US Hurricanes and Economic Damage: Extreme Value Perspective

The historical record of U.S. hurricane damage is analyzed using a peaks-over-threshold approach in which the generalized Pareto distribution (GPD) is applied to model excesses above a specified threshold for a given damage metric. In addition to absolute hurricane damages (total damage), this paper defines a damage index as the ratio of base-year economic damages to the available economic value in the affected region. The paper then incorporates physical covariates at the individual hurricane level into the GPD model, namely, maximum wind speed and a simple yet novel measure of the mean bathymetric slope at landfall, and applies the analysis to both the total damage and the damage index. The parameters of the GPD models with physical covariates are estimated with maximum-likelihood estimation. The results show that for total damage, the only useful covariate is maximum wind speed. However, for the damage index, both the mean bathymetric slope and the maximum wind speed are found to be useful, with coefficients that are consistent with the known physics of each covariate in causing damage. Moreover, inclusion of covariates in the damage index reduces the maximum-likelihood estimate of the shape parameter to zero, transforming the fat tail for the distribution of total damage to a skinny (exponential) tail for the distribution of the damage index. These results suggest that damage measured as a fraction of estimated potential damage may help to remove the local economic signal from the damage database, leaving a data set that better captures the physical relationship between hurricanes and damage. Finally, as an illustrative example of the potential utility of this new methodology within a risk-assessment framework, it is applied to data sets of simulated hurricane tracks corresponding to current and future Intergovernmental Panel on Climate Change (IPCC) A1B-scenario climate states as modeled by two different climate models.