Abstract
The insurance and reinsurance industry, some governments, and private entities employ catastrophe (CAT) bonds to obtain coverage for large losses induced by earthquakes. These financial instruments are designed to transfer catastrophic risks to the capital markets. When an event occurs, a Post-Event Loss Calculation (PELC) process is initiated to determine the losses to the bond and the subsequent recoveries for the bond sponsor. Given certain event parameters such as magnitude of the earthquake and the location of its epicenter, the CAT bond may pay a fixed amount or not pay at all. This paper reviews two statistical techniques for classification of events in order to identify which should trigger bond payments based on a large sample of simulated earthquakes. These statistical techniques are effective, simple to interpret and to implement. A numerical experiment is performed to illustrate their use, and to facilitate a comparison with a previously published evolutionary computation algorithm.
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