Càdlàg
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In mathematics, a càdlàg function (from the French "continue à droite, limitée à gauche") is a function defined on the real numbers (or a subset of them) that is everywhere right-continuous and has left limits everywhere. Càdlàg functions are important in the study of stochastic processes that admit (or even require) jumps, unlike Brownian motion, which has continuous sample paths. The collection of càdlàg functions on a given domain is known as Skorokhod space.
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[edit] Definition
Let (M,d) be a metric space, and let . A function is called a càdlàg function if, for every ,
- the left limit exists; and
- the right limit exists and equals f(t).
That is, f is right-continuous with left limits (in French: continue à droite, limite à gauche, hence càdlàg — the English equivalent, corlol for "continuous on (the) right, limit on (the) left" is somewhat less common). However RCLL (right continuous with left limits) is used in some English texts.
[edit] Examples
- All continuous functions are càdlàg functions.
- As a consequence of their definition, all cumulative distribution functions are càdlàg functions.
[edit] Skorokhod space
The set of all càdlàg functions from E to M is often denoted by D(E;M) (or simply D) and is called Skorokhod space after the Ukrainian mathematician Anatoliy Skorokhod. Skorokhod space can be assigned a topology that, intuitively allows us to "wiggle space and time a bit" (whereas the traditional topology of uniform convergence only allows us to "wiggle space a bit"). For simplicity, take E = [0,T] and — see Billingsley for a more general construction.
We must first define an analogue of the modulus of continuity, . For any , set
and, for δ > 0, define the càdlàg modulus to be
where the infimum runs over all partitions , , with mini(ti − ti − 1) > δ. This definition makes sense for non-càdlàg f (just as the usual modulus of continuity makes sense for discontinuous functions) and it can be shown that f is càdlàg if and only if as .
Now let Λ denote the set of all strictly increasing, continuous bijections from E to itself (these are "wiggles in time"). Let
denote the uniform norm on functions on E. Define the Skorokhod metric σ on D by
- ,
where is the identity function. In terms of the "wiggle" intuition, measures the size of the "wiggle in time", and measures the size of the "wiggle in space".
It can be shown that the Skorokhod metric is, indeed a metric. The topology Σ generated by σ is called the Skorokhod topology on D.
[edit] Properties of Skorokhod space
[edit] Generalization of the uniform topology
The space C of continuous functions on E is a subspace of D. The Skorokhod topology on D coincides with the topology of uniform convergence on C.
[edit] Completeness
It can be shown that, although D is not a complete space with respect to the Skorokhod metric σ, there is a topologically equivalent metric σ0 with respect to which D is complete.
[edit] Separability
With respect to either σ or σ0, D is a separable space. Thus, Skorokhod space is a Polish space.
[edit] Tightness in Skorokhod space
By an application of the Arzelà-Ascoli theorem, one can show that a sequence of probability measures on Skorokhod space D is tight if and only if both the following conditions are met:
and
[edit] References
- Billingsley, Patrick (1995). Probability and Measure. New York, NY: John Wiley & Sons, Inc.. ISBN 0-471-00710-2.