2005
DOI: 10.1214/ejp.v10-261
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On Lévy processes conditioned to stay positive.

Abstract: We construct the law of Lévy processes conditioned to stay positive under general hypotheses. We obtain a Williams type path decomposition at the minimum of these processes. This result is then applied to prove the weak convergence of the law of Lévy processes conditioned to stay positive as their initial state tends to 0. We describe an absolute continuity relationship between the limit law and the measure of the excursions away from 0 of the underlying Lévy process reflected at its minimum. Then, when the Lé… Show more

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Cited by 101 publications
(180 citation statements)
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“…Hence a significant amount of probabilistic information concerning this conditioning is captured by the scale function. See Chaumont and Doney [27] for a complete overview. In a similar spirit Chaumont [25,26] also shows that scale functions can be used to describe the law of a Lévy process conditioned to hit the origin continuously in a finite time.…”
Section: Scale Functions and Applied Probabilitymentioning
confidence: 99%
“…Hence a significant amount of probabilistic information concerning this conditioning is captured by the scale function. See Chaumont and Doney [27] for a complete overview. In a similar spirit Chaumont [25,26] also shows that scale functions can be used to describe the law of a Lévy process conditioned to hit the origin continuously in a finite time.…”
Section: Scale Functions and Applied Probabilitymentioning
confidence: 99%
“…With this purpose, we now briefly recall the definition of the Lévy process conditioned to stay positive ξ ↑ and refer to [8] for a complete account on this subject. The process ξ ↑ is an h-process of ξ killed when it first exists (0, ∞), i.e.…”
Section: Weak Convergence and Entrance Law Of Pssmpmentioning
confidence: 99%
“…There exists a vast body of literature on (one-dimensional) Lévy processes conditioned to stay nonpositive (or nonnegative), see the recent paper by Chaumont and Doney [5] for references. Under the measure P ↓ k , we also find the transform of (X, G).…”
Section: Introductionmentioning
confidence: 99%