The following is a summary of a chapter from Billingsley’s *Convergence of Probability Measures*. The ideas are easy to explain heuristically, but this was the first text I could find which explained how to construct Skorohod space for functions on the whole of the non-negative reals in enough stages that it was easily digestible.

It is relatively straightforward to define a topology on C[0,1], as we can induce from the most sensible metric. In this topology, functions f and g are close together if

is small.

For cadlag functions, things are a bit more complicated. Two functions might be very similar, but have a discontinuity of similar magnitude at slightly different places. The sup norm of the difference is therefore macroscopically large. So we want a metric that also allows uniformly small deformations of the time scale.

We define the Skorohod (or Skorokhod depending on your transliteration preferences) metric d on D[0,1] as follows. Let be the family of continuous, strictly increasing functions from [0,1] to [0,1] which map 0 to 0 and 1 to 1. This will be our family of suitable reparameterisations of the time scale (or abscissa – a new word I learned today. The other axis in a co-ordinate pair is called the ordinate). Anyway, we now say that

In other words, after reparameterising the time scale for g, without moving any time by more than epsilon, the functions are within epsilon in the sup metric.

**Weak Convergence**

We have the condition: if is a tight sequence of probability measures and we have

where is the projection onto a finite-dimensional set. This is a suitable condition for C[0,1]. For D[0,1], we have the additional complication that these projections might not be continuous everywhere.

We can get over this problem. For a measure P, set to be the set of such that is continuous P-almost everywhere (ie for all apart from a collection with P-measure = 0). Then, for all P, it is not hard to check that and is countable.

The tightness condition requires two properties:

1)

2)

These say, respectively, that the measure of doesn’t escape to , and there is no mass given in the limit to functions which ‘wiggle with infinite frequency on an epsilon scale of amplitude’.

Our earlier definition of the Skorohod metric could have been written:

From a topological convergence point of view, there’s no need to use the sup norm on . We want to regulate smoothness of the reparameterisation, so we could use the norm:

that is, the slope is uniformly close to 1 if is small. The advantage of this choice of norm is that an extension to is immediate. Also, the induced product norm

is complete. This gives us a few problems, as for example

as you can’t reparameterise over the jump in a way that ensures the log of the gradient is relatively small. (In particular, to keep the sup norm less than 1, we would need to send , and so by definition.)

So we can’t immediately define Skorohod convergence on by demanding convergence on any restriction to [0,t]. We overcome this in a similar way to convergence of distribution functions.

**Lemma: **If then for any s<t with f cts at s, then .

So this says that the functions converge in Skorohod space if for arbitrarily large times T where the limit function is continuous, the restrictions to [0,T] converge. (Note that cadlag functions have at most countably many discontinuities, so this is fine.)

**A metric for **

If we want to specify an actual metric , the usual tools for specifying a countable product metric will do here:

where is the restriction of f to [0,m], with the potential discontinuity at m smoothed out:

In particular, .

It can be checked that:

**Theorem: ** in if and only iff

and that for every point of continuity t of f.

Similarly weak convergence and tightness properties are available, roughly as you might expect. It is probably better to reference Billingsley’s book or similar sources rather than further attempting to summarise them here.