Univariate and bivariate GPD methods for predicting extreme wind storm losses

DOI10.1016/J.INSMATHECO.2008.11.002zbMath1162.91399OpenAlexW2066171676MaRDI QIDQ1023094

Erik Brodin, Holger Rootzén

Publication date: 10 June 2009

Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2008.11.002

zbMATH Keywords

trend analysisgeneralized Pareto distributionextreme value statisticspeaks over thresholdlikelihood prediction intervalswind storm losses

Mathematics Subject Classification ID

Environmental economics (natural resource models, harvesting, pollution, etc.) (91B76)

Related Items (3)

Earthquake parametric insurance with Bayesian spatial quantile regression ⋮ Multivariate peaks over thresholds models ⋮ Multivariate density estimation using dimension reducing information and tail flattening trans\-formations

Uses Software

• ismev

Cites Work

• Unnamed Item
• Multivariate generalized Pareto distributions
• Effect of extrapolation on coverage accuracy of prediction intervals computed from Pareto-type data
• Can Losses Caused by Wind Storms be Predicted from Meteorological Observations?
• Comparison of Approaches for Estimating the Probability of Coastal Flooding
• Extreme value statistics and wind storm losses: A case study
• Statistics of Extremes
• On prediction intervals based on predictive likelihood or bootstrap methods
• Extreme dependence of multivariate catastrophic losses
• Modeling Catastrophes and their Impact on Insurance Portfolios
• An introduction to statistical modeling of extreme values

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