Squeezing the Risk Out of Reinsurance

stop-loss opportunity in medical reinsuranceA recent and very telling study by two actuarial experts makes clear the important perspective and depth that can be added to financial risk analysis by running Monte Carlo simulations with different probability functions for the same variable.

Writing about a hypothetical case in the reinsurance industry, Lina Chan and Domingo Joaquin sought to predict how a stop-loss underwriting opportunity would affect a reinsurer’s bottom line. Chan, a managing partner in CP Risk Solutions, is a fellow of the Society of Actuaries, and Joaquin is an associate professor of finance at Illinois State University.
To create their predictions, they first established what level of loss in capital position would be unacceptable, and then, using Monte Carlo simulations in Excel, they analyzed three variations of the hypothetical underwriting arrangement.  For each version of the deal, they ran simulations using log-normal, inverse Gaussian, and log-logistic probability functions. 
I was surprised at sunshine-to-gloom differences in researchers’ simulation results.  The gloomiest was obtained with by the model using the log-logistic function, this prompted Chan and Joaquin to endorse the reinsurance deal involving the most sharing of risk––and, of course, of profit.  But what was most striking about their study were the possible courses of action that could have resulted from the analysts’ reliance on only one probability function.  By creating a multi-perspective set of risk analyses, they demonstrated how to effectively squeeze the riskiness of the hypothetical deal down to almost nothing.

» Full text of case study.  

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