If the two random variables are both distorted, i.e.,
, the correlation of
and
is
.
When
, the expression becomes,
![{\displaystyle \phi _{r_{1}r_{2}}={\frac {1}{2\pi {\sqrt {1-\rho ^{2}}}}}\left[\int _{0}^{\infty }\int _{0}^{\infty }e^{-\alpha }\,dy_{1}dy_{2}+\int _{-\infty }^{0}\int _{-\infty }^{0}e^{-\alpha }\,dy_{1}dy_{2}-\int _{0}^{\infty }\int _{-\infty }^{0}e^{-\alpha }\,dy_{1}dy_{2}-\int _{-\infty }^{0}\int _{0}^{\infty }e^{-\alpha }\,dy_{1}dy_{2}\right]}](//wikimedia.org/api/rest_v1/media/math/render/svg/425ab751045a9cff9d156db7d9a5efb8c6431745)
where
.
Noticing that
,
and
,
,
we can simplify the expression of
as

Also, it is convenient to introduce the polar coordinate
. It is thus found that
.
Integration gives
,
This is called "Arcsine law", which was first found by J. H. Van Vleck in 1943 and republished in 1966.[2][3] The "Arcsine law" can also be proved in a simpler way by applying Price's Theorem.[4][5]
The function
can be approximated as
when
is small.
Price's Theorem
Given two jointly normal random variables
and
with joint probability function
,
we form the mean

of some function
of
. If
as
, then
.
Proof. The joint characteristic function of the random variables
and
is by definition the integral
.
From the two-dimensional inversion formula of Fourier transform, it follows that
.
Therefore, plugging the expression of
into
, and differentiating with respect to
, we obtain

After repeated integration by parts and using the condition at
, we obtain the Price's theorem.

[4][5]
Proof of Arcsine law by Price's Theorem
If
, then
where
is the Dirac delta function.
Substituting into Price's Theorem, we obtain,
.
When
,
. Thus
,
which is Van Vleck's well-known result of "Arcsine law".
[2][3]