The tendency for large price moves to follow large moves and small to follow small. It is why constant-volatility models fit poorly.
Where you will meet it
Volatility clustering comes up in 6 learning outcome notes across the FRM syllabus.
A few of them:
Multivariate Random Variables FRM Part I · Quantitative analysis
“Observations with volatility clustering or autocorrelation are not iid, because they are dependent.”
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.”
Parametric Approaches (II): Extreme Value FRM Part II · Market risk measurement and management
“With volatility clustering, apply EVT to standardised residuals.”
Stationary Time Series FRM Part I · Quantitative analysis
“A series whose squares are autocorrelated, such as returns with volatility clustering, can be white noise yet not independent.”
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