I wanted to upload here a small problem I started to work on…. unfortunatley, I could not find (yet) a proper answer. Any comments and suggestions are welcomed. The problem is simple: consider 3 random variables that can be interpreted as exchange rates. Thus X is the exchange rate USD versus EUR, Y is the exchange rate EUR versus YEN, while Z is the exchange rate USD versus YEN. We then have the following constraint XY=Z. Given that constraint, what are the implied constraints on the correlation matrix of random vector (X,Y,Z) ?

- Bounds on correlation

There are classically standard constraints on correlation. For instance, from Cauchy-Schwarz’s inequality

but those bounds are not necessarily sharp, given distributions of and . From Fréchet-Hoeffding bounds, we know that

where . Further, correlation matrices are necessarily positive-semidefinite matrices.

To go further to find possible bounds, recall that

and therefore

i.e.

Similarity, will be a function of .

Thus, given marginal distributions of and , covariances (and also correlations and ) are functions of , and .

Somehow, we have obtained 2 degrees of freedom: if there are no additional constraints on those two coefficients, there should be no other constraints than having positive-semidefinite matrices.

I guess that and should not be connected. Those quantities are related to the *co-skewness ^{*}* and they should be different when dependence is

*asymmetric*.

Given a 3 parameter copula for pair , respectively describing global dependence (that can be related to and tail asymmetry (that should be related either to and depending on the corner) it should be possible to derive any kind of triplets.

- Running simulations

I have tried to generate a lot of samples. The idea is to draw pairs from mixtures of Marshall-Olkins’ copula (wich are asymmetric), where parameters where generated randomly. I draw 250 values according to that copula. Then, the third variable is obtained taking the product of normalized pairs (or Studentized). And then calculate the associated correlation matrix.

- draw from (but actually any positive distribution should work) and
- do 250 times the following (index )

- draw with a Bernoulli distribution
- draw&nbs

p; from copula if and if - set and , and

- estimate the correlation of sample

In dimension 3, I have the following graph,

So far I have not been able to obtain *all* possible correlations, I guess… If anyone has suggestions…

* ^{* }*Recall that the co-skewness matrix, in dimension 2, for a pair is simply

where .