@misc{10481/104066,
year = {2025},
month = {4},
url = {https://hdl.handle.net/10481/104066},
abstract = {This work introduces and analyzes lognormal diffusion processes subject to random catastrophes,
i.e. random events which cause jumps and reset the process to a possibly different random
state. The model incorporates a binomial distribution for the restarting points and employs
stochastic techniques to describe cycles between catastrophes. A maximum likelihood approach
is developed to estimate the model parameters and it is applied both to simulated and real data.
More specifically, we perform a simulation study based on 50 replications and 500 sample paths,
both in the case in which the size of the binomial distribution is known and in the case in
which it is unknown. Moreover, we provide a real application to GDP (gross domestic product)
trajectories of five European countries affected by the economic crises of 2009 and 2020. The
analysis demonstrates the model’s effectiveness in mimicking complex phenomena characterized
by growth dynamics interrupted by random external events.},
organization = {MUR-PRIN 2022 PNRR,
project P2022XSF5H ``Stochastic Models in Biomathematics and Applications''},
organization = {European Union -- Next Generation EU through MUR-PRIN 2022, project 2022XZSAFN ``Anomalous Phenomena on Regular and Irregular Domains: Approximating Complexity for the Applied Sciences''},
organization = {PID2020-1187879GB-100 and CEX2020-001105-M grants, funded by MCIN/AEI/
10.13039/501100011033 (Spain)},
publisher = {Elsevier},
keywords = {Lognormal diffusion processes},
keywords = {Binomial catastrophes},
keywords = {Maximum likelihood estimation},
title = {Special lognormal diffusion processes with binomial random catastrophes and applications to economic data},
doi = {10.1016/j.apm.2025.116146},
author = {Di Crescenzo, Antonio and Musto, Sabina and Paraggio, Paola and Torres Ruiz, Francisco De Asís},
}