1. Discussion: Risk and Valuation of Contingent Catastrophe Bonds by Daniel Bauer and Florian Kramer Discussion by Patrick Brocket Longevity 5: Fifth International Longevity Risk and Capital Market Solutions Conference, New York, NY September 26, 2009

2. Overview of Contribution Catastrophe Mortality (CATM) Bonds are recent capital market innovation Triggered by catastrophic death claims in a fixed interval Little academic analysis previously, but five major deals since 2006 for $1.6 B Modeling is difficult – Involves both evolution, and jumps in mortality

3. Overview of Contribution The paper proposes a new stochastic mortality rate model with two components, –1) A baseline component of a mean-reverting stochastic process, captures four basic characteristics of the historical mortality trend, including the positive stochastic process, mortality rate improvement, the improvement converging to a certain limit, the decrease of variability of distribution of death age. –2) A jump component of a compound Poisson process. describes the jumps in the mortality rate trend, like the 1918 flu pandemic

4. Overview of Contribution The paper uses their new stochastic mortality rate model to analyze in depth a particular securitization (Tartan CATM bond transaction arranged by Goldman Sachs for Scottish Re ) The paper provides filtering algorithm for simulated maximum likelihood estimation for model fit and calibration.

5. Discussion Modeling mortality rates-suggestions –Right now the model mainly captures the time-specific characteristics. The mortality trend for different ages are based on the same fundamental time-specific trend multiplies different age-specific indices. This is appropriate for the Tartan analysis. –Observe from Figure 2, the trend for different ages (20 v.s. 50) doesn’t follow exactly same fundamental trend. For an extension of the mortality modeling, the cohort effect might be considered.

6. Discussion In fitting the model parameters, perhaps use BIC or AIC or other criteria, in to verify the necessity of the parameters selection. Also suggest they might check the model fit based on the historical demographic data to provide further support for the model specification.

7. Discussion A major finding of the paper is that the calculated risk measures and spread in the Tartan securitization are much larger than the reported level in the loss profiles. Suggest further search for explanations why. One possible explanation is the method used to calculate the risk premium (based on the individual annuity quotes which also contain loadings for other sources of risk) so the implied risk premium represents an upper bound.

8. Discussion More on why the Tartan Securitizers might have gotten the risk wrong Basis risk the difference between the general population and the population assured as well as selection effects might account for risk measure differences Finally, “One should never reject incompetence as a null hypothesis” Could the original Tartan modelers have ignored the continuous trend component or used too recent data?