Contents
What is a cointegration relationship?
Cointegration is the existence of long-run relationship between two or more variables. However, the correlation does not necessarily means “long-run”. Correlation is simply a measure of the degree of mutual association between two or more variables.
How do you identify cointegration?
Trace tests When using the trace test to test for cointegration in a sample, we set K0 to zero to test whether the null hypothesis will be rejected. If it is rejected, we can deduce that there exists a cointegration relationship in the sample.
How are cointegration tests used in real life?
Cointegration tests identify scenarios where two or more non-stationary time series are integrated together in a way that they cannot deviate from equilibrium in the long term. The tests are used to identify the degree of sensitivity of two variables to the same average price over a specified period of time.
When does a cointegration of two time series occur?
Cointegration occurs when two or more nonstationary time series: Have a long-run equilibrium. Move together in such a way that their linear combination results in a stationary time series.
What is the intuition behind pairs of trading?
The intuition behind pairs trading is that two cointegrated instruments will follow the same long-run path (since they presumably have some common factor, such as they are both oil companies and are heavily influenced by the price of oil), and any deviations will ultimately return back to the mean.
Why is the error correction model important in cointegration?
Cointegration implies that time series will be connecting through an error correction model. The error correction model is important in time series analysis because it allows us to better understand long-run dynamics. Additionally, failing to properly model cointegrated variables can result in biased estimates. The error correction model: