# Ito’s Formula: a heuristic derivation

• A heuristic look at the stochastic integral.
• heuristic derivation of Itô’s formula.

What follows is a heuristic proof of Itô’s Formula, which states that if and if $f:{\mathbb R} \rightarrow {\mathbb R}$ is a twice continuously differentiable function then (Rigorous proofs of these exercises are not expected.)

Ex 1 [A Heuristic look at Stochastic Integration] For $(B_t: \geq 0)$ a standard Brownian motion argue that, for all $T$ and for $\delta$ sufficiently small and positive, Ex 2 [Continued] Discuss why it is reasonable to expect that and Ex 3 [Continued] If we inductively define $X_t$ by the recursion then discuss why we expect $X_t$ to approximately obey an equation of the form Ex 4[Continued]Let $f$ be a twice differentiable function, argue that Ex 5 [Continued]Argue that This is Itô’s formula.

Ans 1 The 1st sum is an interpolating sum. By independent increments property of Brownian motion, the 2nd sum adds IIDRVs with each with mean $\delta$. Thus the strong law of large numbers gives the approximation.

Ans 2 The first sum is approximation from a Riemann-Stieltjes integral, i.e. So one might expect a integral limit. (This is unrigorous because Riemann-Stieltjes Integration only applies to functions with finite variation – while Brownian motion does not have finite variation.)

The second sum is a Riemann integral upon using the approximation $\left(B_{t+\delta} - B_t\right)^2\approx \delta$ .

Ans 3  Sum to gain $X_T-X_0$ and apply approximations from .

Ans 4 Apply a Taylor approximation In the last equality we use that $(B_{t+\delta}-B_t)=o(\delta^{1/2})$, cf. .

Ans 5 Apply an interpolating sum to  and then apply .