Correlation is a term beginner traders might be more familiar with but cointegration is what advanced traders will sit up and pay attention to. But are they the same term? Certainly not but are different sides to the same coin so to speak.

Correlation and cointegration are terms used in regression analysis, unfortunately they are often confused for synonyms by many students. In similar terms, both are commonly used in forex trading to calculate the relationship between two or more variables over a specific timeframe.

When two variables move in the same direction, they are said to be positively correlated. If they move in opposing directions, the correlation is said to be negative.

Cointegration helps identify the degree to which two variables are sensitive to the same average price over a specific period of time. Thus, cointegration does not reflect whether the pairs would move in the same or opposite direction, but can tell you whether the distance between them remains the same over time.

For day traders, this means that the movement of these variables are not related. However, in the longer term, the variables may track a common average value.

Since cointegration identifies variables that would not drift too far away from each other in the longer term and would revert to a mean distance between them, the concept of cointegration is used for hedging.

Here, too, the degree of cointegration would need to be calculated. The higher the degree of cointegration between two variables, the greater is the probability of them maintaining a stable or constant distance. Another variable is the time two cointegrated variables take to revert to the mean.

Correlation is easier to identify than cointegration; however, the latter is considered as the more reliable regression analysis tool. Therefore, correlation is mostly used by beginners, while more experienced traders rely more heavily on cointegration.

In the simplest setting, the relationship between two variables can be estimated with the help of various techniques but all the techniques are not equally viable for all types of variables. Which technique is suitable, often depends upon nature of variables and purpose of the relationship. In time series analysis, variables often deviate from their mean paths because of various shocks and cyclic fluctuations. Simple O.L.S. regressions do not capture these shocks and cyclic events. And thus, results between two-time series variables might be spurious. The co-integration is used to accommodate such deviations in its estimation.

When two time series variables X and Y do not individually hang around a constant value but their combination (could be linear) does hang around a constant is called cointegration. Sometimes it's considered as a long term relationship between the said variables. On the other hand, correlation is simply a measure of a linear association between any two continuous variables.

Co integration is nothing but correlation between two variables. But, one need to use the simple regression analysis to find the covariance between the two variables using coefficient of Co-integration.

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.

Best wishes,

Atif

what is the difference between cointegration and correlation?

Correlation is based on strength of association between two or more variable which fall between 0 and 1 and it does depend on time lag while cointegration is having two lag event; let say xt and yt so the cointegration is having the two lag event come together if they assume certain condition. So in conclusion correlation does not depend on any condition while cointegration does due to the time lag.

In time series, we have conditional and unconditional observation so cointegration is conditional while correlation is unconditional

Dear Samson,

I just read your post:

"Correlation is based on strength of association between two or more variable which fall between 0 and 1."

Do you mean the correlation is also defined for more than two variables like Correl(X,Y,Z)?

In my opinion, one of the distinct features of correlation and co-integration, among others, is that the correlation is defined for two variables while co-integration is defined for N variables.

Two or more variables are cointegrated if and only if they share common trends.

Srikanth Potharla

Having gone through the answers shared by research fellows. I enjoyed the interest of people to share knowledge.

Refer to you question; correlation is very basic term used to find the association between two variables (positive, negative or no association). Co-integration is the co-movement among underlying variables over long-run. This long-run estimation feature distinguishes it from correlation. If you can go through Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326 or their earlier work, you will come to know the clear concept.

Co-Integration is the correlation among time series non-stationary variables.

Cointegration is the presence of long-run or multiple long run relationship between variables. Nevertheless, the correlation does not necessarily means "long-run". Correlation is simply a measure of the degree of mutual association between two or more variables

Muhammad Atif Nawaz Malik Adeel what is long-run relationship ?

Please read this Link:

https://en.wikipedia.org/wiki/Cointegration

Cointegration relates to the special topic of time series econometrics, where time-series data are often not stationary (short-term in-equilibrium relationships, but long-term equilibrium relationships)

Correlation cannot be separated by regression. Correlation is the closeness of the relationship between the independent variable and the dependent variable which cannot be separated, while the regression of the effect between the independent variable and the dependent variable