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Conditional limit theorems for spectrally positive Lévy processes

Published online by Cambridge University Press:  19 February 2016

Takis Konstantopoulos
Affiliation:
University of Texas at Austin
Gregory S. Richardson*
Affiliation:
St Paul Companies
*
** Postal address: St Paul Companies, 310 N. Washington, 510C, St Paul, MN 55102, USA.

Abstract

We consider spectrally positive Lévy processes with regularly varying Lévy measure and study conditional limit theorems that describe the way that various rare events occur. Specifically, we are interested in the asymptotic behaviour of the distribution of the path of the Lévy process (appropriately scaled) up to some fixed time, conditionally on the event that the process exceeds a (large) positive value at that time. Another rare event we study is the occurrence of a large maximum value up to a fixed time, and the corresponding asymptotic behaviour of the (scaled) Lévy process path. We study these distributional limit theorems both for a centred Lévy process and for one with negative drift. In the latter case, we also look at the reflected process, which is of importance in applications. Our techniques are based on the explicit representation of the Lévy process in terms of a two-dimensional Poisson random measure and merely use the Poissonian properties and regular variation estimates. We also provide a proof for the asymptotic behaviour of the tail of the stationary distribution for the reflected process. The work is motivated by earlier results for discrete-time random walks (e.g. Durrett (1980) and Asmussen (1996)) and also by their applications in risk and queueing theory.

Type
General Applied Probability
Copyright
Copyright © Applied Probability Trust 2002 

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Footnotes

*

Current address: Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX 78712, USA. Email address: [email protected]

This work was supported in part by NSF grant ANI-9903495 and was the basis of the second author's PhD dissertation (Richardson (2000)).

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