Sample-Path Large Deviations for Lévy Processes and Random Walks with Lognormal Increments

Zhe Su; Chang-Han Rhee

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arXiv · 2410.20799

Sample-Path Large Deviations for Lévy Processes and Random Walks with Lognormal Increments

Zhe Su·Chang-Han Rhee

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Abstract

The large deviations theory for heavy-tailed processes has seen significant advances in the recent past. In particular, Rhee et al. (2019) and Bazhba et al. (2020) established large deviation asymptotics at the sample-path level for Lévy processes and random walks with regularly varying and (heavy-tailed) Weibull-type increments. This leaves the lognormal case -- one of the three most prominent classes of heavy-tailed distributions, alongside regular variation and Weibull -- open. This article establishes the \emph{extended large deviation principle} (extended LDP) at the sample-path level for one-dimensional Lévy processes and random walks with lognormal-type increments. Building on these results, we also establish the extended LDPs for multi-dimensional processes with independent coordinates. We demonstrate the sharpness of these results by constructing counterexamples, thereby proving that our results cannot be strengthened to a standard LDP under $J_1$ topology and $M_1'$ topology.

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BibTeX RIS

Zhe Su, Chang-Han Rhee. 2024-10-28. Sample-Path Large Deviations for Lévy Processes and Random Walks with Lognormal Increments. https://arxiv.org/abs/2410.20799

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