Is this possible that $Cor(X, Y)=0.99$, $Cor(Y, Z)=0.99$ but $Cor(X, Z)=0$? Let $X$, $Y$, and $Z$ are three random variables. Intuitively, I think that it is impossible to have $Cor(X, Y)=0.99$, $Cor(Y, Z)=0.99$ but $Cor(X, Z)=0$. My intuitive thought is that $X$ and $Z$ are nearly linearly correlated to $Y$. Hence, they are more or less linearly correlated, which makes the last equality impossible.
I pose this question because of the question and the comments (include my comments) here.
In general, as some others point out, I agree that it is possible that for some $\rho>0$ we may have $$Cor(X, Y)=\rho, Cor(Y, Z)=\rho  \mbox{ and } Cor(X, Z)=0 \qquad (1).$$
My questions are:

*

*Do you think that (1) is wrong when $\rho$ is close to 1, e.g., 0.99?

*If (1) is wrong when $\rho$ is close to 1, what is the maximum value of $\rho$ so that (1) can be correct?

 A: A more intuitive perspective (an example) to complement @Jarle Tufto's +1 answer:
What you are asking, is whether something like this variance-covariance matrix is possible:
$\bf{\Sigma} = \begin{matrix} & X & Y & Z  \\ X & 1 & 0.9 & 0 \\ Y & 0.9 & 1 & 0.9 \\ Z & 0 & 0.9 & 1\\ \end{matrix}$
This matrix is not positive-semidefinite. In fact, it is indefinite, since its determinant is negative. For example, a multivariate normal vector with this var-cov matrix cannot exist, since its PDF requires the determinant of $\Sigma$. If it is negative, the PDF would become negative, which would lead to negative probabilities. For this not to happen, the condition mentioned by @Jarle Tufto  needs to be fulfilled.
$PDF_{Gauss}(x) =(2\pi)^{-0.5k}\det(\Sigma)^{-0.5}e^{-0.5(x-\mu)^T\Sigma^{-1}(x-\mu)}$
A: If you performed linear regression on $Y$, you would get an $R^2$ value of at most 1.
In your problem setting:

*

*Performing linear regression on $Y$ with $X$ gets $R = 0.99$.

*Performing linear regression on $Y$ with $Z$ gets $R = 0.99$.

*$X$ and $Z$ are not correlated, so they would both independently contribute to the $R^2$ value of a regression on $Y$.

Combining these, when you perform linear regression on $Y$ with both $X$ and $Z$, you get $R^2 = (0.99)^2 + (0.99)^2 > 1$, which is impossible. This should also provide some idea of the bounds on these correlation values.
A: The correlation matrix needs to be positive semi-definite with non-negative eigenvalues.  The eigenvalues of the correlation matrix are the solutions of
$$
\left|
\begin{matrix}
1-\lambda & \rho & \rho \\
\rho & 1-\lambda & 0 \\
\rho & 0 & 1-\lambda 
\end{matrix}
\right|
=(1-\lambda)\big((1-\lambda)^2-2\rho^2)\big)
=0
$$
so the eigenvalues are $1$ and $1\pm\sqrt{2}\rho$.  These are all non-negative for
$$
-\frac1{\sqrt{2}} \le \rho \le \frac1{\sqrt{2}}.
$$
A: I posted this previously on Math StackExchange, but will reiterate here. If $\rho_{AB} = \text{Corr}(A, B)$, and similarly defined for $\rho_{BC}$ and $\rho_{AC}$, we have the inequality.
\begin{align*}
\rho_{AC} \ge \max\{2(\rho_{AB} + \rho_{BC}) - 3, 2\rho_{AB}\rho_{BC} - 1\} 
\end{align*}
Proof. Some notation. I let $\sigma_{AB} = \text{Cov}(A,B)$ and $\sigma_A^2 = \text{Var}(A)$.
Let's first prove $\rho_{AC} \ge 2(\rho_{AB} + \rho_{BC}) - 3$. Recall the identity
\begin{align*}
2 E[X^2] + 2E[Y^2] = E[(X+Y)^2] + E[(X-Y)^2]
\end{align*}
hence $2E[Y^2] \le E[(X+Y)^2] + E[(X-Y)^2]$. Set
\begin{align*}
X = \widetilde{B} - (\widetilde{A} + \widetilde{C})/2 \quad \text{and} \quad Y =
 (\widetilde{A} - \widetilde{C})/2
\end{align*}
where $\widetilde{C} = (C - E[C])/\sigma_C$, the normalized random variable, and similarly for $\widetilde{A}, \widetilde{B}$. Upon substitution and simplification, we get
\begin{align*}
\frac{1}{2}(2 - 2\rho_{AC}) \le (2 - 2\rho_{AB}) + (2 - 2\rho_{BC}) \iff \rho_{AC} \ge 2(\rho_{AB} + \rho_{BC}) - 3
\end{align*}
To prove $\rho_{AC} \ge 2\rho_{AB}\rho_{BC} - 1$, consider the random variable
\begin{align*}
W = 2 \frac{\sigma_{AB}}{\sigma_B^2}B - A
\end{align*}
We can verify $\sigma_W^2 = \sigma_A^2$, and hence $\sigma_{WC} \le \sigma_{W}\sigma_{C} = \sigma_ A \sigma_C$ by the Cauchy-Schwarz inequality. On the other hand, you may compute
\begin{align*}
\sigma_{WC} = 2 \frac{\sigma_{AB}}{\sigma_B^2}\sigma_{BC} - \sigma_{AC}
\end{align*}
Reorganizing all this, we prove $\rho_{AC} \ge 2\rho_{AB}\rho_{BC} - 1$.
In your specific example, with $\rho_{AB} = \rho_{BC} = 0.99$, then no matter the construction of $A, B, C$, we must have $\rho_{AC} \ge 0.9602$.
