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.

Examples

from openbb import obb
# Perform co-integration test between two timeseries.
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
maxlag	PositiveInt	Number of lags to use in the test.		False

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Returns

OBBject
    Data