Nonparametric simulation of multivariate extreme events via spectral bootstrap

Nisrine Madhar, Juliette Legrand and Maud Thomas

Scandinavian Journal of Statistics, 2026, vol. 53, issue 1, 482-497

Abstract: Inference in extreme value theory (EVT) relies on a limited number of extreme observations, making estimation challenging. To address this limitation, we propose a nonparametric simulation scheme, the multivariate extreme events spectral bootstrap simulation procedure, relying on the spectral representation of multivariate generalized Pareto‐distributed random vectors. Unlike standard bootstrap methods, our approach preserves the joint tail behavior of the data and generates additional synthetic extreme data, thereby improving the reliability of inference. We demonstrate the effectiveness of our procedure on the estimation of tail risk metrics, under both simulated and real data. The results highlight the potential of this method for enhancing risk assessment in high‐dimensional extreme scenarios.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/sjos.70048

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:53:y:2026:i:1:p:482-497

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0303-6898

Access Statistics for this article

Scandinavian Journal of Statistics is currently edited by ÿrnulf Borgan and Bo Lindqvist

More articles in Scandinavian Journal of Statistics from Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association, Swedish Statistical Association
Bibliographic data for series maintained by Wiley Content Delivery ().