P-variation

In mathematical analysis, p-variation is a collection of seminorms on functions from an ordered set to a metric space, indexed by a real number . p-variation is a measure of the regularity or smoothness of a function. Specifically, if , where is a metric space and I a totally ordered set, its p-variation is

where D ranges over all finite partitions of the interval I.

The p variation of a function decreases with p. If f has finite p-variation and g is an α-Hölder continuous function, then has finite -variation.

The case when p is one is called total variation, and functions with a finite 1-variation are called bounded variation functions.

One can interpret the p-variation as a parameter-independent version of the Hölder norm, which also extends to discontinuous functions. If f is αHölder continuous (i.e. its αHölder norm is finite) then its -variation is finite. Specifically, on an interval [a,b], . Conversely, if f is continuous and has finite p-variation, there exists a reparameterisation, , such that is Hölder continuous.

If p is less than q then the space of functions of finite p-variation on a compact set is continuously embedded with norm 1 into those of finite q-variation. I.e. . However unlike the analogous situation with Hölder spaces the embedding is not compact. For example, consider the real functions on [0,1] given by . They are uniformly bounded in 1-variation and converge pointwise to a discontinuous function f but this not only is not a convergence in p-variation for any p but also is not uniform convergence.

Application to Riemann–Stieltjes integration

If f and g are functions from  [a, b] to ℝ with no common discontinuities and with f having finite p-variation and g having finite q-variation, with then the Riemann–Stieltjes Integral

is well-defined. This integral is known as the Young integral because it comes from Young (1936).[1] The value of this definite integral is bounded by the Young-Loève estimate as follows

where C is a constant which only depends on p and q and ξ is any number between a and b.[2] If f and g are continuous, the indefinite integral is a continuous function with finite q-variation: If astb then , its q-variation on [s,t], is bounded by where C is a constant which only depends on p and q.[3]

Young differential equations

A function from ℝd to e × d real matrices is called an ℝe-valued one-form on ℝd.

If f is a Lipschitz continuous ℝe-valued one-form on ℝd, and X is a continuous function from the interval [a, b] to ℝd with finite p-variation with p less than 2, then the integral of f on X, , can be calculated because each component of f(X(t)) will be a path of finite p-variation and the integral is a sum of finitely many Young integrals. It provides the solution to the equation driven by the path X.

More significantly, if f is a Lipschitz continuous ℝe-valued one-form on ℝe, and X is a continuous function from the interval [a, b] to ℝd with finite p-variation with p less than 2, then Young integration is enough to establish the solution of the equation driven by the path X.[4]

Rough differential equations

The theory of rough paths generalises the Young integral and Young differential equations and makes heavy use of the concept of p-variation.

For Brownian motion

p-variation should be contrasted with the quadratic variation which is used in stochastic analysis, where it takes one stochastic process to another. Quadratic variation is defined as a limit as the partition gets finer, whereas p-variation is a supremum over all partitions. Thus the quadratic variation of a process could be smaller than its 2-variation. If Wt is a standard Brownian motion on [0, T] then with probability one its p-variation is infinite for and finite otherwise. The quadratic variation of W is .

Computation of p-variation for discrete time series

For a discrete time series of observations X0,...,XN it is straightforward to compute its p-variation with complexity of O(N2). Here is an example C++ code using dynamic programming:

double p_var(const std::vector<double>& X, double p) {
	if (X.size() == 0)
		return 0.0;
	std::vector<double> cum_p_var(X.size(), 0.0);   // cumulative p-variation
	for (size_t n = 1; n < X.size(); n++) {
		for (size_t k = 0; k < n; k++) {
			cum_p_var[n] = std::max(cum_p_var[n], cum_p_var[k] + std::pow(std::abs(X[n] - X[k]), p));
		}
	}
	return std::pow(cum_p_var.back(), 1./p);
}

There exist much more efficient, but also more complicated, algorithms for ℝ-valued processes[5] [6] and for processes in arbitrary metric spaces.[6]

References

  1. https://fabricebaudoin.wordpress.com/2012/12/25/lecture-7-youngs-integral/
  2. Friz, Peter K.; Victoir, Nicolas (2010). Multidimensional Stochastic Processes as Rough Paths: Theory and Applications (Cambridge Studies in Advanced Mathematics ed.). Cambridge University Press.
  3. Lyons, Terry; Caruana, Michael; Levy, Thierry (2007). Differential equations driven by rough paths, vol. 1908 of Lecture Notes in Mathematics. Springer.
  4. https://fabricebaudoin.wordpress.com/2012/12/26/lecture-8-youngs-differential-equations/
  5. Butkus, V.; Norvaiša, R. (2018). "Computation of p-variation". Lithuanian Mathematical Journal. doi:10.1007/s10986-018-9414-3.
  6. https://github.com/khumarahn/p-var
  • Young, L.C. (1936), "An inequality of the Hölder type, connected with Stieltjes integration", Acta Mathematica, 67 (1): 251–282, doi:10.1007/bf02401743.
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