Can someone plase spell out these formulas to me…at work without books, predtending to work but really thinking about CFA… I know that CORR = CV / (SD1)(SD2) what is CV? what is coefficient of varriation? what is relationship to above formula?

Co of Var = St. Dev/Mean

You mean Correlation = Covariance/(SD1*SD2)

To find out what the coefficient of variation is, simply look at the formula: it measures the amount of risk (SD) per unit of mean return. The correlation is just a standardized version of the CV. It must be a number between -1 and +1. -1 indicates a linear negative relationship, 0 indicates no relationship, +1 indicates perfect positive relationship

please do not confuse coefficient of variation with covariance… CV is a usuall abbrev. for coefficient of variation, Cov for covariance. This is not the same concept.

yes

there is where my confusion is… What is formula for Covariance (Cov)? What is formula for CV?

martin.heidegger Wrote: ------------------------------------------------------- > You mean > > Correlation = Covariance/(SD1*SD2) if the above is the formula for correlation, is there a separate fomula for calculating covariance, or is it just solved algebraically?

yes is there a seperate formula for covariance (I understand the above formula can be rearanged)?

coefficient of variation is simply the amount of risk you take on to get one extra unit of return. The lower the better. Covariance measures in what direction two variables move with respect to their means. Doesn’t tell you how strong it is which is why you standardize using the st dev’s to get the correlation coefficient. You square the Correlation Coefficient to get the Coefficient of Determination. This isn’t in the curriculum I think

Coefficient of Determination is R², which is again something different… (-> linear regression, yes it is empirical correlation²) For two random variables, X and Y: Cov(X,Y) = E[(X-E[X])(Y-E[Y])], Var(X) = Cov(X,X) Corr(X,Y) = Cov(X,Y)/(sqrt(Var(X)Var(Y)) but you can read this in any statistics text, and in CFAI vol I, p 342, 344, 345.

that’s it thanks!