Understand cointegration in trading and finance. Cointegration quantitatively evaluates time series variable connections. It commonly finds long-term connections between stocks, commodities, and currencies.
What’s Cointegration?
Cointegration measures the likelihood of two or more time series variables moving together over time, notwithstanding short-term instability. It discovers a long-term relationship between two or more variables despite their short-term movements.
An equilibrium relationship between variables is cointegration. This indicates that any deviations from this long-term equilibrium will be addressed, forcing variables to converge.
Trading: Cointegration Use?
Cointegration lets traders link assets that will move together. Trading methods that exploit long-term equilibrium deviations may be created using this data.
Consider cointegrated stocks A and B. If stock A increases dramatically while stock B remains level, a trader may expect stock B to rise to balance the link. This might let the trader buy stock B and profit from price convergence.
Cointegration Calculation: How?
Cointegration is calculated using Engle-Granger two-step statistics. Testing for a unit root involves regression analysis on two or more time series variables. Unit roots indicate non-stationary, non-cointegrated variables. Unit root-free tests show stationary and perhaps cointegrated variables.
Cointegration may be calculated in R, Python (using statsmodels), and MATLAB.
Conclusion
Cointegration may help traders find long-term data relationships. Trading price convergence and divergence is profitable when traders understand cointegration. Use cointegration with other trade analysis methodologies.
Sources:
https://en.wikipedia.org/wiki/Cointegration
https://www.investopedia.com/terms/c/cointegration.asp