Linear factor copula models and their properties

Pavel Krupskii; Marc G. Genton

doi:10.1111/sjos.12325

Linear factor copula models and their properties

Pavel Krupskii, Marc G. Genton

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme-value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme-value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

Original language	English (US)
Pages (from-to)	861-878
Number of pages	18
Journal	Scandinavian Journal of Statistics
Volume	45
Issue number	4
DOIs	https://doi.org/10.1111/sjos.12325
State	Published - Apr 25 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research was supported by the King Abdullah University of Science and Technology (KAUST). We would like to thank the anonymous referee, associate editor, and editor-in-chief for their comments that helped improve this paper.

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10.1111/sjos.12325

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abstract = "We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme-value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme-value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.",

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N2 - We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme-value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme-value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

AB - We consider a special case of factor copula models with additive common factors and independent components. These models are flexible and parsimonious with O(d) parameters where d is the dimension. The linear structure allows one to obtain closed form expressions for some copulas and their extreme-value limits. These copulas can be used to model data with strong tail dependencies, such as extreme data. We study the dependence properties of these linear factor copula models and derive the corresponding limiting extreme-value copulas with a factor structure. We show how parameter estimates can be obtained for these copulas and apply one of these copulas to analyse a financial data set.

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EP - 878

JO - Scandinavian Journal of Statistics

JF - Scandinavian Journal of Statistics

IS - 4

ER -

Linear factor copula models and their properties

Abstract

Bibliographical note

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