Stock Volatility Forecasting

A machine learning project to predict stock volatility using various time series models.

This project focuses on predicting stock volatility using various time series models. The notebook covers:

  1. Data Preparation: Loading historical stock data (e.g., from Yahoo Finance), calculating daily returns, and computing volatility (e.g., using rolling standard deviation).

  2. Exploratory Data Analysis (EDA): Visualizing stock prices, returns, and volatility over time to identify trends, seasonality, and other patterns.

  3. Model Implementation: Applying several time series models for forecasting volatility, which typically include:
    • ARIMA/SARIMA: Autoregressive Integrated Moving Average models for capturing linear dependencies.
    • GARCH (Generalized Autoregressive Conditional Heteroskedasticity): Models specifically designed to capture volatility clustering (periods of high volatility followed by high volatility, and vice versa).
    • Machine Learning Models: Potentially other models like Random Forests, Gradient Boosting, or Neural Networks if features are engineered from the time series data.

  4. Model Evaluation: Assessing the performance of the forecasting models using metrics relevant to time series prediction (e.g., RMSE, MAE, or specific metrics for volatility).

  5. Conclusion: Summarizing the findings and discussing the effectiveness of different models for stock volatility forecasting.

The primary goal is to demonstrate how to build and evaluate models for predicting future stock volatility, which is crucial for risk management and option pricing.