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Machine Learning for Volatility Forecasting: LSTMs, Transformers, and Regime Models: Deep Learning Models for Realized Volatility, Implied Vol Surface
Paperback
Publisher: Independently Published
Published: Sep 19 2025
Pages: 654
Weight: 1.90
Height: 1.31 Width: 6.00 Depth: 9.00
Language: English
Volatility drives everything, options pricing, risk management, and portfolio returns. Machine Learning for Volatility Forecasting is the definitive guide to applying deep learning architectures to predict realized volatility, implied vol surfaces, and regime shifts with precision.
This hands-on book teaches you to build LSTM networks, attention-based Transformers, and hybrid ML models that outperform classical GARCH approaches. Using Python and open-source libraries, you'll create scalable pipelines for volatility prediction, backtest their performance, and deploy them for real-time risk management.
Inside, you'll learn how to:
Prepare High-Frequency Data - Clean and transform price and option data for ML pipelines.
Model Volatility with LSTMs - Capture volatility clustering and memory effects using sequence models.
Build Transformer Architectures - Use attention to detect complex dependencies and jumps in volatility.
Detect Market Regimes - Apply unsupervised learning (HMM, clustering) to segment risk environments.
Compare Against Baselines - Benchmark ML models versus GARCH, HAR-RV, and EWMA models.
Deploy & Monitor Models - Productionize volatility forecasts with Python, JAX, and live dashboards.
With practical code, full datasets, and calibration workflows, this book is perfect for quants, risk managers, and developers who want to move beyond theory and build production-grade volatility forecasting systems.
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