Question

Explain Covariance and correlation.

Answer 1

Covariance

Covariance measures the directional relationship between the returns on two assets. A positive covariance means that asset returns move together while a negative covariance means they move inversely. Covariance is calculated by analyzing at-return surprises (standard deviations from the expected return) or by multiplying the correlation between the two variables by the standard deviation of each variable.

Key points:-

(1) Covariance is a statistical tool that is used to determine the relationship between the movement of two asset prices.

(2) When two stocks tend to move together, they are seen as having a positive covariance; when they move inversely, the covariance is negative.

(3) Covariance is a significant tool in modern portfolio theory used to ascertain what securities to put in a portfolio.

(4) Risk and volatility can be reduced in a portfolio by pairing assets that have a negative covariance.

Correlation

Correlation, in the finance and investment industries, is a statistic that measures the degree to which two securities move in relation to each other. Correlations are used in advanced portfolio management, computed as the correlation coefficient, which has a value that must fall between -1.0 and +1.0.

A perfect positive correlation means that the correlation coefficient is exactly 1. This implies that as one security moves, either up or down, the other security moves in lockstep, in the same direction. A perfect negative correlation means that two assets move in opposite directions, while a zero correlation implies no relationship at all.

Key points:-

(1) Correlation is a statistic that measures the degree to which two variables move in relation to each other.

(2) In finance, the correlation can measure the movement of a stock with that of a benchmark index, such as the Beta.

(3) Correlation measures association, but does not tell you if x causes y or vice versa, or if the association is caused by some third (perhaps unseen) factor.