A distribution in which extreme outcomes are more likely than the normal distribution would imply. Financial returns commonly show it, so normal-based models understate tail risk.
Where you will meet it
Fat tails comes up in 24 learning outcome notes across the FRM syllabus.
A few of them:
Fund Management FRM Part I · Financial markets and products
“Hedge fund returns are more varied and often show negative skew, fat tails and serial correlation.”
Common Univariate Random Variables FRM Part I · Quantitative analysis
“Mixing normals with the same mean but different volatilities gives a symmetric distribution with excess kurtosis, which is how regime-switching between calm and stressed markets produces fat tails.”
Measures of Financial Risk FRM Part I · Valuation and risk models
“They show excess kurtosis (kurtosis well above 3, meaning fat tails and a taller, narrower centre), often negative skewness (a longer, heavier left tail, with more weight on large losses), and volatility clustering, where turbulent days follow turbulent days.”
Estimating Market Risk Measures: An Introduction and Overview FRM Part II · Market risk measurement and management
“Exam point: fat tails mean normal VaR understates high-confidence risk.”
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