Introduction to Fat Tails

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Fat Tails
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
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 Heat1
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
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-WhatThey-Mean-for-Policy.aspx
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