Properties of the Euler Discretization of a diffusion

Question

Let $X$ be a continuous 1-d diffusion:

$$ dX_t = a(X_t)dt + b(X_t)dW_t, X_0 = x. $$ W is a standard Brownian Motion and $a(\cdot)$ and $b(\cdot)$ can have nice regularity properties.

Let $Z^n_1,Z^n_2,\ldots$ be i.i.d. standard normal random variables. The Euler discretization $X^n$ with step size $\frac{1}{n}$ is the process given by

$$ X^n_\frac{k}{n} = X^n_\frac{k-1}{n} + \frac{1}{n}\cdot a(X^n_\frac{k-1}{n}) + \frac{1}{\sqrt{n}}b(X^n_\frac{k-1}{n}) \cdot Z^n_k, X^n_0 = x. $$

A (at least to me) reasonable way to embed $X$ and $X^1,X^2,\ldots$ on the same probability space is by taking the $Z_i$ generated by the increments of the Brownian Motion $W$: $Z^n_i = \sqrt{n} \cdot (W_\frac{i}{n} - W_\frac{i-1}{n})$.

The reason why I want all process defined on the same space is that I want convergence rates, and I have heard that weak convergence results aren't appropriate for this, although I don't really understand why.

It's believable that in some sense $X^n \rightarrow X$. There are several different types of convergence available (expectation over product space, expectation of time supremum). I'm interested in what kind of convergence there is, and specifically in the convergence rates.

Motivation: the problem I'm studying is an optimal stopping problem on a diffusion when there is perfect information. When information comes at discrete times, it becomes a weird discretization, which can be approximated by the one above. I'd like to see how fast there is convergence to the perfect information case.

Answer 1

Given your interest in optimal stopping, the relevant result for you is probably strong uniform convergence, by which I mean convergence of the expectation of the time supremum. If your coefficients $a$ and $b$ are Lipschitz, then I believe the result is well-known. The proof is simple with the right "embedding", as you mentioned. Without more information about how stopping times come in to your particular problem, it's hard to know if this is a useful suggestion, but here is the idea anyway:

Suppose there exists $L > 0$ such that $|a(x) - a(y)| + |b(x) - b(y)| \le L|x-y|$. Fix a time horizon $T > 0$. Define $t^n_j = jT/n$ for $j=0,\ldots,n$. For trajectories $x$ and times $t$ define $\lfloor t \rfloor _ n := \max\{t^n_j : t^n_j\le t, \ j=0,\ldots,n\}$ and $a^n(t,x) := a(x(\lfloor t \rfloor _ n))$. Define $b^n$ similarly. Let $X^n$ solve the SDE $dX^n_t = a^n(t,X^n)dt + b^n(t,X^n)dW_t$, $X^n_0 = x$. This is trivially solveable, just by noting that the SDE is actually equivalent to the following recursion:

$X^n(t) = X^n(t^n_{j}) + a(X^n(t^n_{j}))(t - t^n_j) + b(X^n(t^n_{j}))(W(t) - W(t^n_j))$, for $t \in [t^n_j,t^n_{j+1}]$.

This is your desired embedding, and it is straightforward to prove with Doob's and Gronwall's inequalities that

$\mathbb{E}[\sup_{0 \le t \le T}|X(t) - X^n(t)|^2] \rightarrow 0$.

Answer 2

What you call the Euler discretization is sometimes called the Euler-Maruyama discretization. There is a lot of literature about its convergence properties. One place to look is the classic book by Kloeden and Platen (Numerical Solution of Stochastic Differential Equations). Another is Milstein and Tretyakov (Stochastic Numerics for Mathematical Physics). A very convenient and readable introduction is the paper by Des Higham http://epubs.siam.org/doi/pdf/10.1137/S0036144500378302.