cointegration

Show co-integration between two timeseries using the two step Engle-Granger test.

The two-step Engle-Granger test is a method designed to detect co-integration between two time series. Co-integration is a statistical property indicating that two or more time series move together over the long term, even if they are individually non-stationary. This concept is crucial in economics and finance, where identifying pairs or groups of assets that share a common stochastic trend can inform long-term investment strategies and risk management practices. The Engle-Granger test first checks for a stable, long-term relationship by regressing one time series on the other and then tests the residuals for stationarity. If the residuals are found to be stationary, it suggests that despite any short-term deviations, the series are bound by an equilibrium relationship over time.

Example

from openbb import obb

stock_data = obb.equity.price.historical(symbol='TSLA', start_date='2023-01-01', provider='fmp').to_df()
obb.econometrics.cointegration(data=stock_data, columns=["open", "close"])

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Parameters

• standard

Name	Type	Description	Default	Optional
data	List[Data]	Input dataset.		False
columns	List[str]	Data columns to check cointegration		False

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Returns

OBBject
    results : Data
        OBBject with the results being the score from the test.