real-time predictive options model - mathematical modeling
Numerical Solutions: Projects in Mathematical Modeling
Jaisal Friedman
This paper explores the Black-Scholes-Merton options pricing model, derives a predictive extension model, and visualizes both models in comparison to real-time pricing options pricing. The paper also explores various methodologies of calculating historical volatility. A portfolio of 5 U.S. Market Stocks and 1 index fund was taken as example for the project. The model was limited to visual analysis from real-time simulations as further explained in the extensions
The project was written in python. The Jupyter notebook is a reference of how to interact with the option_.py and pytradier.py files.
To configure the Library to run, rename the config_example.json file as config.json and enter your own details. You will need to get the required API keys, as well as install the python dependencies.
A sample of the generated 3D volatility surfaces is shown below.
Interactive Volatility Surfaces
Please reference the paper/latex file in the GitHub for specifics on the Math behind each model.
There’s some really interesting extensions, if you’d like to discuss please feel free to reach out to me :)
