BSc IT Project Guide: Volatility Forecasting Using Historical Stock Data
1. Introduction
Volatility forecasting is a critical component in financial markets, aiding in risk management, option pricing, and portfolio allocation. This project aims to predict stock market volatility using historical data and machine learning techniques, providing insights that can help investors and analysts make informed decisions.
2. Objectives
- To collect and preprocess historical stock market data.
- To explore various volatility measures (e.g., historical volatility, GARCH
models).
- To apply machine learning models (e.g., Random Forest, LSTM) for forecasting
volatility.
- To evaluate the performance of the models using appropriate metrics.
- To visualize predicted versus actual volatility to demonstrate accuracy.
3. Tools and Technologies
- Python (Pandas, NumPy, scikit-learn, Keras/TensorFlow)
- Jupyter Notebook or any IDE
- Financial data APIs (e.g., Yahoo Finance, Alpha Vantage)
- Matplotlib/Seaborn for data visualization
4. System Architecture
1. Data Collection: Download historical stock price data.
2. Data Preprocessing: Clean and format the data.
3. Feature Engineering: Create features like log returns, moving averages.
4. Model Building: Train models such as GARCH, LSTM, or Random Forest.
5. Evaluation: Compare predictions with actual volatility metrics.
6. Visualization: Graph results for interpretation.
5. Methodology
- Load historical price data for selected stocks.
- Calculate rolling volatility using standard deviation of log returns.
- Train a machine learning model using historical data and lag features.
- Test the model on unseen data and evaluate performance (RMSE, MAE).
- Plot and interpret the results.
6. Expected Outcome
A functional model capable of forecasting future stock market volatility with reasonable accuracy. Charts comparing actual and predicted volatility trends, and a report summarizing model performance and insights.
7. Future Scope
- Integration with real-time data sources.
- Deployment of the model as a web-based dashboard.
- Experimentation with deep learning models like GRU, Bi-LSTM.
- Expansion to multi-asset volatility forecasting.