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A risk manager at a leading global bank is conducting a time series analysis on equity returns. The goal of the analysis is to determine whether the equity returns time series exhibits covariance stationarity. Covariance stationarity is a fundamental property in time series analysis that ensures consistency in the mean, variance, and autocovariance over time, making statistical inferences and model predictions more reliable. Which of the following statements describes a necessary criterion for a time series to be classified as covariance stationary?
A
The distribution of a time series should have a kurtosis value near 3.0, ensuring no fat tails will distort stationarity.
B
The distribution of a time series should have a skewness value near O, so that its mean will fall in the center of the distribution.
C
The autocovariance of a covariance stationary time series depends only on the lag, h, between observations, not on time.
D
When the autocovariance function is asymmetric with respect to lag, h, forward looking stationarity can be achieved.