Is a weighted average of two correlation matrices again a correlation matrix?

Let $R$ and $Q$ be two correlation matrices of the same size and let $p\in[0,1]$. I'm trying to show that $pR+(1-p)Q$ is still a correlation matrix.

I claim that $\sqrt pX+\sqrt{1-p}Y$ is a random vector that has correlation matrix $pR+(1-p)Q$. Here, $X$ is the random vector with correlation $R$ and $Y$ is the random vector with correlation $Q$. Also, $X$ and $Y$ are independent. I can get the covariance part to match but I can't get the standard deviation in the denominator to match. I'm not sure why. Can someone show me how to do this?

Here is an argument in two steps. Firstly, $M = pR + (1-p)Q$ is the variance-covariance matrix of some vector of random variables. Proof: we require to show that $M$ is semi-positive definite. We know that $R$ and $Q$ are, since they are correlation matrices. Hence for any non-zero vector $x$, $x^tPx \geq 0$ and $x^tQx \geq 0$. Since $p\geq0$ and $(1-p)\geq0$ we have, as required:

$$x^tMx = p(x^tRx) + (1-p)(x^tQx) \geq 0$$.

For this covariance matrix to be a correlation matrix we additionally require that the random variables that form the components of the vector have variance one. The variances are the diagonal elements, and we already know that the diagonal elements of $R$ and $Q$ are one since they are correlation matrices. So:

$$M_{ii} = pR_{ii} + (1-p)Q_{ii} = p(1) + (1-p)(1) = 1$$

This estalishes that "the weighted average of two correlation matrices is a correlation matrix", which is the question in the title.

It doesn't address the question implicit in the body of the post though:

I claim that $\sqrt pX+\sqrt{1-p}Y$ is a random vector that has correlation matrix $pR+(1-p)Q$. Here, $X$ is the random vector with correlation $R$ and $Y$ is the random vector with correlation $Q$. Also, $X$ and $Y$ are independent. I can get the covariance part to match but I can't get the standard deviation in the denominator to match. I'm not sure why.

The covariance part is correct, which is why that part matched. But the claim about the correlation matrix of $\sqrt pX+\sqrt{1-p}Y$ is incorrect, which is why the algebra doesn't work out. I will demonstrate this with simulation from the bivariate normal distribution using $p=\frac{1}{2}$. The relevant intuition in my choice of parameters is that the variances/covariance of $X$ swamp any variation in $Y$, so that the correlation structure of $\frac{1}{\sqrt{2}}X + \frac{1}{\sqrt{2}}Y$ is largely determined by $X$. Here's the R code for the set-up:

library(MASS) # has mvrnorm to simulate multivariate normals
n <- 1e7 # simulated sample size
mu <- c(0,0)
Sigma1 <- matrix(c(100,50,50,100), nrow=2)
Sigma2 <-matrix(c(1,0,0,1), nrow=2)

# covariance = 50, correlation = 0.5
x <- mvrnorm(n, mu, Sigma1)

# covariance = correlation = 0
y <- mvrnorm(n, mu, Sigma2)

# covariance = ?, correlation = ?
z <- sqrt(0.5)*x + sqrt(0.5)*y


For $X$ and $Y$ we find the covariance between the first and second components of the vector is pretty close to the specified population covariance. For $\frac{1}{\sqrt{2}}X + \frac{1}{\sqrt{2}}Y$ we find the covariance is indeed the weighted average ($p = \frac{1}{2}$) between the covariances for $X$ and $Y$.

> cov(x[,1], x[,2])
[1] 50.0234
> cov(y[,1], y[,2])
[1] -0.0004923819
> cov(z[,1], z[,2])
[1] 25.01153


For the correlation between first and second components, again the sample of simulated $X$ and $Y$ behaves in the expected manner, but $\frac{1}{\sqrt{2}}X + \frac{1}{\sqrt{2}}Y$ largely reflects the behaviour of $X$.

> cor(x[,1], x[,2])
[1] 0.5001478
> cor(y[,1], y[,2])
[1] -0.0004925274
> cor(z[,1], z[,2])
[1] 0.4951888


Let's visualise the problem by looking at the relationship between the first and second components for our samples of each of the three random vectors. To get it to plot in a reasonable time I resampled with n <- 1e5 first but this didn't substantially alter the results.

require(ggplot2)
df <- data.frame(variable=rep(c("X", "Z", "Y"), each=n),
first=c(x[,1], z[,1], y[,1]),
second=c(x[,2], z[,2], y[,2]))
g <- ggplot(df, aes(x=first, y=second, colour=variable)) +
geom_point(alpha = 0.1) + coord_fixed() + theme_bw() +
guides(colour = guide_legend(override.aes = list(alpha = 1))) +
xlab("First component") + ylab("Second component")
print(g)


g <- g + guides(colour=FALSE) + facet_grid(. ~ variable)
print(g)


We can see that the components of $Y$ are not correlated (the points from the sample of $Y$ form a roughly circular blob) while the components of $X$ are (its points form a roughly elliptical blob with its axes not aligned with horizontal and vertical). Their weighted average $Z$ resembles a scaled-down version of $X$ because the contribution from $Y$ is so small. This produces the required averaging of covariances, but leaves the correlation almost unchanged from that of $X$.

So if the original poster wants to make headway with their $\sqrt pX+\sqrt{1-p}Y$ approach (which is rather neat) then it's important to prevent such "swamping". This is feasible, since the covariance matrix does behave in the manner they desire, and a correlation matrix is just the covariance matrix for a vector whose components each have unit variance. So rather than setting $X$ as any random vector with correlation $R$, and $Y$ as any random vector independent of $X$ and with correlation $Q$, try being stricter by demanding that $Q$ and $R$ are the covariance matrices. Can you make your algebra work now? Look at this simulation with $p=\frac{1}{4}$ so that $Z$ is mostly weighted towards $Y$:

> Sigma1 <- matrix(c(1,0.4,0.4,1), nrow=2) # variance = 1 for each component
> Sigma2 <-matrix(c(1,0.8,0.8,1), nrow=2)
>
> # covariance = correlation = 0.4
> x <- mvrnorm(n, mu, Sigma1)
>
> # covariance = correlation = 0.8
> y <- mvrnorm(n, mu, Sigma2)
>
> # p = 1/4
> # covariance = ?, correlation = ?
> z <- sqrt(0.25)*x + sqrt(0.75)*y
>
> cor(x[,1], x[,2])
[1] 0.4001048
> cor(y[,1], y[,2])
[1] 0.8001744
> cor(z[,1], z[,2]) # weighted average .25*.4 + .75*.8 = 0.7
[1] 0.7000963


• (Obviously there is no point specifying that $x$ is non-zero, but I think it helps make the contrast with the positive definite case) Commented Dec 1, 2014 at 19:33
• Thank you very much for your help. I learned a lot from your post. Commented Dec 4, 2014 at 20:13

To be a non-degenerate correlation matrix, $pR+(1-p)Q$ must have two properties:

1. All the diagonal elements of $pR+(1-p)Q$ must be $1$
2. $pR+(1-p)Q$ must be positive definite

Obviously (1) is met...I think you see that. Noting both $R$ and $Q$ are positive definite, (2) holds since any positive definite matrix multiplied by a positive scalar is still positive definite and the sum of any 2 positive definite matrices is also positive definite.

Hope that simplifies things!

• +1 -- but for those not entirely familiar with the concept that positive-definiteness is really a property of a quadratic form (rather than of any matrix used to represent it), it might be worthwhile noting that what you say is true of symmetric real matrices, not of all real matrices.
– whuber
Commented Dec 1, 2014 at 16:42
• Should we say positive semi definite? stats.stackexchange.com/questions/56832/… Commented Dec 1, 2014 at 19:05
• @Silver It makes no difference. The only potential nuance along these lines is the fact that when $R$ and $Q$ are positive-semidefinite but indefinite, nevertheless $pR+(1-p)Q$ can be definite.
– whuber
Commented Dec 1, 2014 at 20:06