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A Simulation Approach for Estimating Hurricane Risk over a 5-yr Horizon

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  • 1 Risk Management Solutions, London, United Kingdom
  • | 2 Risk Management Solutions, and University College of London, London, United Kingdom
  • | 3 Risk Management Solutions, London, United Kingdom
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Abstract

We develop a stochastic North Atlantic hurricane track model whose climate inputs are Atlantic main development region (MDR) and Indo-Pacific (IP) sea surface temperatures and produce extremely long model simulations for 58 different climates, each one conditioned on 5 yr of observed SSTs from 1950 to 2011—hereafter referred as medium-term (MT) views.

Stringent tests are then performed to prove that MT simulations are better predictors of hurricane landfalls than a long-term view conditioned on the entire SST time series from 1950 to 2011.

In this analysis, the authors extrapolate beyond the historical record, but not in terms of a forecast of future conditions. Rather it is attempted to define—within the limitation of the modeling approach—the magnitude of extreme events that could have materialized in the past at fixed probability thresholds and what is the likelihood of observed landfalls given such estimates.

Finally, a loss proxy is built and the value of the analysis results from a simplified property and casualty insurance perspective is shown. Medium-term simulations of hurricane activity are used to set the strategy of reinsurance coverage purchased by a hypothetical primary insurance, leading to improved solvency margins.

Denotes Open Access content.

Corresponding author address: A. Bonazzi, 30 Monument Street, London, EC3R 8NB, United Kingdom. E-mail: abonazzi@rms.com

Abstract

We develop a stochastic North Atlantic hurricane track model whose climate inputs are Atlantic main development region (MDR) and Indo-Pacific (IP) sea surface temperatures and produce extremely long model simulations for 58 different climates, each one conditioned on 5 yr of observed SSTs from 1950 to 2011—hereafter referred as medium-term (MT) views.

Stringent tests are then performed to prove that MT simulations are better predictors of hurricane landfalls than a long-term view conditioned on the entire SST time series from 1950 to 2011.

In this analysis, the authors extrapolate beyond the historical record, but not in terms of a forecast of future conditions. Rather it is attempted to define—within the limitation of the modeling approach—the magnitude of extreme events that could have materialized in the past at fixed probability thresholds and what is the likelihood of observed landfalls given such estimates.

Finally, a loss proxy is built and the value of the analysis results from a simplified property and casualty insurance perspective is shown. Medium-term simulations of hurricane activity are used to set the strategy of reinsurance coverage purchased by a hypothetical primary insurance, leading to improved solvency margins.

Denotes Open Access content.

Corresponding author address: A. Bonazzi, 30 Monument Street, London, EC3R 8NB, United Kingdom. E-mail: abonazzi@rms.com
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