In probability theory, a continuous stochastic process is a type of stochastic process that may be said to be "continuous" as a function of its "time" or index parameter. Continuity is a nice property for (the sample paths of) a process to have, since it implies that they are well-behaved in some sense, and, therefore, much easier to analyze. It is implicit here that the index of the stochastic process is a continuous variable. Some authors^[1] define a "continuous (stochastic) process" as only requiring that the index variable be continuous, without continuity of sample paths: in another terminology, this would be a continuous-time stochastic process, in parallel to a "discrete-time process". Given the possible confusion, caution is needed.^[1]

Let (Ω, Σ, P) be a probability space, let T be some interval of time, and let X : T × Ω → S be a stochastic process. For simplicity, the rest of this article will take the state space S to be the real line R, but the definitions go through mutatis mutandis if S is Rⁿ, a normed vector space, or even a general metric space.

Given a time t ∈ T, X is said to be continuous with probability one at t if

${\displaystyle \mathbf {P} \left(\left\{\omega \in \Omega \left|\lim _{s\to t}{\big |}X_{s}(\omega )-X_{t}(\omega ){\big |}=0\right.\right\}\right)=1.}$

Given a time t ∈ T, X is said to be continuous in mean-square at t if E[|X_t|²] < +∞ and

${\displaystyle \lim _{s\to t}\mathbf {E} \left[{\big |}X_{s}-X_{t}{\big |}^{2}\right]=0.}$

Given a time t ∈ T, X is said to be continuous in probability at t if, for all ε > 0,

${\displaystyle \lim _{s\to t}\mathbf {P} \left(\left\{\omega \in \Omega \left|{\big |}X_{s}(\omega )-X_{t}(\omega ){\big |}\geq \varepsilon \right.\right\}\right)=0.}$

Equivalently, X is continuous in probability at time t if

${\displaystyle \lim _{s\to t}\mathbf {E} \left[{\frac {{\big |}X_{s}-X_{t}{\big |}}{1+{\big |}X_{s}-X_{t}{\big |}}}\right]=0.}$

Given a time t ∈ T, X is said to be continuous in distribution at t if

${\displaystyle \lim _{s\to t}F_{s}(x)=F_{t}(x)}$

for all points x at which F_t is continuous, where F_t denotes the cumulative distribution function of the random variable X_t.

X is said to be sample continuous if X_t(ω) is continuous in t for P-almost all ω ∈ Ω. Sample continuity is the appropriate notion of continuity for processes such as Itō diffusions.

X is said to be a Feller-continuous process if, for any fixed t ∈ T and any bounded, continuous and Σ-measurable function g : S → R, E^x[g(X_t)] depends continuously upon x. Here x denotes the initial state of the process X, and E^x denotes expectation conditional upon the event that X starts at x.

The relationships between the various types of continuity of stochastic processes are akin to the relationships between the various types of convergence of random variables. In particular:

• continuity with probability one implies continuity in probability;
• continuity in mean-square implies continuity in probability;
• continuity with probability one neither implies, nor is implied by, continuity in mean-square;
• continuity in probability implies, but is not implied by, continuity in distribution.

It is tempting to confuse continuity with probability one with sample continuity. Continuity with probability one at time t means that P(A_t) = 0, where the event A_t is given by

${\displaystyle A_{t}=\left\{\omega \in \Omega \left|\lim _{s\to t}{\big |}X_{s}(\omega )-X_{t}(\omega ){\big |}\neq 0\right.\right\},}$

and it is perfectly feasible to check whether or not this holds for each t ∈ T. Sample continuity, on the other hand, requires that P(A) = 0, where

${\displaystyle A=\bigcup _{t\in T}A_{t}.}$

A is an uncountable union of events, so it may not actually be an event itself, so P(A) may be undefined! Even worse, even if A is an event, P(A) can be strictly positive even if P(A_t) = 0 for every t ∈ T. This is the case, for example, with the telegraph process.

This article includes a list of general references, but it lacks sufficient corresponding inline citations. Please help to improve this article by introducing more precise citations. (November 2010) (Learn how and when to remove this message)

• Kloeden, Peter E.; Platen, Eckhard (1992). Numerical solution of stochastic differential equations. Applications of Mathematics (New York) 23. Berlin: Springer-Verlag. pp. 38–39. ISBN 3-540-54062-8.
• Øksendal, Bernt K. (2003). Stochastic Differential Equations: An Introduction with Applications (Sixth ed.). Berlin: Springer. ISBN 3-540-04758-1. (See Lemma 8.1.4)