Technical Papers
May 11, 2017

Probable Maximum Loss for the Florida Public Hurricane Loss Model: Comparison

Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 3, Issue 4

Abstract

Hurricanes are a way of life in South Florida, where owning a home without windstorm insurance is almost an impossibility. Insurance premiums for windstorm loses are computed through the use of complex mathematical models called catastrophe (cat) models. When they were first developed, cat models focused on the calculation of average annual loss due to wind. However, with exposure increasing rapidly along the coast of South Florida, it is imperative for insurance companies to protect themselves from the “once-in-100-year event”; in other words, probable maximum loss (PML). Gulati et al. (2014) investigated the computation and distribution of probable maximum loss in the case of personal residential structures for version 5.0 of the Florida Public Hurricane Loss Model using parametric and nonparametric methods. Here, the authors investigate the computation of probable maximum insured losses for personal and commercial residential buildings in version 6.1 of the model using the same methods. The authors also compare how PML values for total insured loss have changed between the two versions.

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Acknowledgments

This research is supported by the State of Florida through a Department of Financial Services (FDFS) grant to the Florida International University International Hurricane Research Center. The opinions, findings, and conclusions expressed in this paper are not necessarily those of the FDFS.

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Go to ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 3Issue 4December 2017

History

Received: Jun 20, 2016
Accepted: Feb 7, 2017
Published online: May 11, 2017
Discussion open until: Oct 11, 2017
Published in print: Dec 1, 2017

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Authors

Affiliations

Sneh Gulati, Ph.D. [email protected]
Professor, Dept. of Mathematics and Statistics, Florida International Univ., Miami, FL 33199 (corresponding author). E-mail: [email protected]
Florence George, Ph.D.
Associate Professor, Dept. of Mathematics and Statistics, Florida International Univ., Miami, FL 33199.
B. M. Golam Kibria, Ph.D.
Professor, Dept. of Mathematics and Statistics, Florida International Univ., Miami, FL 33199.
Shahid Hamid, Ph.D.
Professor, Dept. of Finance, Florida International Univ., Miami, FL 33199.
Steve Cocke, Ph.D.
Senior Research Scientist, Center for Ocean-Atmosphere Prediction Studies, Florida State Univ., Tallahassee, FL 32306.
Jean-Paul Pinelli, Ph.D. https://orcid.org/0000-0002-6663-9486
Professor, Dept. of Civil Engineering, Florida Institute for Technology, Melbourne, FL 32901. ORCID: https://orcid.org/0000-0002-6663-9486

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