A risk manager at a major global bank is conducting a time series analysis on the returns of equities. The manager aims to ascertain whether the time series exhibits covariance stationarity, a crucial property for reliable financial modeling and forecasting. Which of the following statements correctly identifies a necessary condition for a time series to be considered covariance stationary?

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The distribution of a time series should have a kurtosis value near 3.0, ensuring no fat tails will distort stationarity.

6.2%

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.

10.8%

The autocovariance of a covariance stationary time series depends only on the lag: h,between observations,not on time.

78.5%

When the autocovariance function is asymmetric with respect to lag, h, forward looking stationarity can be achieved

4.6%

Financial Risk Manager Part 1