It’s a Weibull

To understand the premiums charged by the Texas Windstorm Insurance Association and the current legal and financial issues being debated in Austin, you have to get your hands a little dirty with the actuarial science.  You need to have some ability to model the damages likely to be caused by a tropical cyclone on the Texas coast.  Now, to do this really well, it might be thought you need an awful lot of very fine data.  In fact, however, you can do a pretty good job of understanding TWIA’s perspective by just reverse engineering publicly available information.

What I want to show is that the perceived annual exposure to the Texas Windstorm Association can be really well modeled by something known in statistics as a Weibull Distribution. To be fancy, it’s a zero-censored three parameter Weibull Distribution: 

CensoredDistribution[{0, ∞},
 WeibullDistribution[0.418001, 1.26765*10^8, -4.81157*10^8]]

We can plot the results of this distribution against the predictions made by TWIA’ s two consultants: AIR and RMS. The x-axis of the graph are the annual losses to TWIA.  The y-axis of the graph is the probability that the losses will be less than or equal to the corresponding amount on the x-axis. As one can see, it is almost a perfect fit.  For statisticians, the “adjusted R Squared” value is 0.995. 

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How did I find this function? Part of it is some intuition and some expertise about loss functions.  But a lot of it comes from running a “non-linear regression” on data in the public domain.  Here’s a chart (an “exceedance table”) provided by reinsurance broker Guy Carpenter to TWIA.  It shows the estimates of two consultants, AIR and RMS, about the losses likely to be suffered by TWIA.  Basically, you can use statistics software (I used Mathematica) to run a non-linear regression on this data and assume the underlying model is a censored Weibull distribution of some sort.  And, in less than a second, out pop the parameters to the Weibull distribution that best fit the data. As shown above it fits the AIR and RMS data points really well.  Moreover, it calculates the “AAL” (the mean annual loss to TWIA) pretty well too.

 

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In some forthcoming posts, I’ m going to show what the importance of this finding is, but suffice it to say, it explains a lot about the current controversy and suggests some matters to be examined with care.