Let J be the random process defined by J

_{t}:= X_{t}Y_{t}. At any time, the product of X and Y is J's value. Ito's formula says
dJ := d(Xt Yt) = Xt dY + Yt dX + dX dY

Note this is actually a stoch integral equation. If there's no dW term hidden in dX, then this reduces to an ordinary integral equation. Is this a differential equation? No. There's no differential here.

Note that X and Y are random processes with some diffusion i.e. dW elements.

I used to see it as an equation relating multiple unknowns – dJ, dX, dY, X, Y. Wrong! Instead, it describes how the Next increment in the process J is

**Determined**and precisely predictedIto's formula is a

__predictive__formula, but it's 100% reliable and accurate. Based on info revealed so far, this formula specifies exactly the mean and variance of the next increment dJ. Since dJ is Guassian, the distribution of this rand var is fully described. We can work out the

*probability of dJ falling into any range.*

**precise**Therefore, Ito's formula is the most precise prediction of the next increment. No prediction can be more precise. By construction, all of Xt, Yt, Jt ... are already revealed, and are potential inputs to the predictive formula. If X (and Y) is a well-defined stoch process, then dX (and dY) is predicted in terms of Xt , Yt , dB and dt, such as dX = X

_{t}

^{2}dt + 3Y

_{t}dB

The formula above actually means "Over the next interval dt, the increment in X has a deterministic component (= current revealed value of X squared times dt), and a BM component ~ N(0, variance = 9 Y

Given 1) the dynamics of stoch process(es), 2) how a new process is composed therefrom, Ito's formula lets us work out the deterministic + random components of __next_increment__.

_{t}^{2}dt)"Given 1) the dynamics of stoch process(es), 2) how a new process is composed therefrom, Ito's formula lets us work out the deterministic + random components of __next_increment__.

We have a similarly precise prediction of dY, the next increment in Y. As such, we already know

Xt, Yt -- the Realized values

dt – the interval's length

dX, dY – predicted increments

Therefore dJ can be predicted.

For me, the #1 take-away is in the dX formula, which predicts the next increment using Realized values.

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