Modelling Across Extremal Dependence Classes

Jennifer L. Wadsworth · Jonathan A. Tawn · Anthony C. Davison · Daniel M. Elton

76 citations0/0 files storedJournal of the Royal Statistical Society Series B (Statistical Methodology)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Probability and Risk Models Statistical Distribution Estimation and Applications Financial Risk and Volatility Modeling

Abstract

Summary Different dependence scenarios can arise in multivariate extremes, entailing careful selection of an appropriate class of models. In bivariate extremes, the variables are either asymptotically dependent or are asymptotically independent. Most available statistical models suit one or other of these cases, but not both, resulting in a stage in the inference that is unaccounted for but can substantially impact subsequent extrapolation. Existing modelling solutions to this problem are either applicable only on subdomains or appeal to multiple limit theories. We introduce a unified representation for bivariate extremes that encompasses a wide variety of dependence scenarios and applies when at least one variable is large. Our representation motivates a parametric model that encompasses both dependence classes. We implement a simple version of this model and show that it performs well in a range of settings.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 76 citations exist upstream.

References

1987Extreme Values, Regular Variation and Point Processes 2004Statistics of Extremes 2006Heavy-Tail Phenomena: Probabilistic and Statistical Modeling 1999Dependence Measures for Extreme Value Analyses 2004A Conditional Approach for Multivariate Extreme Values (with Discussion)2009Multivariate Archimedean copulas, d-monotone functions and ℓ1-norm symmetric distributions 1991Modelling Extreme Multivariate Events 2009Multivariate Archimedean copulas, d-monotone functions and ℓ1-norm symmetric distributions

Cited by

2022Higher-dimensional spatial extremes via single-site conditioning 2019Extremal dependence of random scale constructions 2022Asymmetric tail dependence modeling, with application to cryptocurrency market data

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.