Catastrophe Risk Modeling and Extreme Value Theory With Python (Quantitative Risk and Actuarial Modeling Collection) - Tapa blanda

Sinopsis

Level up your actuarial and analytics toolkit with the most complete, implementation-focused guide to catastrophe portfolios and tail risk. This intensive, 33-chapter blueprint takes you from rigorous theory to exam-style multiple-choice reinforcement and straight into production-ready Python code—chapter by chapter.

Who it’s for

• Actuaries, catastrophe modelers, and reinsurance analysts
• ERM leaders and capital modelers building internal models
• Data scientists and quantitative researchers entering insurance risk

What you’ll master

• Extreme Value Theory end to end: domains of attraction, GEV/POT, tail index estimators, declustering, and nonstationary extremes
• Spatial/spatiotemporal extremes, conditional extremes, and tail dependence for multi-peril portfolios
• Full catastrophe model pipeline: hazard → exposure → vulnerability → financial terms → portfolio roll-up
• Year-event tables, OEP/AEP/CDEP, PML and Tail-VaR, uncertainty bands, and secondary uncertainty
• Rare-event simulation (importance sampling, subset simulation) for extreme quantiles and exceedance probabilities
• Reinsurance structuring and optimization; ILS, triggers, and basis risk analytics
• Climate conditioning, trend-aware EVT, model validation, and governance

Build real portfolios, not toy examples

• Calibrate thresholds, tail indices, and return levels on sparse, messy data
• Construct EP curves with uncertainty overlays; attribute risk by region/peril/layer
• Simulate occurrence and aggregate treaties with reinstatements and hours clauses
• Quantify and manage basis risk for indemnity, parametric, and modeled-loss triggers
• Stress-test nonstationarity and compound events (e.g., wind–surge–rain)

Why this book

• Dense, practitioner-grade coverage with a direct line to real decisions
• Designed for on-the-job impact: each topic closes with runnable Python workflows
• Bridges actuarial rigor and catastrophe engineering, so you can price, allocate capital, and communicate tail risk with confidence

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