Catastrophe Risk Modeling and Extreme Value Theory With Python - Brossura
Libro 5 di 7: Quantitative Risk and Actuarial Modeling Collection
Sinossi
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
Upgrade your models, tighten your capital, and outpace uncertainty. Start building industrial-grade catastrophe analytics today.
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Catastrophe Risk Modeling and Extreme Value Theory With Python
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Catastrophe Risk Modeling and Extreme Value Theory With Python (Paperback)
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Paperback. Condizione: new. Paperback. 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 forActuaries, catastrophe modelers, and reinsurance analystsERM leaders and capital modelers building internal modelsData scientists and quantitative researchers entering insurance riskWhat you'll masterExtreme Value Theory end to end: domains of attraction, GEV/POT, tail index estimators, declustering, and nonstationary extremesSpatial/spatiotemporal extremes, conditional extremes, and tail dependence for multi-peril portfoliosFull catastrophe model pipeline: hazard exposure vulnerability financial terms portfolio roll-upYear-event tables, OEP/AEP/CDEP, PML and Tail-VaR, uncertainty bands, and secondary uncertaintyRare-event simulation (importance sampling, subset simulation) for extreme quantiles and exceedance probabilitiesReinsurance structuring and optimization; ILS, triggers, and basis risk analyticsClimate conditioning, trend-aware EVT, model validation, and governanceBuild real portfolios, not toy examplesCalibrate thresholds, tail indices, and return levels on sparse, messy dataConstruct EP curves with uncertainty overlays; attribute risk by region/peril/layerSimulate occurrence and aggregate treaties with reinstatements and hours clausesQuantify and manage basis risk for indemnity, parametric, and modeled-loss triggersStress-test nonstationarity and compound events (e.g., wind-surge-rain)Why this bookDense, practitioner-grade coverage with a direct line to real decisionsDesigned for on-the-job impact: each topic closes with runnable Python workflowsBridges actuarial rigor and catastrophe engineering, so you can price, allocate capital, and communicate tail risk with confidenceUpgrade your models, tighten your capital, and outpace uncertainty. Start building industrial-grade catastrophe analytics today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9798264092480
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Catastrophe Risk Modeling and Extreme Value Theory With Python
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Catastrophe Risk Modeling and Extreme Value Theory With Python (Quantitative Risk and Actuarial Modeling Collection)
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Catastrophe Risk Modeling and Extreme Value Theory With Python
Da: GreatBookPricesUK, Woodford Green, Regno Unito
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Condizione: As New. Unread book in perfect condition. Codice articolo 51306989
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Catastrophe Risk Modeling and Extreme Value Theory With Python (Paperback)
Print on DemandDa: CitiRetail, Stevenage, Regno Unito
Valutazione del venditore 5 su 5 stelle
Paperback. Condizione: new. Paperback. 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 forActuaries, catastrophe modelers, and reinsurance analystsERM leaders and capital modelers building internal modelsData scientists and quantitative researchers entering insurance riskWhat you'll masterExtreme Value Theory end to end: domains of attraction, GEV/POT, tail index estimators, declustering, and nonstationary extremesSpatial/spatiotemporal extremes, conditional extremes, and tail dependence for multi-peril portfoliosFull catastrophe model pipeline: hazard exposure vulnerability financial terms portfolio roll-upYear-event tables, OEP/AEP/CDEP, PML and Tail-VaR, uncertainty bands, and secondary uncertaintyRare-event simulation (importance sampling, subset simulation) for extreme quantiles and exceedance probabilitiesReinsurance structuring and optimization; ILS, triggers, and basis risk analyticsClimate conditioning, trend-aware EVT, model validation, and governanceBuild real portfolios, not toy examplesCalibrate thresholds, tail indices, and return levels on sparse, messy dataConstruct EP curves with uncertainty overlays; attribute risk by region/peril/layerSimulate occurrence and aggregate treaties with reinstatements and hours clausesQuantify and manage basis risk for indemnity, parametric, and modeled-loss triggersStress-test nonstationarity and compound events (e.g., wind-surge-rain)Why this bookDense, practitioner-grade coverage with a direct line to real decisionsDesigned for on-the-job impact: each topic closes with runnable Python workflowsBridges actuarial rigor and catastrophe engineering, so you can price, allocate capital, and communicate tail risk with confidenceUpgrade your models, tighten your capital, and outpace uncertainty. Start building industrial-grade catastrophe analytics today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9798264092480
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Catastrophe Risk Modeling and Extreme Value Theory With Python (Quantitative Risk and Actuarial Modeling Collection)
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