Name
Abstract | ||
|---|---|---|
When assessing the impact of extreme events, it is often not just a single component, but the combined behavior of several components which is important. Statistical modeling using multivariate generalized Pareto (GP) distributions constitutes the multivariate analogue of univariate peaks over thresholds modeling, which is widely used in finance and engineering. We develop general methods for construction of multivariate GP distributions and use them to create a variety of new statistical models. A censored likelihood procedure is proposed to make inference on these models, together with a threshold selection procedure, goodness-of-fit diagnostics, and a computationally tractable strategy for model selection. The models are fitted to returns of stock prices of four UK-based banks and to rainfall data in the context of landslide risk estimation. Supplementary materials and codes are available online. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/00401706.2018.1462738 | TECHNOMETRICS |
Keywords | Field | DocType |
Financial risk,Landslides,Multivariate extremes,Tail dependence | Econometrics,Tail dependence,Multivariate statistics,Extreme value theory,Inference,Generalized Pareto distribution,Statistical model,Statistics,Univariate,Mathematics | Journal |
Volume | Issue | ISSN |
61.0 | 1.0 | 0040-1706 |
Citations | PageRank | References |
1 | 0.49 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anna Kiriliouk | 1 | 1 | 0.49 |
| Holger Rootzén | 2 | 2 | 1.38 |
| Johan Segers | 3 | 41 | 10.37 |
| Jennifer Wadsworth | 4 | 1 | 0.49 |