Correlation is better than covariance for these reasons: 1 -- Because correlation removes the effect of the variance of the variables, it provides a standardized, absolute measure of the strength of the relationship, bounded by -1.0 and 1.0.
However, when it comes to making a choice between covariance vs correlation to measure relationship between variables, correlation is preferred over covariance because it does not get affected by the change in scale.
Both covariance and correlation measure the relationship and the dependency between two variables. Covariance indicates the direction of the linear relationship between variables. Correlation measures both the strength and direction of the linear relationship between two variables.
Covariance is when two variables vary with each other, whereas Correlation is when the change in one variable results in the change in another variable.
Covariance is a measure of how two variables change together, but its magnitude is unbounded, so it is difficult to interpret. By dividing covariance by the product of the two standard deviations, one can calculate the normalized version of the statistic. This is the correlation coefficient.
In short, covariance tells you that two variables change the same way while correlation reveals how a change in one variable affects a change in the other. You also may use covariance to find the standard deviation of a multi-stock portfolio.
Using the covariance matrix is one way for building factors that account for the size of the state. Hence, my answer is to use covariance matrix when variance of the original variable is important, and use correlation when it is not.
Explanation: Correlation is the process of studying the cause and effect relationship that exists between two variables. Correlation coefficient is the measure of the correlation that exists between two variables.