P-variation

One can interpret the p-variation as a parameter-independent version of the Hölder norm, which also extends to discontinuous functions. If f is α–Hölder continuous (i.e. its α–Hölder norm is finite) then its ${\displaystyle {\frac {1}{\alpha }}}$-variation is finite. Specifically, on an interval [a,b], ${\displaystyle \|f\|_{{\frac {1}{\alpha }}{\text{-var}}}\leq \|f\|_{\alpha }(b-a)^{\alpha }}$. Conversely, if f is continuous and has finite p-variation, there exists a reparameterisation, ${\displaystyle \tau }$, such that ${\displaystyle f\circ \tau }$ is ${\displaystyle 1/p-}$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. ${\displaystyle \|f\|_{q{\text{-var}}}\leq \|f\|_{p{\text{-var}}}}$. However unlike the analogous situation with Hölder spaces the embedding is not compact. For example, consider the real functions on [0,1] given by ${\displaystyle f_{n}(x)=x^{n}}$. 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.

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 ${\displaystyle {\frac {1}{p}}+{\frac {1}{q}}>1}$ then the Riemann–Stieltjes Integral

${\displaystyle \int _{a}^{b}f(x)\,dg(x):=\lim _{|D|\to 0}\sum _{t_{k}\in D}f(t_{k})[g(t_{k+1})-g({t_{k}})]}$

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

${\displaystyle \left|\int _{a}^{b}f(x)\,dg(x)-f(\xi )[g(b)-g(a)]\right|\leq C\,\|f\|_{p{\text{-var}}}\|\,g\|_{q{\text{-var}}}}$

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 ${\displaystyle F(w)=\int _{a}^{w}f(x)\,dg(x)}$ is a continuous function with finite q-variation: If a ≤ s ≤ t ≤ b then ${\displaystyle \|F\|_{q{\text{-var}};[s,t]}}$, its q-variation on [s,t], is bounded by ${\displaystyle C\|g\|_{q{\text{-var}};[s,t]}(\|f\|_{p{\text{-var}};[s,t]}+\|f\|_{\infty$ ;[s,t]})\leq 2C\|g\|_{q{\text{-var}};[s,t]}(\|f\|_{p{\text{-var}};[a,b]}+f(a))} where C is a constant which only depends on p and q.[3]

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

If f is a Lipschitz continuous ℝ^e-valued one-form on ℝ^d, and X is a continuous function from the interval [a, b] to ℝ^d with finite p-variation with p less than 2, then the integral of f on X, ${\displaystyle \int _{a}^{b}f(X(t))\,dX(t)}$, 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 ${\displaystyle dY=f(X)\,dX}$ driven by the path X.

More significantly, if f is a Lipschitz continuous ℝ^e-valued one-form on ℝ^e, and X is a continuous function from the interval [a, b] to ℝ^d with finite p-variation with p less than 2, then Young integration is enough to establish the solution of the equation ${\displaystyle dY=f(Y)\,dX}$ driven by the path X.[4]

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

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 W_t is a standard Brownian motion on [0, T] then with probability one its p-variation is infinite for ${\displaystyle p\leq 2}$ and finite otherwise. The quadratic variation of W is ${\displaystyle [W]_{T}=T}$.

For a discrete time series of observations X₀,...,X_N 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].

• Young, L.C. (1936), "An inequality of the Hölder type, connected with Stieltjes integration", Acta Mathematica, 67 (1): 251–282, doi:10.1007/bf02401743.

• Continuous Paths with bounded p-variation Fabrice Baudoin
• On the Young integral, truncated variation and rough paths Rafał M. Łochowski