Volatility Forecasting & Model Validation in Equity Markets

Objective

Forecast and compare volatility models (ARIMA, GARCH, ML) on S&P 500 returns.

Data

• S&P 500 (Yahoo Finance)
• VIX (Yahoo Finance)

Project Structure

• data/ -> raw and processed data
• notebooks/ -> exploratory and modeling notebooks
• src/ -> reusable scripts (loading, preprocessing, models)
• README.md -> project overview
• requirements.txt -> Python packages

Methodology

1. Data cleaning & exploration
2. Log return calculation
3. Stationarity & autocorrelation analysis
4. ARIMA modeling
5. GARCH modeling
6. Walk-forward backtesting
7. Comparison & discussion

Key Insights

Under Construction

Environment Setup

git clone
https://github.com/1412richa/volatility-forecasting.git
cd volatility-forecasting

python -m venv .venv
source venv\Scripts\activate

pip install -r requirements.txt