Connecting Integral and Differential Forms of Itô’s Lemma
Summary
The answer explains why an integral statement of Itô’s lemma and a differential shorthand can describe the same process. It begins with an Itô process written as a drift integral plus a stochastic integral, then shows how the compact SDE notation summarizes that construction. For a sufficiently smooth function of time and one process, the lemma adds the time derivative, the process derivative, and a second-derivative correction weighted by quadratic variation.
For a diffusion with volatility coefficient, quadratic variation accumulates at the squared volatility rate, linking the correction term to the version expressed with volatility squared and time. The answer emphasizes that differential notation is shorthand, while the integral expression states the mathematical relationship more precisely. It also distinguishes the one-process formula from the two-process formula, which includes each process’s variation and their cross variation. Regularity and integrability assumptions are mentioned, but not developed in detail.
Key ideas
- An Itô process can be represented by drift and stochastic integrals or summarized with SDE notation.
- The differential form of Itô’s lemma is shorthand for an integral relationship.
- Quadratic variation for a diffusion accumulates according to squared volatility over time.
- Functions of two processes require individual quadratic variation terms and a cross variation term.
Tags
Full text
# Link between two Itô's Lemma written in different ways
# Link between two Itô's Lemma written in different ways
I have been told that these two expressions of Itô's Lemma are the same, but written in different ways :
$$ f(t,X_t) = f(0, X_0) + \int_{0}^{t} \frac{\partial f}{\partial s} ds + \int_{0}^{t} \frac{\partial f}{\partial X_s} dX_s + \frac{1}{2}\int_{0}^{t} \frac{\partial^2 f}{\partial {X_s}^2}{\sigma_s}^2 ds$$
and
$$df(X_t, Y_t) = \frac{\partial f}{\partial X_t}dX_t + \frac{\partial f}{\partial Y_t}dY_t + \frac{1}{2} \frac{\partial^2 f}{\partial {X_t}^2}d<X_t>_t + \frac{1}{2} \frac{\partial^2 f}{\partial {Y_t}^2}d<Y>_t + \frac{\partial^2 f}{\partial X_t \partial Y_t}d<X, Y>_t $$
where $<X, Y>_t$ is the quadratic variation operator (and $<X>_t = <X,X>_t$).
I cannot figure out why they are similar. In my eyes, they seem pretty different. How can I pass from the first one to the second one? Actually I don't really understand how the second one can be interpreted and applied.
## Answer by bcf (score 3, accepted)
https://quant.stackexchange.com/a/22049
I assume you're confused between the integral and SDE writings of Ito's lemma, since the two equations you have are indeed different.
Let $X_t$ be an Ito process defined by $$ X_t = X_0 + \int_0^t \alpha_s \, ds + \int_0^t \sigma_s \, dW_s $$ for adapted processes $\alpha_s$ and $\sigma_s$ (and assuming some technical boundedness condition on the integrals). This equation may be written in shorthand as an SDE as $$ dX_t = \alpha_t dt + \sigma_t dW_t. $$ The SDE is not rigorous - it is simply a shortcut way of writing down the integrals above, and provides a bit of intuition behind the evolution of $X$ over "infinitesimally small" time intervals.
Now consider a measurable function $f: [0,T] \times \mathbb{R} \to \mathbb{R}$ such that $$ f(\cdot,x)\in C^1([0,T]) \quad \forall x \in \mathbb{R}, $$ and $$ f(t,\cdot) \in C^2(\mathbb{R}) \quad \forall t \in [0,T]. $$
I would argue the correct (mathematically rigorous) way of stating Ito's lemma is
> $$ f(T,X_T) = f(0,X_0) + \int_0^T \frac{\partial}{\partial t}f(s,X_s) \, ds + \int_0^T \frac{\partial}{\partial x}f(s,X_s) \, dX_s \\ \qquad + \frac{1}{2}\int_0^T \frac{\partial^2}{\partial x^2}f(s,X_s) \, d<X,X>_s $$
The quantity $<X,X>_s$ is the quadratic variation accumulated by the Ito process $X$ up until time $s$. You can show (Shreve II, page 143-144, e.g.) that this is given by $$ <X,X>_s = \int_0^s \sigma^2_u \, du, $$ or, in differential (shorthand) form as $$ d<X,X>_s = \sigma^2_s \, ds. $$ Plugging this into Ito's lemma gives your first equation.
Now, just like the Ito process $X$ was written in shorthand as an SDE, so may $f$, since it, too, is an Ito process. That is, we also have
> $$ df(t,X_t) = \frac{\partial}{\partial t}f(t,X_t)dt + \frac{\partial}{\partial x}f(t,X_t) dX_t + \frac{1}{2}\frac{\partial^2}{\partial x^2}f(t,X_t) d<X,X>_t. $$
The first boxed equation had precise mathematical meaning. The second boxed equation is just shorthand for the first.
Update: Your second equation is often called Ito's product rule. Ito's lemma is "usually" stated for functions of one Ito process as it was for my answer above. If you have a function of two Ito processes then both processes' quadratic variation and cross variation appear in Ito's lemma, aka Ito's product rule. See Shreve II, page 168, e.g. for a decent explanationShown 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.