Financial Risk Manager Part 1
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All the following characteristics indicate a time series that's covariance stationary EXCEPT:
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NNikitesh
A
Stability of the autocorrelation
B
Stability of the mean
C
Stability of the covariance structure
D
A nonconstant variance
Explanation:
A covariance stationary time series must have:
- Constant mean (stationary mean)
- Constant variance (homoscedasticity)
- Constant autocovariance structure (autocovariance depends only on lag, not time)
Option D (a nonconstant variance) is the exception because it violates the requirement for constant variance. A nonconstant variance indicates heteroscedasticity, which means the time series is not covariance stationary.
The other options are all characteristics of covariance stationarity:
- A: Stability of autocorrelation (autocorrelation depends only on lag)
- B: Stability of the mean (constant mean over time)
- C: Stability of the covariance structure (autocovariance depends only on lag)
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