In the example, there is a positive covariance, so the two stocks tend to move together. When one stock has a positive return, the other tends to have a positive return as well. If the result were negative, then the two stocks would tend to have opposite returns—when one had a positive return, the other would have a negative return.
Finding that two stocks have a high or low covariance might not be a useful metric on its own. Covariance can tell how the stocks move together, but to determine the strength of the relationship, we need to look at their correlation.
The correlation should, therefore, be used in conjunction with the covariance, and is represented by this equation:. The equation above reveals that the correlation between two variables is the covariance between both variables divided by the product of the standard deviation of the variables. While both measures reveal whether two variables are positively or inversely related, the correlation provides additional information by determining the degree to which both variables move together.
The correlation will always have a measurement value between -1 and 1, and it adds a strength value on how the stocks move together. If the correlation is 1, they move perfectly together, and if the correlation is -1, the stocks move perfectly in opposite directions. If the correlation is 0, then the two stocks move in random directions from each other. 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. The standard deviation is the accepted calculation for risk, which is extremely important when selecting stocks. Most investors would want to select stocks that move in opposite directions because the risk will be lower, though they'll provide the same amount of potential return.
Covariance is a common statistical calculation that can show how two stocks tend to move together. Because we can only use historical returns , there will never be complete certainty about the future. Also, covariance should not be used on its own. Instead, it should be used in conjunction with other calculations such as correlation or standard deviation. Fundamental Analysis. Portfolio Management. Technical Analysis.
Portfolio Construction. It is obtained by dividing the covariance of two variables by the product of their standard deviations. From the previous example, the covariance between Stock A and Stock B is 3. Calculate the correlation given that the standard deviation of Stock A is 2. Share on :. Generic selectors. Exact matches only. Search in title. Search in content. Search in excerpt. Search in posts. Search in pages. Advice and How to Study Videos. All Levels.
Alternative Investments. BookLet Top Level. Corporate Finance. Demystified Videos. Financial Reporting and Analysis. Fixed Income. Level I. Level I Economics Full Videos. Level I Ethics Full Videos. Level II. Correlation and covariance are very closely related to each other, and yet they differ a lot.
Covariance defines the type of interaction, but correlation defines not only the type but also the strength of this relationship. Due to this reason, correlation is often termed as the special case of covariance. However, if one must choose between the two, most analysts prefer correlation as it remains unaffected by the changes in dimensions, locations, and scale. However, an important limitation is that both these concepts measure the only linear relationship. This has been a guide to the Covariance vs Correlation.
Here we discuss the top 5 differences between Covariance and Correlation along with infographics and a comparison table. You may also have a look at the following articles —.
Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. Forgot Password? Correlation is considered as the best tool for for measuring and expressing the quantitative relationship between two variables in formula.
On the other hand, covariance is when two items vary together. Read the given article to know the differences between covariance and correlation. Basis for Comparison Covariance Correlation Meaning Covariance is a measure indicating the extent to which two random variables change in tandem. Correlation is a statistical measure that indicates how strongly two variables are related. What is it? Covariance is a statistical term, defined as a systematic relationship between a pair of random variables wherein a change in one variable reciprocated by an equivalent change in another variable.
Further, it ascertains the linear relationship between variables. Therefore, when the value is zero, it indicates no relationship. In addition to this, when all the observations of the either variable are same, the covariance will be zero.
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