Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Technometrics on 19/04/2018, available online: http://www.tandfonline.com/10.1080/00401706.2018.1462738
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Final published version
Licence: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Peaks over thresholds modelling with multivariate generalized Pareto distributions
AU - Kiriliouk, Anna
AU - Rootzén, Holger
AU - Segers, Johan
AU - Wadsworth, Jennifer Lynne
N1 - This is an Accepted Manuscript of an article published by Taylor & Francis in Technometrics on 19/04/2018, available online: http://www.tandfonline.com/10.1080/00401706.2018.1462738
PY - 2019/1/2
Y1 - 2019/1/2
N2 - When assessing the impact of extreme events, it is often not just a single component, but the combined behaviour of several components which is important. Statistical modelling using multivariate generalized Pareto (GP) distributions constitutes the multivariate analogue of univariate peaks over thresholds modelling, 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.
AB - When assessing the impact of extreme events, it is often not just a single component, but the combined behaviour of several components which is important. Statistical modelling using multivariate generalized Pareto (GP) distributions constitutes the multivariate analogue of univariate peaks over thresholds modelling, 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.
KW - financial risk
KW - landslides
KW - multivariate extremes
KW - tail dependence
U2 - 10.1080/00401706.2018.1462738
DO - 10.1080/00401706.2018.1462738
M3 - Journal article
VL - 61
SP - 123
EP - 135
JO - Technometrics
JF - Technometrics
SN - 0040-1706
IS - 1
ER -