Properties of the Itô Integral

Fix an adapted square-integrable integrand H and let the upper limit run, turning the Itô integral into a process:

M_t = \int_0^t H_s\, dW_s, \qquad 0 \le t \le T.

This object is the workhorse of mathematical finance — a self-financing trading gain, a risk-neutral price increment — and it inherits a beautiful list of properties from the construction. The headline is that M_t is a continuous martingale: a fair bet on a fair game stays fair. We collect the properties, then prove the martingale one in full.

The martingale property, line by line

We show \mathbb{E}[M_t \mid \mathcal{F}_s] = M_s for all s \le t. The whole proof rests on one structural fact: the Itô integral, like an ordinary integral, is additive over adjacent intervals.

Step 1 — split the integral at the present time s. The integral from 0 to t is the integral up to s plus the integral over the remaining slice (s, t]:

M_t = \int_0^t H\, dW = \int_0^s H\, dW + \int_s^t H\, dW = M_s + \int_s^t H\, dW.

Step 2 — take the conditional expectation and use linearity. Condition on everything known at s and split the two pieces:

\mathbb{E}[M_t \mid \mathcal{F}_s] = \mathbb{E}[M_s \mid \mathcal{F}_s] + \mathbb{E}\!\left[\int_s^t H\, dW \;\middle|\; \mathcal{F}_s\right].

Step 3 — the past term is known. The running integral M_s is \mathcal{F}_s-measurable — it is built only from increments up to time s — so it passes through the conditioning untouched:

\mathbb{E}[M_s \mid \mathcal{F}_s] = M_s.

Step 4 — the future slice has conditional mean zero. The increment \int_s^t H\, dW is itself an Itô integral, built from left-endpoint positions H_{t_i} (each known at t_i \ge s) times future increments that are independent of \mathcal{F}_s and mean-zero. The very argument that gave the integral mean zero, applied conditionally on \mathcal{F}_s, gives

\mathbb{E}\!\left[\int_s^t H\, dW \;\middle|\; \mathcal{F}_s\right] = 0.

Step 5 — collect. Adding Steps 3 and 4,

\mathbb{E}[M_t \mid \mathcal{F}_s] = M_s + 0 = M_s.

So M_t = \int_0^t H\, dW is a martingale: the best forecast of any future value of the integral, given the present, is its current value. "Martingale in, martingale out" — integrating an adapted bet against Brownian motion never creates drift.

Let H be adapted with \mathbb{E}\big[\int_0^T H_s^2\, ds\big] < \infty, and set M_t = \int_0^t H_s\, dW_s. Then:

The martingale M_t has zero mean, so all of its "size" is variance — and the quadratic variation records exactly where that variance comes from along the path. For the Itô integral it is

[M]_t = \int_0^t H_s^2\, ds.

Read it as a rate: over an instant dt the martingale accumulates squared fluctuation d[M]_t = H_t^2\, dt — the local "energy" injected by the noise, scaled by how aggressively the strategy H_t is positioned right then. (The differential shorthand is (H\, dW)^2 = H^2 (dW)^2 = H^2\, dt, the (dW)^2 = dt rule once more.) Taking the expectation recovers the isometry, \mathbb{E}[M_t^2] = \mathbb{E}[\int_0^t H^2\, ds] = \mathbb{E}[[M]_t] — so the quadratic variation is the pathwise refinement of the variance.

A caveat — local martingales. Everything above assumed the L^2 condition \mathbb{E}[\int_0^T H^2\, ds] < \infty. When H is only locally square-integrable (the integral of H^2 is finite up to a sequence of stopping times growing to T, but its expectation may blow up), the construction still works and M_t is a local martingale — a martingale "up to a localising sequence". A local martingale need not have constant mean, so the genuine martingale property (and the clean \mathbb{E}[M_t] = 0) can fail without the integrability guard. This is a real subtlety in pricing: a strategy that is only a local martingale can manufacture an apparent free lunch, which integrability conditions exist to rule out.

One path of the integral

Below is a sample path of M_t = \int_0^t H_s\, dW_s for a simple deterministic integrand H, built as the running left-endpoint sum \sum_{t_i \le t} H_{t_i}\,\Delta W_i. Notice it is continuous and hovers around the dashed zero line — the fair-game level — never developing a drift, however far it wanders. Refresh to draw a fresh path.