Smoothly runs the Don

Earlier, I wrote something about changing distributions. More recently a couple of examples of this phenomena have come up, so let's make things concrete.

Suppose x(t) is randomly distributed according to N(0,s(t)) [normal distribution with mean zero and standard deviation s(t)] where s(t) is a continuous, bounded and slow function of t. Suppose we sample x(t) discretely. (Take s(t) = 2 + sin(t) with t in years and daily sampling, for instance.)

The variables x(t) are not iid, but (under a bunch of smoothness conditions) they are locally iid: we can think of the distribution being fibred over time and a small change in time inducing a small change in the distribution. One might hope that one could import a lot of non parametric statistics into this setting, where we were trying to gain information about the variation of s(t) by sampling the xs.

Note that this is not a stochastic volatility model or a GARCH model: volatilities are not random, but rather determined by an initially unknown function.

Is this sort of thing well known I wonder? It's similar to local volatility models, but there we have a situation where we can deduce s(t) with certainty as we know the prices of all vanilla options, whereas I'm more interested in a situation where we can only observe s(t) by sampling x(t).

One of the applic- ations, as in this picture of a large piece of metal hanging from a crane a hundred feet above a busy junction, is operational risk, but there are others which may be even more slippery.