Infinitesimal generator (stochastic processes)

In mathematics — specifically, in stochastic analysis — the infinitesimal generator of a stochastic process is a partial differential operator that encodes a great deal of information about the process. The generator is used in evolution equations such as the Kolmogorov backward equation (which describes the evolution of statistics of the process); its L2 Hermitian adjoint is used in evolution equations such as the Fokker-Planck equation (which describes the evolution of the probability density functions of the process).


Let X : [0, +∞) × Ω → Rn defined on a probability space (Ω, Σ, P) be an Itō diffusion satisfying a stochastic differential equation of the form

mathrm{d} X_{t} = b(X_{t}) , mathrm{d} t + sigma (X_{t}) , mathrm{d} B_{t},

where B is an m-dimensional Brownian motion and b : Rn → Rn and σ : Rn → Rn×m are the drift and diffusion fields respectively. For a point x ∈ Rn, let Px denote the law of X given initial datum X0 = x, and let Ex denote expectation with respect to Px.

The infinitesimal generator of X is the operator A, which is defined to act on suitable functions f : Rn → R by

A f (x) = lim_{t downarrow 0} frac{mathbf{E}^{x} [f(X_{t})] - f(x)}{t}.

The set of all functions f for which this limit exists at a point x is denoted DA(x), while DA denotes the set of all f for which the limit exists for all x ∈ Rn. One can show that any compactly-supported C2 (twice differentiable with continuous second derivative) function f lies in DA and that

A f (x) = sum_{i} b_{i} (x) frac{partial f}{partial x_{i}} (x) + frac1{2} sum_{i, j} big(sigma (x) sigma (x)^{top} big)_{i, j} frac{partial^{2} f}{partial x_{i} , partial x_{j}} (x),

or, in terms of the gradient and scalar and Frobenius inner products,

A f (x) = b(x) cdot nabla_{x} f(x) + frac1{2} big(sigma(x) sigma(x)^{top} big) : nabla_{x} nabla_{x} f(x).

Generators of some common processes

  • Standard Brownian motion on Rn, which satisfies the stochastic differential equation dXt = dBt, has generator ½Δ, where Δ denotes the Laplace operator.
  • The two-dimensional process Y satisfying

mathrm{d} Y_{t} = { mathrm{d} t choose mathrm{d} B_{t} } ,

where B is a one-dimensional Brownian motion, can be thought of as the graph of that Brownian motion, and has generator

A f(t, x) = frac{partial f}{partial t} (t, x) + frac1{2} frac{partial^{2} f}{partial x^{2}} (t, x).

  • The Ornstein-Uhlenbeck process on R, which satisfies the stochastic differential equation dXt = μXt dt + σ dBt, has generator

A f(x) = mu x f'(x) + frac{sigma^{2}}{2} f(x).

  • Similarly, the graph of the Ornstein-Uhlenbeck process has generator

A f(t, x) = frac{partial f}{partial t} (t, x) + mu x frac{partial f}{partial x} (t, x) + frac{sigma^{2}}{2} frac{partial^{2} f}{partial x^{2}} (t, x).

  • A geometric Brownian motion on R, which satisfies the stochastic differential equation dXt = rXt dt + αXt dBt, has generator

A f(x) = r x f'(x) + frac1{2} alpha^{2} x^{2} f(x).

See also


  • Øksendal, Bernt K. (2003). Stochastic Differential Equations: An Introduction with Applications. Sixth edition, Berlin: Springer. ISBN 3-540-04758-1. (See Section 7)
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