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How does one compute $E[(\int_0^T W_s ds)^2]$ where $(W_t)_{t \in [0,T]}$ is standard Brownian motion in $(\Omega, \mathscr F, \mathbb P)$?

Apparently proving

$$\int_0^T W_s ds = \int_0^T (T-s) dW_s \tag{*}$$

might need to assume that $E[(\int_0^T W_s ds)^2] < \infty$


I think I can just use Ito's lemma to show $(*)$ and then use Ito isometry to show that

$$E[(\int_0^T W_s ds)^2] = E[(\int_0^T (T-s) dW_s)^2] = (\int_0^T (T-s)^2 ds) < \infty$$

Is that okay? If not, why?

Regardless, I'm still curious as to how compute $E[(\int_0^T W_s ds)^2]$ besides using the representation if it's possible? I don't think we encountered $\int_0^T W_s ds$ in classes

BCLC
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1 Answers1

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Assuming that $E[W_tW_s]=\sigma^2\min(t,s)$, \begin{align} E\left[\left(\int_0^TW_s\,\mathrm ds\right)^2\right]&=E\left[\int_0^TW_t\,\mathrm dt\int_0^TW_s\,\mathrm ds\right]\\ &=\int_0^T\int_0^T E[W_tW_s]\,\mathrm dt\,\mathrm ds\\ &=\int_0^T\int_0^T \sigma^2\min(t,s)\,\mathrm dt\,\mathrm ds\\ &=\int_0^T\left[\int_0^T\sigma^2\min(t,s)\,\mathrm dt\right]\,\mathrm ds\\ &=\int_0^T\left[\int_0^s\sigma^2 \min(t,s)\,\mathrm dt + \int_s^T\sigma^2 \min(t,s)\,\mathrm dt\right]\,\mathrm ds\\ &=\int_0^T \int_0^s\sigma^2 \min(t,s)\,\mathrm dt\,\mathrm ds + \int_0^T\int_s^T\sigma^2 \min(t,s)\,\mathrm dt\,\mathrm ds\tag{1}\\ &= \int_0^T\int_0^s\sigma^2 t\,\mathrm dt\,\mathrm ds + \int_0^T\int_s^T\sigma^2 s\,\mathrm dt\,\mathrm ds\tag{2}\\ &=\int_0^T\sigma^2\frac{s^2}{2}\,\mathrm ds + \int_0^T\sigma^2(T-s)s\,\mathrm ds\\ &= \sigma^2\left(\frac{T^3}{6}+\frac{T^3}{2}-\frac{T^3}{3}\right)\\ &= \sigma^2\frac{T^3}{3} \end{align}

I hope that the answer above is understandable and acceptable to most readers, but for the benefit of @youpilat13 and any others confused as to how $(2)$ follows from $(1)$, I add the following explanation.

There are two double integrals in $(1)$ and they are over disjoint right triangular regions, separated by the line $s=t$, in the plane. As to why the two integrals are being summed, we are trying to compute the integral over a square region and we are breaking up the integral over the square into the sum of integrals over two disjoint subsets (right triangles), computing each integral separately, and then adding the results together to determine the integral over the square region.

In the inner integral in the first double integral in $(1)$, $s$ is a constant with respect to $t$, the variable of integration, but since $t$ varies from $0$ to $s$ only, it must be that $\min(t,s)$ equals $t$. Similarly, in the inner integral in the second double integral in $(1)$, $s$ is a constant with respect to $t$, the variable of integration, but since $t$ varies from $s$ to $T$ only, it must be that $\min(t,s)$ equals $s$.

Dilip Sarwate
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  • Thanks Dilip Sarwate. I was afraid of going through using a definition with partitions or something. Hahaha. Does Fubini's indeed hold for switching an expectation and a double integral? Well O guess if we can switch for two integrals we can switch for n integrals (n=3). Just haven't seen that before – BCLC Dec 24 '15 at 05:33
  • T^3 not T^2?... – BCLC Dec 26 '15 at 19:35
  • Dilip Sarwate, I think you can [remove the question marks](http://www.math.nyu.edu/faculty/goodman/teaching/StochCalc2004/notes/l6.pdf) – BCLC Dec 28 '15 at 10:02
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    @DilipSarwate . Hello ! I have difficulties to understand the step : $$\begin{aligned} &=\int_{0}^{T} \int_{0}^{T} \sigma^{2} \min (t, s) \mathrm{d} t \mathrm{~d} s \\ &=\int_{0}^{T} \int_{0}^{s} \sigma^{2} t \mathrm{~d} t \mathrm{~d} s+\int_{0}^{T} \int_{s}^{T} \sigma^{2} s \mathrm{~d} t \mathrm{~d} s \end{aligned}$$ . If you could help me to understand the trick ? Regards – youpilat13 Sep 24 '21 at 09:13
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    @youpilat13 The integral is over a square region, and it is being written as the sum of integrals over two disjoint triangular regions separated by the line $s=t$. _Draw a picture_ to understand what's going on. Then, notice that in one region, $\min(t,s) = t$, while in the other region, $\min(t,s) = s$. – Dilip Sarwate Sep 24 '21 at 13:39
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    @DilipSarwate . Thanks but why 2 terms summed : either $\text{min}(t,s)=s$, or $\text{min}(t,s)=t$ but we can't take into account the 2 cases, can we ? In both cases, I have only a triangular region, not a square region ? – youpilat13 Oct 04 '21 at 11:04