Geometric brownian motion under stochastic resetting

  • We study the effects of stochastic resetting on geometric Brownian motion with drift (GBM), a canonical stochastic multiplicative process for nonstationary and nonergodic dynamics. Resetting is a sudden interruption of a process, which consecutively renews its dynamics. We show that, although resetting renders GBM stationary, the resulting process remains nonergodic. Quite surprisingly, the effect of resetting is pivotal in manifesting the nonergodic behavior. In particular, we observe three different long-time regimes: a quenched state, an unstable state, and a stable annealed state depending on the resetting strength. Notably, in the last regime, the system is self-averaging and thus the sample average will always mimic ergodic behavior establishing a stand-alone feature for GBM under resetting. Crucially, the above-mentioned regimes are well separated by a self-averaging time period which can be minimized by an optimal resetting rate. Our results can be useful to interpret data emanating from stock market collapse or reconstitutionWe study the effects of stochastic resetting on geometric Brownian motion with drift (GBM), a canonical stochastic multiplicative process for nonstationary and nonergodic dynamics. Resetting is a sudden interruption of a process, which consecutively renews its dynamics. We show that, although resetting renders GBM stationary, the resulting process remains nonergodic. Quite surprisingly, the effect of resetting is pivotal in manifesting the nonergodic behavior. In particular, we observe three different long-time regimes: a quenched state, an unstable state, and a stable annealed state depending on the resetting strength. Notably, in the last regime, the system is self-averaging and thus the sample average will always mimic ergodic behavior establishing a stand-alone feature for GBM under resetting. Crucially, the above-mentioned regimes are well separated by a self-averaging time period which can be minimized by an optimal resetting rate. Our results can be useful to interpret data emanating from stock market collapse or reconstitution of investment portfolios.show moreshow less

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Metadaten
Author details:Viktor StojkoskiORCiD, Trifce SandevORCiDGND, Ljupco KocarevGND, Arnab PalORCiD
DOI:https://doi.org/10.1103/PhysRevE.104.014121
ISSN:2470-0045
ISSN:2470-0053
Pubmed ID:https://pubmed.ncbi.nlm.nih.gov/34412255
Title of parent work (English):Physical review : E, Statistical, nonlinear and soft matter physics
Subtitle (English):a stationary yet nonergodic process
Publisher:American Physical Society
Place of publishing:College Park
Publication type:Article
Language:English
Date of first publication:2021/07/19
Publication year:2021
Release date:2024/11/29
Volume:104
Issue:1
Article number:014121
Number of pages:11
Funding institution:German Science Foundation (DFG)German Research Foundation (DFG) [ME 1535/12-1]; Alexander von Humboldt FoundationAlexander von Humboldt Foundation; Raymond and Beverly Sackler Post-Doctoral Scholarship; Ratner Center for Single Molecule Science at Tel Aviv University
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie
DDC classification:5 Naturwissenschaften und Mathematik / 53 Physik
Peer review:Referiert
Publishing method:Open Access / Green Open-Access
License (German):License LogoCC-BY - Namensnennung 4.0 International
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