Almost every model in the first two modules quietly assumed the data points were independent and identically distributed. Financial data are neither. A price today is the price yesterday plus a small change, volatility comes in clusters, and the statistical properties of a series drift over time. Stationarity is the property that makes a time series learnable at all, and autocorrelation is the tool that measures the memory inside it. This lesson defines both, shows why a non-stationary series wrecks an otherwise sound model, works through the sample autocorrelation of a return series and the unit root of a random walk by hand, and covers the tests (ADF and KPSS) and transforms you use to make a series stationary. It is the foundation for the rest of the module: ARIMA, GARCH, and cointegration all build directly on these ideas.
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