Lesson 8.7: Choosing Your Weapon: GARCH vs. XGBoost vs. LSTM
You have an arsenal of forecasting tools. This lesson is a practical guide for the working quant on how to choose the right model for the job. We compare the classical statistical workhorse (GARCH), the machine learning champion (XGBoost), and the deep learning specialist (LSTM) across key dimensions like data needs, performance, and interpretability.
Part 1: The Three Contenders
The Specialist. A parametric statistical model designed for one job only: modeling and forecasting volatility.
The Generalist. A powerful, tree-based machine learning model that can learn any relationship, but requires careful feature engineering for time series.
The Sequence Master. A deep learning model with built-in memory, designed specifically to learn from sequential data without manual feature engineering.
Part 2: The Decision Matrix
| Criterion | GARCH | XGBoost | LSTM |
|---|---|---|---|
| Primary Use | Volatility Forecasting | Forecasting Anything (Mean, Vol, etc.) | Forecasting Anything |
| Interpretability | Excellent | Good (Feature Importance) | Poor (Black Box) |
| Data Needs | Low (only needs the return series) | Medium (needs engineered features) | High (needs lots of data to train) |
| Feature Engineering | None required | Extensive (lags, rolling stats) | Minimal (learns features automatically) |
| Performance | Strong benchmark for volatility | Often state-of-the-art for tabular data | Can be state-of-the-art for long, complex sequences |
| Training Time | Fast | Medium | Very Slow |
| When to Use | For a quick, robust, and interpretable volatility forecast. The first benchmark you should always build. | When you have many external features and want a high-performance model without the complexity of deep learning. | When you have very long sequences and believe complex, long-range dependencies are important. Good for NLP and multi-asset systems. |
What's Next? A Final Capstone
You now have a complete decision-making framework for tackling time series problems.
It's time to put it all together. The final lesson of this module will be a capstone project where we apply our most sophisticated sequence model, the LSTM, to the real-world problem of forecasting stock market volatility.