Document Type

Thesis

Degree

Master of Science (MS)

Department

Statistics

Advisor's Name

Sneh Gulati

Advisor's Title

Committee Chair

Advisor's Name

B.M. Golam Kibria

Advisor's Name

Shahid Hamid

Keywords

Catastrophe modeling, Risk measure, GPD, Extreme Value Theory, Tail distribution

Date of Defense

1-26-2012

Abstract

This thesis reviewed various heavy tailed distributions and Extreme Value Theory (EVT) to estimate the catastrophic losses simulated from Florida Public Hurricane Loss Projection Model (FPHLPM). We have compared risk measures such as Probable Maximum Loss (PML) and Tail Value at Risk (TVaR) of the selected distributions with empirical estimation to capture the characteristics of the loss data as well as its tail distribution. Generalized Pareto Distribution (GPD) is the main focus for modeling the tail losses in this application. We found that the hurricane loss data generated from FPHLPM were consistent with historical losses and were not as heavy as expected. The tail of the stochastic annual maximum losses can be explained by an exponential distribution.

This thesis also touched on the philosophical implication of small probability, high impact events such as Black Swan and discussed the limitations of quantifying catastrophic losses for future inference using statistical methods.


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