As the founder of AIR, one of the top two modeling companies, and its CEO for 20 years, I spent much of my career informing insurers and reinsurers about catastrophe models and extolling the virtues of model output—i.e., exceedence probability (EP) curves. Relative to the information available before catastrophe models, EP curves were a major advancement.
Executive SummaryThe catastrophe model-generated EP curves—and more specifically the "probable maximum losses" (PMLs) derived from those curves—provide only a partial picture of a company's large loss potential. Adding newer information on return period events, where the probabilities are based on the hazard versus the loss, gives more insight into tail risk and provides advanced metrics for monitoring "informal" risk tolerances. This article illustrates how one chart can summarize multiple risk metrics for a succinct and complete picture of your company's catastrophe loss potential.
After starting Karen Clark & Co. (KCC) and entering the real world of insurance company executives and boards who rely on the model output, I learned a few things. For example, there is still some confusion about what EP curves, and particularly the PMLs derived from these curves, represent.
I’ve also learned that model EP curves do not give senior executives and boards of directors all of the information they’d like to have for risk management purposes. While the EP curve output is valuable for monitoring your formal risk tolerance statements, most companies also have “informal” risk tolerances they’d like to manage.
Partly because it’s easily derived from model EP curve output, the 100-year PML has evolved as the most important risk metric used by senior management, rating agencies and regulators. Along with the 250-year PML, it provides the basis for most formal risk tolerance statements. Despite the importance of this metric, you frequently hear the 100-year PML expressed incorrectly as the 100-year “event.”
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