In the world of financial markets, understanding and forecasting stock price volatility is crucial for making informed investment decisions and managing risks effectively. This project leverages the power of GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models and Artificial Neural Networks (ANN) to develop robust and accurate tools for predicting stock price volatility. By combining the time-tested statistical approach of GARCH with the deep learning capabilities of ANN, I aim to provide investors and analysts with a reliable framework for modeling and forecasting stock market volatility. Dive into my code, data, and documentation to explore how this hybrid approach can enhance your stock market analysis and decision-making processes.
