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Reactive Publishing

Risk measurement sits at the core of modern finance, yet traditional metrics often lag behind the complexity of today's markets. Machine Learning for Risk Measurement with Python bridges that gap.

This book takes you from classical risk measures, Value-at-Risk, Expected Shortfall, volatility modeling, and stress testing, into the next generation of predictive risk signals powered by machine learning. You will learn how to move beyond static, backward-looking calculations and build models that anticipate risk rather than merely report it.

Using clear Python implementations, the book covers:

• Core classical risk metrics and their practical limitations
• Feature engineering for risk-sensitive data
• Supervised and unsupervised approaches for detecting emerging risk patterns
• Model validation, backtesting, and stability under changing market regimes
• Interpretable machine learning techniques that keep risk models transparent and defensible

Written for quantitative analysts, risk managers, data scientists in finance, and Python-fluent practitioners, the material emphasizes practical application over abstract theory. Every major concept is grounded in code you can adapt, extend, and deploy.

Whether you are modernizing an existing risk framework or building predictive capabilities from the ground up, this book provides a structured path from established metrics to forward-looking risk signals.

Parametry knihy

• Plný název: Machine Learning for Risk Measurement with Python
• Podnázev: From Classical Metrics to Predictive Risk Signals
• Autor: James Preston, Alice Schwartz
• Jazyk: Angličtina
• Vazba: Brožovaná
• Počet stran: 400
• EAN: 9798193088967
• ID: 53540060
• Nakladatelství: Independently published
• Hmotnost: 483 g
• Rozměry: 229 × 152 × 25 mm
• Datum vydání: 17. August 2026

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