The component perils coefficients display the same drop in value as in the Model (1’). US Wind drops from 3.4481 to 2.3966. US Quake drops from 4.4659 to 2.1872. The Quake coverage was in much demand in Q2 2006, and there is anecdotal evidence for this. After Hurricane Katrina in August 2005, it became apparent that while physical property damage generate the main demand for catastrophe coverage, catastrophes can also affect workers compensation claims. Accordingly, reinsurers began to increase their coverage in US quake zones to allow for additional non physical claims from an earthquake.

# **Cross Section Analysis including ILWs and Re-Modeled Risk Statistics**

Beyond the ILS market there is a related market for insurance risk that trespasses outside the traditional realm; it is the Industry Loss Warranty Market, or ILW market. Since much of the ILW trading takes place in the capital market. However, whereas the ILS quantities are a well known, the ILW sizes are not. Neither is there any declared expected loss associated with a particular ILW transaction. They are very much private and confidential transactions. There are, however, indicated prices and modeling agencies that will provide third party expected loss estimates.

We use in this analysis remodeled ILW and ILS estimates provided by Air Worldwide, which is one of the three largest catastrophe risk modeling firms with RMS and EQECAT. As one of three independent modelers they provide roughly one third of the original expected loss estimates and risk analyses for all issued ILS. An important question is whether all modeling firms model on the same basis. Is a 2% expected loss from AIR directly comparable to a 2% expected loss from RMS? Generally the answer is positive but there are differences that market participants are well aware of. One company tends to be conservative with a particular peril than another while another is more liberal elsewhere. Over time investors have been sensitized to such nuances and have come to demand for themselves analyses of risk on the same, or at least on a consistent, basis. The model companies have responded by re-modeling nearly all deals that are outstanding whether originally analyzed by themselves or their competitors.

One consequence of having a remodeled set of estimates is that they may differ from those originally provided with an ILS. We thus distinguish the original expected loss, oEL, and the remodeled expected loss, rEL. By way of double checking the series, Model (1b) is re-run with both series for the Q1 2008 ILS set with secondary yields. The results are shown in Table 9. The parameter estimates look very similar. Apparently, the market looks mostly at the original data, and not yet at remodeled statistics.

**Table 9.  Comparing the Use of Original vs Re-Modeled Expected Loss**

|_Coefficients_<br>**Observed data: First**<br> <br>|_Standard Error_<br>**Quarter 2008 (83 o**<br>|_t Stat_<br>**bservations)**<br>|_P-value_<br>|
|---|---|---|---|
|Intercept<br>0.041456|0.00435|9.529676|7.14E-15|
|Expected Loss<br>0.883247|0.12939|6.826238|1.46E-09|
|Multiple R = 0.6043||||
|**Remodeled data: First**|**Quarter 2008 (83 o**|**bservations)**||
|Intercept<br>0.040351|0.005137|7.854396|1.45E-11|
|Expected loss<br>0.872826|0.159259|5.48055|4.67E-07|
|Multiple R = 0.5201||||

Another virtue of having data modeled on the same basis is that we can make estimates of other statistics beyond expected loss. Theoretically this is possible with original risk analysis as well but prospectus 16