Fat Tails Micro Correlations Cooke and Kousky nsf

Fat Tails Micro Correlations Cooke and Kousky nsf# 0960865 http: //www. rff. org/Events/Pages/Introductio n-Climate-Change-Extreme-Events. aspx Tail Dependence

“Nice” distributions don’t surprise Someday, you’ll meet a taller person Tallest so far Next record

But Not:

Catastrophes are Different! Stock market Hurricanes Insurance

Nice distribution Women’s height [cm] Bigger is less bigger Mean excess Average height above heloise Mean excess curve decreasing

Fat Tails: Natural disasters Worse is more worse Average above Heat 1 Mean excess curve increasing Mean Excess

Fat Tail heuristics Historical averages ‘average out’ US crop insurance claims mean excess Variance is Infinite Variance is finite US crop insurance claims running average

Fat Tail Heuristics Historical averages just keep growing US Flood Claims per $ Income by County and Year

Katrina cost 100$B What’s the chance that the Next Katrina will cost >200$B? Probability that next extreme > 2 x current extreme 100 samples 2500 samples Thin tail (exponential) 0. 02 0. 0008 Super Fat 0. 5

Ask someone from St. Tammany County, LA: ‘After Katrina, flood loss claims in your county totaled $240 per dollar income (2000 dollars); in the next flood at least as bad as Katrina, what do you expect your (2000) dollar loss per dollar income to be? ”

Answer: $4, 000 US Flood Claims per $ Income by County and Year

Sobering Data • Tail Risk Show Background on Fat Tail Distributions http: //www. rff. org/News/Features/Pages/Und erstanding-Fat-Tailed-Distributions-and-What. They-Mean-for-Policy. aspx