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Processus de Lévy en Finance - Laboratoire de Probabilités et ...

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142 CHAPTER 4. DEPENDENCE OF LEVY PROCESSES<br />

Definition 4.5 (F-volume). L<strong>et</strong> F : S ⊂ ¯R d → ¯R. For a, b ∈ S, with a ≤ b, the F -volume of<br />

|a, b| is <strong>de</strong>fined by<br />

V F (|a, b|) := ∑ (−1) N(c) F (c), (4.4)<br />

where the sum is tak<strong>en</strong> over all vertices c of |a, b|, and N(c) := #{k : c k = a k }.<br />

The notion of F-volume should only be se<strong>en</strong> as a conv<strong>en</strong>i<strong>en</strong>t notation for the sum in the<br />

right-hand si<strong>de</strong> of (4.4). It does not in g<strong>en</strong>eral correspond to any measure because the measure<br />

of |a, b| will <strong>de</strong>p<strong>en</strong>d on wh<strong>et</strong>her the boundary is inclu<strong>de</strong>d or not.<br />

In particular, in two dim<strong>en</strong>sions (d = 2) the F -volume of a rectangle B = |x 1 , x 2 | × |y 1 , y 2 |<br />

satisfies<br />

V F (B) = F (x 2 , y 2 ) − F (x 2 , y 1 ) − F (x 1 , y 2 ) + F (x 1 , y 1 ).<br />

If F (u) = ∏ d<br />

i=1 u i, the F -volume of any interval is equal to its Lebesgue measure.<br />

Definition 4.6 (d-increasing function). A function F : S ⊂ ¯R d → ¯R is called d-increasing<br />

if for all a, b ∈ S with a ≤ b, V F (|a, b|) ≥ 0.<br />

Definition 4.7 (Groun<strong>de</strong>d function). For each k, l<strong>et</strong> S k ⊂ ¯R be such that inf S k ∈ S k . A<br />

function F : S 1 × · · · × S d → ¯R is groun<strong>de</strong>d if F (x 1 , . . . , x d ) = 0 wh<strong>en</strong>ever x k = inf S k for at<br />

least one k.<br />

Definition 4.8 (margins of a d-increasing groun<strong>de</strong>d function). For each k, l<strong>et</strong> S k ⊂ ¯R<br />

be such that inf S k ∈ S k and sup S k ∈ S k and l<strong>et</strong> F : S 1 × · · · × S d → ¯R be d-increasing and<br />

groun<strong>de</strong>d. Th<strong>en</strong> for I ⊂ {1, . . . , d} nonempty, the I-margin of F is a function F I : ∏ i∈I S i → ¯R<br />

<strong>de</strong>fined by<br />

F I ((x i ) i∈I ) := F (x 1 , . . . , x d )| xi =sup S i , i∈Ic. (4.5)<br />

Wh<strong>en</strong> I = {k}, to simplify notation, the (one-dim<strong>en</strong>sional) I-margin of F is <strong>de</strong>noted by F k .<br />

Example 4.3. The distribution function F of a random vector X ∈ R d is usually <strong>de</strong>fined by<br />

F (x 1 , . . . , x d ) := P [X 1 ≤ x 1 , . . . , X d ≤ x d ]

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