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Linear factor copula models and their properties

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  • Pavel Krupskii
  • Marc G. Genton

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.

Suggested Citation

  • Pavel Krupskii & Marc G. Genton, 2018. "Linear factor copula models and their properties," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(4), pages 861-878, December.
    • Handle: RePEc:bla:scjsta:v:45:y:2018:i:4:p:861-878
    DOI: 10.1111/sjos.12325
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    Cited by:

    1. Pavel Krupskii & Harry Joe, 2022. "Approximate likelihood with proxy variables for parameter estimation in high-dimensional factor copula models," Statistical Papers, Springer, vol. 63(2), pages 543-569, April.

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