Covariance vs Correlation

Difference Between Covariance and Correlation

Covariance and Correlation are two terms which are exactly opposite to each other, they both are used in statistics and regression analysis, covariance shows us how the two variables vary from each other whereas correlation shows us the relationship between the two variables and how are they related.

Correlation and covariance are two statistical concepts that are used to determine the relationship between two random variables. Correlation defines how a change in one variable will impact the other, while covariance defines how two items vary together. Confusing? Let’s dive in further to understand the difference between these closely related terms.

What is Covariance?

Covariance measures how the two variables move with respect to each other and is an extension of the concept of variance (which tells about how a single variable varies). It can take any value from -∞ to +∞.

The higher this value, the more dependent the relationship is. A positive number signifies positive covariance and denotes that there is a direct relationship. Effectively this means that an increase in one variable would also lead to a corresponding increase in the other variable provided other conditions remain constant.
On the other hand, a negative number signifies negative covariance, which denotes an inverse relationship between the two variables. Though covariance is perfect for defining the type of relationship, it is bad for interpreting its magnitude.

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Source: Covariance vs Correlation (wallstreetmojo.com)

What is the Correlation?

Correlation is a step ahead of covariance as it quantifies the relationship between two random variables. In simple terms, it is a unit measure of how these variables change with respect to each other (normalized covariance value).

Unlike covariance, the correlation has an upper and lower cap on a range. It can only take values between +1 and -1. A correlation of +1 indicates that random variables have a direct and strong relationship.
On the other hand, the correlation of -1 indicates that there is a strong inverse relationship, and an increase in one variable will lead to an equal and opposite decrease in the other variable. 0 means that the two numbers are independent.

Formula for Covariance and Correlation

Let’s express these two concepts, mathematically. For two random variables A and B with mean values as Ua and Ub and standard deviation as Sa and Sb respectively:

Effectively the relationship between the two can be defined as:

Both correlations and covariance find application in fields of statistical and financial analysis. Since correlation standardizes the relationship, it is helpful in comparison of any two variables. This help analyst in coming up with strategies like pair trade and hedgingHedging Hedging is a type of investment that works like insurance and protects you from any financial losses. Hedging is achieved by taking the opposing position in the market.read more for not only efficient returns on the portfolio but also safeguarding these returns in terms of adverse movements in the stock market.

Correlation vs Covariance Infographics

Let’s see the top difference between Correlation vs Covariance.

Key Differences

Covariance is an indicator of the degree to which two random variables change with respect to each other. Correlation, on the other hand, measures the strength of this relationship. The value of correlationCorrelation Correlation is a statistical measure between two variables that is defined as a change in one variable corresponding to a change in the other. It is calculated as (x(i)-mean(x))*(y(i)-mean(y)) / ((x(i)-mean(x))2 * (y(i)-mean(y))2.read more is bound on the upper by +1 and on the lower side by -1. Thus, it is a definite range. However, the range of covariance is indefinite. It can take any positive value or any negative value (theoretically, the range is -∞ to +∞). You can rest assured that a correlation of .5 is greater than .3, and the first set of numbers (with correlation as .5) are more dependent on each other than the second set (with correlation as .3). Interpreting such a result would be tough from covariance calculations.
Change of scale affects covariance. For example, if the value of two variables is multiplied by similar or different constants, then this affects the calculated covariance of these two numbers. However, applying the same mechanism for correlation, multiplication by constants does not change the previous result. This is because a change of scale does not affect correlation.
Unlike covariance, correlation is a unit-free measure of the inter-dependency of two variables. This makes it easy for calculated correlation values to be compared across any two variables irrespective of their units and dimensions.
CovarianceCovariance Covariance is a statistical measure used to find the relationship between two assets and is calculated as the standard deviation of the return of the two assets multiplied by its correlation. If it gives a positive number then the assets are said to have positive covariance i.e. when the returns of one asset goes up, the return of second assets also goes up and vice versa for negative covariance.read more can be calculated for only two variables. Correlation, on the other hand, can be calculated for multiple sets of numbers. Another factor that makes the correlation desirable to analysts compared to covariance.