# Lévy Process as Combination of a Brownian Motion with Drift and Infinite Sum of Independent Compound Poisson Processes: Introduction to Martingales. Part 4.

Every piecewise constant Lévy process Xt0 can be represented in the form for some Poisson random measure with intensity measure of the form ν(dx)dt where ν is a finite measure, defined by

ν(A) = E[#{t ∈ [0,1] : ∆Xt0 ≠ 0, ∆Xt0 ∈ A}], A ∈ B(Rd) —– (1)

Given a Brownian motion with drift γt + Wt, independent from X0, the sum Xt = Xt0 + γt + Wt defines another Lévy process, which can be decomposed as:

Xt = γt + Wt + ∑s∈[0,t] ΔXs = γt + Wt + ∫[0,t]xRd xJX (ds x dx) —– (2)

where JX is a Poisson random measure on [0,∞[×Rd with intensity ν(dx)dt.

Can every Lévy process be represented in this form? Given a Lévy process Xt, we can still define its Lévy measure ν as above. ν(A) is still finite for any compact set A such that 0 ∉ A: if this were not true, the process would have an infinite number of jumps of finite size on [0, T], which contradicts the cadlag property. So ν defines a Radon measure on Rd \ {0}. But ν is not necessarily a finite measure: the above restriction still allows it to blow up at zero and X may have an infinite number of small jumps on [0, T]. In this case the sum of the jumps becomes an infinite series and its convergence imposes some conditions on the measure ν, under which we obtain a decomposition of X.

Let (Xt)t≥0 be a Lévy process on Rd and ν its Lévy measure.

ν is a Radon measure on Rd \ {0} and verifies:

|x|≤1 |x|2 v(dx) < ∞

The jump measure of X, denoted by JX, is a Poisson random measure on [0,∞[×Rd with intensity measure ν(dx)dt.

∃ a vector γ and a d-dimensional Brownian motion (Bt)t≥0 with covariance matrix A such that

Xt = γt + Bt + Xtl + limε↓0 X’εt —– (3)

where

Xtl = ∫|x|≥1,s∈[0,t] xJX (ds x dx)

X’εt = ∫ε≤|x|<1,s∈[0,t] x{JX (ds x dx) – ν(dx)ds}

≡ ∫ε≤|x|<1,s∈[0,t] xJ’X (ds x dx)

The terms in (3) are independent and the convergence in the last term is almost sure and uniform in t on [0,T].

The Lévy-Itô decomposition entails that for every Lévy process ∃ a vector γ, a positive definite matrix A and a positive measure ν that uniquely determine its distribution. The triplet (A,ν,γ) is called characteristic tripletor Lévy triplet of the process Xt. γt + Bt is a continuous Gaussian Lévy process and every Gaussian Lévy process is continuous and can be written in this form and can be described by two parameters: the drift γ and the covariance matrix of Brownian motion, denoted by A. The other two terms are discontinuous processes incorporating the jumps of Xt and are described by the Lévy measure ν. The condition ∫|y|≥1 ν(dy) < ∞ means that X has a finite number of jumps with absolute value larger than 1. So the sum

Xtl = ∑|∆Xs|≥10≤s≤t ∆Xs

contains almost surely a finite number of terms and Xtl is a compound Poisson process. There is nothing special about the threshold ∆X = 1: for any ε > 0, the sum of jumps with amplitude between ε and 1:

Xεt = ∑1>|∆Xs|≥ε0≤s≤t ∆Xs = ∫ε≤|x|≤1,s∈[0,t] xJX(ds x dx) —– (4)

is again a well-defined compound Poisson process. However, contrarily to the compound Poisson case, ν can have a singularity at zero: there can be infinitely many small jumps and their sum does not necessarily converge. This prevents us from making ε go to 0 directly in (4). In order to obtain convergence we have to center the remainder term, i.e., replace the jump integral by its compensated version,

X’εt = ∫ε≤|x|≤1,s∈[0,t] xJ’X (ds x dx) —– (5)

which, is a martingale. While Xε can be interpreted as an infinite superposition of independent Poisson processes, X’εshould be seen as an infinite superposition of independent compensated, i.e., centered Poisson processes to which a central-limit type argument can be applied to show convergence. An important implication of the Lévy-Itô decomposition is that every Lévy process is a combination of a Brownian motion with drift and a possibly infinite sum of independent compound Poisson processes. This also means that every Lévy process can be approximated with arbitrary precision by a jump-diffusion process, that is by the sum of Brownian motion with drift and a compound Poisson process.

# Sobolev Spaces

For any integer n ≥ 0, the Sobolev space Hn(R) is defined to be the set of functions f which are square-integrable together with all their derivatives of order up to n:

f ∈ Hn(R) ⇐⇒ ∫-∞ [f2 + ∑k=1n (dkf/dxk)2 dx ≤ ∞

This is a linear space, and in fact a Hilbert space with norm given by:

∥f∥Hn = ∫-∞ [f2 + ∑k=1n (dkf/dxk)2) dx]1/2

It is a standard fact that this norm of f can be expressed in terms of the Fourier transform fˆ (appropriately normalized) of f by:

∥f∥2Hn = ∫-∞ [(1 + y2)n |fˆ(y)|2 dy

The advantage of that new definition is that it can be extended to non-integral and non-positive values. For any real number s, not necessarily an integer nor positive, we define the Sobolev space Hs(R) to be the Hilbert space of functions associated with the following norm:

∥f∥2Hs = ∫-∞ [(1 + y2)s |fˆ(y)|2 dy —– (1)

Clearly, H0(R) = L2(R) and Hs(R) ⊂ Hs′(R) for s ≥ s′ and in particular Hs(R) ⊂ L2(R) ⊂ H−s(R), for s ≥ 0. Hs(R) is, for general s ∈ R, a space of (tempered) distributions. For example δ(k), the k-th derivative of a delta Dirac distribution, is in H−k−1/2</sup−ε(R) for ε > 0.

In the case when s > 1/2, there are two classical results.

Continuity of Multiplicity:

If s > 1/2, if f and g belong to Hs(R), then fg belongs to Hs(R), and the map (f,g) → fg from Hs × Hs to Hs is continuous.

Denote by Cbn(R) the space of n times continuously differentiable real-valued functions which are bounded together with all their n first derivatives. Let Cnb0(R) be the closed subspace of Cbn(R) of functions which converges to 0 at ±∞ together with all their n first derivatives. These are Banach spaces for the norm:

∥f∥Cbn = max0≤k≤n supx |f(k)(x)| = max0≤k≤n ∥f(k)∥ C0b

Sobolev embedding:

If s > n + 1/2 and if f ∈ Hs(R), then there is a function g in Cnb0(R) which is equal to f almost everywhere. In addition, there is a constant cs, depending only on s, such that:

∥g∥Cbn ≤ c∥f∥Hs

From now on we shall always take the continuous representative of any function in Hs(R). As a consequence of the Sobolev embedding theorem, if s > 1/2, then any function f in Hs(R) is continuous and bounded on the real line and converges to zero at ±∞, so that its value is defined everywhere.

We define, for s ∈ R, a continuous bilinear form on H−s(R) × Hs(R) by:

〈f, g〉= ∫-∞ (fˆ(y))’ gˆ(y)dy —– (2)

where z’ is the complex conjugate of z. Schwarz inequality and (1) give that

|< f , g >| ≤ ∥f∥H−s∥g∥Hs —– (3)

which indeed shows that the bilinear form in (2) is continuous. We note that formally the bilinear form (2) can be written as

〈f, g〉= ∫-∞ f(x) g(x) dx

where, if s ≥ 0, f is in a space of distributions H−s(R) and g is in a space of “test functions” Hs(R).

Any continuous linear form g → u(g) on Hs(R) is, due to (1), of the form u(g) = 〈f, g〉 for some f ∈ H−s(R), with ∥f∥H−s = ∥u∥(Hs)′, so that henceforth we can identify the dual (Hs(R))′ of Hs(R) with H−s(R). In particular, if s > 1/2 then Hs(R) ⊂ C0b0 (R), so H−s(R) contains all bounded Radon measures.