
- Navigate to the
notebooksdirectory and open the Jupyter notebooks to follow the implementation steps and examples. - Execute the code cells in the notebooks sequentially to replicate the analysis and results.
- Customize parameters or example scenarios as needed for your specific use case.
- Michelangelo D'andrea
- Lorenzo Calamida
Enjoy exploring the fascinating world of option pricing with the Cox-Ross-Rubinstein model!