Deriving the Brownian Itô Integral of Squared Motion
Summary
The document derives the Itô integral of squared Brownian motion using both partition sums and the Stratonovich integral. The partition method rewrites each increment term using differences of cubes, squared increments, and cubed increments, then invokes their limiting behavior. The alternative route uses the Stratonovich chain rule to obtain the cubic endpoint term, then converts to the Itô convention by accounting for quadratic covariation. It also illustrates the simpler linear integrand case and gives the corresponding Itô lemma relationship.
These methods show why ordinary algebra on partition sums produces correction terms in stochastic integration. The account is pedagogical and assumes familiarity with Brownian motion, quadratic variation, and the definitions of Itô and Stratonovich integrals. It presents no empirical trading evidence or financial application; its value for quant work is foundational stochastic calculus that can support derivations of diffusion models and related pricing formulas.
Key ideas
- Stratonovich sums use the average of the integrand at adjacent partition points.
- Converting a Stratonovich integral to an Itô integral adds a quadratic covariation correction.
- The integral of squared Brownian motion can be related to a cubic endpoint term and an ordinary time integral.
- Partition sum identities expose the squared and cubed increment terms needed for the Itô limit.
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# Calculate Ito integral $\int_0^t W_s^2\text dW_s$ from first principles
# Calculate Ito integral $\int_0^t W_s^2\text dW_s$ from first principles
I am stuck on the 1st equation of the solution where the Wiener process $W_{t_i}^2$ is expanded so that the Itô integral (in terms of infinite sums) looks like the RHS of the first equation of the solution. I can follow the rest of what they did.
If the problem were to solve $\int_0^t W_s\text dW_s$ instead then I think the analogous piece would be $$W_{t_i} = \frac{1}{2}\left(W_{t_{i+1}} + W_{t_i}\right) - \frac{1}{2}\left(W_{t_{i+1}} - W_{t_i}\right).$$
What's the trick to getting this solution started?
## Answer by ir7 (score 6, accepted)
https://quant.stackexchange.com/a/61721
An alternative way is using the Stratonovich integral. By definition, we have
$$\int_0^t X_s \, \circ dW_s = \lim_{n\rightarrow \infty} \sum_{i=1}^n \frac{X_{t_i} +X_{t_{i-1}}}{2}\left( W_{t_i} -W_{t_{i-1}}\right) \; \; (1)$$
One can then show that for a deterministic smooth functions $f$ and $g$ we have:
$$ \int_0^t g'(W_s)\, \circ dW_s = g(W_t)- g(W_0)\; \; (2) $$ and $$\int_0^t f(W_s)\, \circ dW_s =\int_0^t f(W_s) \, dW_s + \frac{1}{2} \int_0^t f'(W_s) \, ds \; \; (3). $$
Using (1), we get:
$$\int_0^t W_s \, \circ dW_s = \lim_{n\rightarrow \infty} \sum_{i=1}^n \frac{W_{t_i} +W_{t_{i-1}}}{2}\left( W_{t_i} -W_{t_{i-1}}\right) $$ $$= \frac{1}{2} \lim_{n\rightarrow \infty} \sum_{i=1}^n \left( W_{t_i}^2 -W^2_{t_{i-1}}\right) = \frac{1}{2} W_t^2 $$
Using (2) with $g(x) = 1/2x^2, g'(x) = x$, we get the same result $$\int_0^t W_s \, \circ dW_s = \frac{1}{2} W_t^2. $$
From (3) with $f(x)=x, f'(x) = 1$, we can now get the Ito integral:
$$\int_0^t f(W_s) \, dW_s = \int_0^t f(W_s)\, \circ dW_s - \frac{1}{2} \int_0^t f'(W_s) \, ds $$ $$ = \frac{1}{2} W_t^2 - \frac{1}{2} t $$
We can repeat the recipe above for calculating:
$$ \int_0^t W_s^2 \, dW_s $$
The convenience of Stratonovich integral definition strikes again:
$$\frac{1}{3}W_t^3 \stackrel{(2)}{=}\int_0^t W_s^2 \, \circ dW_s \stackrel{(1)}{=} \lim_{n\rightarrow \infty} \sum_{i=1}^n \frac{W_{t_i}^2 +W_{t_{i-1}}^2}{2}\left( W_{t_i} -W_{t_{i-1}}\right) $$ $$\stackrel{algebra}{=} \lim_{n\rightarrow \infty} \sum_{i=1}^n W_{t_{i-1}}^2 \left( W_{t_i} - W_{t_{i-1}}\right) + \lim_{n\rightarrow \infty} \sum_{i=1}^n \frac{1}{2} \left( W_{t_i}^2 - W_{t_{i-1}}^2\right) \left( W_{t_i} - W_{t_{i-1}}\right) $$
$$ = \int_0^t W_s^2dW_s + \frac{1}{2} [W^2, W]_t = \int_0^t W_s^2dW_s + \int_0^t W_s ds $$
Note that the algebra needed is much nicer than @Kevin's :):
$$ \frac{1}{2}(a+b)(x-y) = b(x-y)+ \frac{1}{2} (a-b)(x-y) $$
and it is the basis of the fundamental result behind (3):
$$\int_0^t X_s \, \circ dW_s = \int_0^t X_s dW_s + \frac{1}{2} [X,W]_t $$
## Answer by Kevin (score 5)
https://quant.stackexchange.com/a/61717
## Integrating $W_t$
Consider the partition $t_i=it/n$ with $t_0=0$ and $t_n=t$. Then, by definition, \begin{align*} \int_0^t W_s\text{d}W_s &= \lim_{n\to\infty} \sum_{i=0}^{n-1} W_{t_i}\left(W_{t_{i+1}}-W_{t_i}\right). \end{align*}
You can do the limit by using the identity $$ W_{t_i}\left(W_{t_{i+1}}-W_{t_i}\right)=\frac{1}{2}\left(W_{t_{i+1}}^2-W_{t_i}^2-\left(W_{t_{i+1}}-W_{t_i}\right)^2\right).$$
## Integrating $W_t^2$
Using the same partition as before, \begin{align*} \int_0^t W_s^2\text{d}W_s &= \lim_{n\to\infty} \sum_{i=0}^{n-1} W_{t_i}^2\left(W_{t_{i+1}}-W_{t_i}\right). \end{align*} For this case, you can use the identity $$ W_{t_i}^2\left(W_{t_{i+1}}-W_{t_i}\right)=\frac{1}{3}(W_{t_{i+1}}^3-W_{t_i}^3)-W_{t_i}(W_{t_{i+1}}-W_{t_i})^2-\frac{1}{3}(W_{t_{i+1}}-W_{t_i})^3.$$ The rest is as usual.
## Itô's Lemma
You get the same result much quicker if you set $f(t,x)=\frac{1}{3}x^3$. Then, \begin{align} \text df(W_t) = W_t\text{d}t + W_t^2\text dW_t, \end{align} which implies $$\frac{1}{3}W_t^3=\int_0^tW_s\text{d}s+\int_0^tW_s^2\text{d}W_s.$$Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.