# Approximation of partial derivative of a function of stochastic variable

Let $X_t$ be an Ito process $$dX_t=a(X_t,t)dt + b(X_t,t)dW_t$$ where $W_t$ is a Wiener process.

A numerical approximations of the solution of this equations is proposed by Milstein:

$$X_T=X_t+a(X_t,t) \Delta t+ b(X_t,t)\Delta W_t+ \frac{1}{2}b(X_t,t) \frac{\partial{b(X_t,t)} }{\partial{x}} \left( \Delta W_t^2 - \Delta t\right)$$

where

$\Delta t = T-t$

$\Delta W_t = W_T-W_t$

According with literature, this can be transformed into a derivative-free scheme via the approximation (known as Platen explicit order 1 strong scheme): $$b(X_t,t) \frac{\partial{b(X_t,t)} }{\partial{x}} \approx \frac{b(X_t+a(X_t,t) \Delta t+ b(X_t,t)\sqrt{\Delta t},t)-b(X_t,t)}{\sqrt{\Delta t}}$$

Can anybody help understand how this approximation of the partial derivative is obtained?

Thanks

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## migrated from math.stackexchange.comJan 23 '13 at 1:11

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Using the deterministic Taylor expansion it is easy to show that the ratio $$\frac{1}{\sqrt\Delta}\left\{b(\tau_n,Y_n + a\Delta + b\sqrt\Delta) - b(\tau_n,Y_n)\right\}$$ is a forward difference approximation for $b\frac{\partial{b}}{\partial{x}}$ at $(\tau_n,Y_n)$ if we neglect higher order terms.
A good way to intuit this is that the "average size of a Wiener process step $dW$" is $\sqrt{\Delta t}$. This is because the variance is $\Delta t$. So if you think about the time scale for anything associated with $b$ as $\sqrt{\Delta t}$ the formula follows.