Return Series Stationarity

Stationarity refers to a time series whose statistical properties, such as mean and variance, remain constant over time. Financial price series are rarely stationary, which is why analysts transform them into return series to perform meaningful statistical tests.

A return series is generally considered stationary if it does not exhibit a long-term trend or changing volatility structure. Testing for stationarity, often using the Augmented Dickey-Fuller test, is a prerequisite for applying many quantitative models, including those for trend persistence.

If a series is non-stationary, the results of statistical models may be spurious and unreliable for forecasting. Ensuring stationarity allows traders to use standard econometric tools to identify real market signals amidst the noise.

It is a critical step in the data preprocessing pipeline for any algorithmic trading system in the digital asset space.