I'd be inclined to say no, Im looking for the answer myself. But if you have Cov(A-B,A+B)=Cov(A,A)-Cov(B,B)-Cov(B,A)+Cov(A,B), then the last two will cancel but if Var(B)>Var(A) then we would get a negative covariance. [Cov(A,A)=Var(A)] So it looks possible because as far as I know there is no squaring of the coefficeients when you bring them out of the covariance so a negative answer is entirely possible.
variance - covariance - how to calculate and its uses
The covariance between two variables is simply the average product of the values of two variables that have been expressed as deviations from their respective means. ------------------------------------------------------------------------------------------------- A worked example may be referenced at: http://math.info/Statistics/Covariance
One can find information on the covariance matrix on the Wikipedia website where there is much information about the mathematics involved. One can also find information on Mathworks.
look in a maths dictionary
Maybe I'm not providing a full information. But if you're asking about importance of covariance in trading, then before investing you should assess if your stocks are codependent. All investors try to diversify a portfolio and minimize risks. and covariance can show if two stocks are exposed to the same risk. Now it's easily calculated, there're different services. Actually, for better understanding just read Investopedia really.
variance - covariance - how to calculate and its uses
Covariance - 2011 was released on: USA: 20 September 2011
) Distinguish clearly between analysis of variance and analysis of covariance.
[N*(N-1)]/2 N=1700 (1700*1699)/2 = 1,444,150 Covariance
The covariance between two variables is simply the average product of the values of two variables that have been expressed as deviations from their respective means. ------------------------------------------------------------------------------------------------- A worked example may be referenced at: http://math.info/Statistics/Covariance
A mix of linear regression and analysis of variance. analysis of covariance is responsible for intergroup variance when analysis of variance is performed.
Covariance: An Overview. Variance refers to the spread of a data set around its mean value, while a covariance refers to the measure of the directional relationship between two random variables.
The cast of Covariance - 2011 includes: David Razowsky as Russell Gains Dawn Westlake as Genevieve Pace
Correlation is scaled to be between -1 and +1 depending on whether there is positive or negative correlation, and is dimensionless. The covariance however, ranges from zero, in the case of two independent variables, to Var(X), in the case where the two sets of data are equal. The units of COV(X,Y) are the units of X times the units of Y. correlation is the expected value of two random variables (E[XY]),whereas covariance is expected value of variations of two random variable from their expected values,
See related link.
When you carrying out multivariate analyses.
One can find information on the covariance matrix on the Wikipedia website where there is much information about the mathematics involved. One can also find information on Mathworks.