ISBN: 9798175633383 - applied quantitative risk analytics: var, expected shortfall, and stress testing with python (5 risultati)

Taschenbuch. Condizione: Neu. Neuware - Reactive PublishingAn 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.…

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. Reactive PublishingAn 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condizione: new. Paperback. Reactive PublishingAn 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…