Correlation between variables I asked this question on stats SE but did not find a suitable answer so far. Maybe someone can help. 
Given n random variables x1,...,xn (one-dimensional). 
The following is known (corr() = Pearson correlation):
corr(x1,x2) = a
corr(x2,x3) = a

The actual values of the random variables and their covariances are unkown though. Only some of their correlations are known.
From this, is it possible to calculate
corr(x3,x1) = ?

or give an estimate of the lowest possible correlation coefficient
corr(x3,x1) > a

More generally: 
Given set of correlations 
corr(x_i, x_i+1) with i=[1..c], c<n

is it possible to either directly calculate 
corr(x_1, x_c+1)

or give a lower bound a of the coefficient with
corr(x_1, x_c+1) > a

 A: For the first part of your question you were pointed to a quant answer which would give you a lower bound of  $a^2-\sqrt{1-a^2}$.  The upper bound is clearly $1$, for example if $X_1 = X_3$. 
You can apply the same result successively, though note than once $0$ appears in the range of possibilities then you need to take slightly more care on the next step.
A: Danny Kaplan's textbook on statistics makes something of an issue of this.  Things like this: If there's a 95% correlation between $X$ and $Y$ and a 90% correlation between $Y$ and $Z$, then what are the biggest and smallest possible correlations between $X$ and $Z$?  The answer comes from the fact that the correlation is the cosine of an angle between two vectors in an inner product space.  A 95% correlation means the angle is $\arccos 0.95 \approx 18.19487^\circ$.  And 90% means the angle is $\arccos 0.9 \approx 25.8419^\circ$.  Add the angles, getting $44.03677^\circ$, and the cosine is $0.7188938$.  That's the smallest possible correlation between $X$ and $Z$.  Subtract the angles, getting $7.64703^\circ$.  The cosine is about $0.9911066$.  That's the largest possible correlation between $X$ and $Z$.
A: I find it most intuitive to use the cholesky-decomposition of some correlation-matrix to look at such questions. The cholesky-decomposition provides a lower triangular matrix which always has (given the variables $\small x_1,x_2,x_3 $) the form
$\qquad \small \begin{array} {r|lll}
x_1: & 1 & . & . & \\
x_2: & a_1 & a_2 & . \\
x_3: & b_1 & b_2 & b_3 \\
\end{array} $
which can be continued to more rows/columns and where the dots mean (systematical) zeroes.
The squares of the entries of one row sum up to 1 , and the correlations are the sum of the products of the entries along two rows, say for $\small corr(x_1,x_2)=1 \cdot a_1 $ or $\small corr(x_2,x_3)=a_1 \cdot b_1 + a_2 \cdot b_2 $
If we now want to know the possible range for the correlation $\small corr(x_2,x_3) $ given $\small corr(x_1,x_2)=a $ and $\small corr(x_1,x_3)=b $  then we know immediately that a,b must be the entries in the first column:
$\qquad \small \begin{array} {r|lll}
x_1: & 1 & . & . & \\
x_2: & a & a_2 & . \\
x_3: & b & b_2 & b_3 \\
\end{array} $
and by the rule of sum-of-squares = 1 we get
$\qquad \small \begin{array} {r|lll}
x_1^*: & 1 & . & . & \\
x_2^*: & a^2 & 1-a^2 & . \\
x_3^*: & b^2 & b_2^2 & 1-b^2-b_2^2 \\
\end{array} $
Here all except the entry $\small b_2$ are fixed or determined by the choice of $\small b_2$, which is also limited to the obvious interval $\small 0 \le b_2^2 \le  1-b^2$.     
Let's for simpliness assume a and b are positive values.
Then it is also obvious, that we get the possible range for the correlation $\small corr(x_2,x_3) $ if we set $\small x_2 $      


*

*to its maximum, that is $\small b_2^2 = 1-b^2, b_2=\sqrt{1-b^2} b_3=0$
$\qquad \small \begin{array} {r|lll} x_1: & 1 & . & . &
   \\ x_2: & a & \sqrt{1-a^2} & . \\ x_3: & b & \sqrt{1-b^2} & 0 \\
   \end{array} $
and $\small corr(x_2,x_3)=a \cdot b + \sqrt{1-a^2}\cdot \sqrt{1-b^2} $
If a=b we have then $\small corr(x_2,x_3)=a^2 + (1-a^2) = 1 $    

*to some mean value, (which, when we allow only positive values for all entries
is also its minimum) that is $\small b_2^2 = 0, b_3^2=1-b^2,b_3=\sqrt{1-b^2}$ and
$\qquad \small \begin{array} {r|lll}
     x_1: & 1 & . & . & \\
     x_2: & a & \sqrt{1-a^2} & . \\
     x_3: & b & 0 & \sqrt{1-b^2} \\
     \end{array} $
and $\small corr(x_2,x_3)=a \cdot b + 0 $
If a=b we have then $\small corr(x_2,x_3)=a^2 + 0 $    

*to its minimum (possibly negative, and then not minimal in its absolute value), that is $\small b_2^2 = 1-b^2, b_2=-\sqrt{1-b^2} ,\qquad b_3=0$
$\qquad \small \begin{array} {r|lll} x_1: & 1 & . & . &
   \\ x_2: & a & +\sqrt{1-a^2} & . \\ x_3: & b & - \sqrt{1-b^2} & 0 \\
   \end{array} $
and $\small corr(x_2,x_3)=a \cdot b - \sqrt{1-a^2}\cdot \sqrt{1-b^2} < a\cdot b $       
If a=b then we get $\small corr(x_2,x_3)=a \cdot a - \sqrt{1-a^2}\cdot \sqrt{1-a^2} = 2a^2-1 < a^2 $ which might also come out to be zero or even negative.      
Completely similarly this can be done if more variables in the correlation-matrix are existent, because only the number of rows/columns in the cholesky-factor increases accordingly.
(Remark: for simpliness of the exposition of the principle of that calculations I did not attempt a more exact case-distinction) 
