nuuskur
Science Advisor
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Here's what comes to mind
In case of independent random variables E(XY) = E(X)E(Y). By first expanding the expression inside expected value:
<br /> \begin{align*}<br /> &[(W_u-W_v)^2 + 2(W_u-W_v)W_v + W_v^2]W_v^2 \\<br /> =&(W_u-W_v)^2W_v^2 + 2(W_uW_v -W_v^2)W_v^2 + W_v^4 \\<br /> =&(W_u-W_v)^2W_v^2 + 2W_uW_v^3 - W_v^4<br /> \end{align*}<br />
To get the mgf of Brownian, one computes
<br /> M_W(x) = \mathbb E(e^{xW_s}) = \mbox{exp}\left ( \frac{1}{2}x^2s\right )<br />
Fourth derivative (w.r.t ##x##) at x=0 gives us the fourth moment, which is (if I calculated correctly) \mathbb E(W_v^4) = 3v^2.
Since W_u-W_v, W_v are independent, squaring them preserves independence, so
<br /> \mathbb E(W_u-W_v)^2W_v^2 = \mathbb E(W_u-W_v)^2\mathbb E(W_v^2) = (u-v)v<br />
Not sure what is happening with the middle part, though. Does it vanish?
In case of independent random variables E(XY) = E(X)E(Y). By first expanding the expression inside expected value:
<br /> \begin{align*}<br /> &[(W_u-W_v)^2 + 2(W_u-W_v)W_v + W_v^2]W_v^2 \\<br /> =&(W_u-W_v)^2W_v^2 + 2(W_uW_v -W_v^2)W_v^2 + W_v^4 \\<br /> =&(W_u-W_v)^2W_v^2 + 2W_uW_v^3 - W_v^4<br /> \end{align*}<br />
To get the mgf of Brownian, one computes
<br /> M_W(x) = \mathbb E(e^{xW_s}) = \mbox{exp}\left ( \frac{1}{2}x^2s\right )<br />
Fourth derivative (w.r.t ##x##) at x=0 gives us the fourth moment, which is (if I calculated correctly) \mathbb E(W_v^4) = 3v^2.
Since W_u-W_v, W_v are independent, squaring them preserves independence, so
<br /> \mathbb E(W_u-W_v)^2W_v^2 = \mathbb E(W_u-W_v)^2\mathbb E(W_v^2) = (u-v)v<br />
Not sure what is happening with the middle part, though. Does it vanish?
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