A time series whose mean, variance and autocorrelations do not change over time. Many forecasting methods require it.
Where you will meet it
Stationarity comes up in 11 learning outcome notes across the FRM syllabus.
A few of them:
Stationary Time Series FRM Part I · Quantitative analysis
“A series is covariance stationary when its first two moments do not depend on time.”
Stationary Time Series FRM Part I · Quantitative analysis
“The autocovariance function measures how a stationary series moves with its own past.”
Stationary Time Series FRM Part I · Quantitative analysis
“It is covariance stationary when the roots of the characteristic equation lie outside the unit circle, which for AR(1) means the absolute value of φ is below one.”
Stationary Time Series FRM Part I · Quantitative analysis
“Because it is a finite sum of white noise it is always covariance stationary, whatever the θ values.”
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