Skip to content

FRM Part I · FRM Exam Part I · Stationary Time Series

Which set of properties defines a zero-mean white noise process ε_t?

A zero-mean white noise process has a constant zero mean, a constant finite variance, and zero autocovariance at all non-zero lags. It need not be normal or independent; those are stronger special cases.

  1. AConstant zero mean, constant finite variance, and zero autocovariance at all non-zero lagsCorrect
  2. BZero mean, variance that grows with t, and zero autocovariance at all non-zero lags
  3. CConstant zero mean, constant variance, and autocorrelations that decay geometrically
  4. DZero mean and constant variance, with the series required to be normally distributed and independent

Explanation

A white noise process has mean zero, a constant finite variance and no serial correlation (zero autocovariance at every non-zero lag). A growing variance would violate stationarity, geometric decay describes an AR(1), and normality or independence is not required (that is the stricter Gaussian or independent white noise).

Did you get it right without looking?

One question tells you little. A timed set on Stationary Time Series shows your real accuracy, how long you take and where you lose marks.

Practise Stationary Time SeriesFree diagnostic mock

More Stationary Time Series questions