Applied Quantitative Risk Analytics: VaR, Expected Shortfall, and Stress Testing with Python

By: James Preston

Reactive Publishing

An enterprise-grade guide to building, validating, and deploying quantitative risk models in Python.

Applied Quantitative Risk Analytics provides a rigorous, hands-on framework for measuring and managing market risk using modern Python libraries. Designed for quantitative analysts, risk managers, and financial engineers, this practical manual bridges the gap between theoretical risk metrics and operational code execution.

Moving beyond basic statistical concepts, the book covers the full lifecycle of quantitative risk measurement. You will learn how to construct historical, parametric, and Monte Carlo Value at Risk (VaR) models, implement Expected Shortfall (ES) to capture tail risk, and design regulatory-compliant stress testing scenarios.

Key Topics Covered:

Market Risk Metrics: Mathematical foundations and code implementations for standard and parametric Value at Risk (VaR).Extreme Value & Tail Risk: Advanced estimation of Expected Shortfall (CVaR) to address non-normal return distributions and fat-tailed asset behavior.Simulation Techniques: Building Monte Carlo and historical simulation engines using NumPy, Pandas, and SciPy.Stress Testing & Scenario Analysis: Designing historical crisis scenarios, hypotheticals, and reverse stress tests for complex portfolios.Model Validation & Backtesting: Evaluating model coverage using Kupiec's POF test, Christoffersen's independence test, and failure rate tracking.

Whether you are upgrading legacy risk infrastructure or implementing modern quantitative models from scratch, this resource provides the production-ready code and structural methodology required for modern financial engineering.

• Format: Paperback
• Language: English
• ISBN: B0HKDVCQ7F
• ISBN13: 9798175633383
• Release Date: September 2026
• Publisher: Independently published
• Length: 390 Pages
• Weight: 1.04 lbs.
• Dimensions: 9.0" x 0.9" x 6.0"