Chartered Financial Analyst (CFA) Practice Exam Level 2

Covariance stationary time series analysis is characterized by a constant and finite expected value. This means that throughout the time series, the average value remains the same over time, reflecting stability in the data's mean. In financial contexts, this is crucial for various modeling and forecasting techniques since stationary series are easier to analyze and predict.

In a covariance stationary series, not only does the expected value remain constant, but the variances around that mean do not change either over time, although the question here specifically highlights the expected value. Therefore, understanding this property helps in identifying reliable patterns within the data, essential for effective financial analysis and decision-making.

The other options present characteristics that do not align with covariance stationarity: variance must remain constant, covariance does not vary non-constantly, and that the mean should not trend over time, which distinguishes the stationary series from non-stationary series that exhibit trends.