A Probabilistic Framework for Site-Specific Tropical Cyclone Loss Quantification and Reinsurance Optimization

Kunal J. Rathore 

Quantifying the financial risk posed by landfalling tropical cyclones requires statistical methods that extend beyond the limits of a short and non-stationary historical observational record. This paper explores techniques for improving site-specific loss estimation in tropical
cyclone catastrophe risk, an area of ongoing interest to insurance and reinsurance pricing
and solvency capital management. We present an end-to-end probabilistic catastrophe risk
model that captures extreme tail behavior by fitting a continuous Weibull intensity distribu
tion to multi-era NOAA HURDAT2 meteorological data, simulating a 10,000-year stochastic
event set. This hazard catalog is evaluated against an Industry Exposure Database derived
from NOAA Digital Coast economic indices, representing a total insured asset value of
$39.01 Billion across primary high-hazard coastal corridors. Physical damage is simulated
via structural vulnerability functions parameterized as log-normal cumulative distribution
functions, and liabilities are reshaped through vectorized financial policy layers, tracking in
dividual properties rather than county-averaged zones. The resulting property-level resolu
tion reveals loss concentration patterns within a single metro area that a regionally-averaged
model cannot recover.