Dependent defaults and losses with factor copula models

Abstract

Abstract:We present a class of exible and tractable static factor models for the term structure of joint default probabilities, the factor copula models. These high-dimensional models remain parsimonious with paircopula constructions, and nest many standard models as special cases. The loss distribution of a portfolio of contingent claims can be exactly and e ciently computed when individual losses are discretely supported on a nite grid. Numerical examples study the key features a ecting the loss distribution and multi-name credit derivatives prices. An empirical exercise illustrates the exibility of our approach by tting credit index tranche prices.

Why Ordinal Variables Can (Almost) Always Be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods

“…If the ordinal nature of variables would be taken seriously, more sophisticated modeling strategies for factor analysis that try to estimate more flexible distributions are required (Jin and Yang-Wallentin, 2017;Foldnes and Grønneberg, 2019). A particularly attractive distribution class is the factor copula model (Krupskii and Joe, 2013;Nikoloulopoulos and Joe, 2015;Ackerer and Vatter, 2017;Krupskii and Genton, 2018). Copula models decompose a joint distribution for modeling into marginal distributions and modeling the dependency structure.…”

Why Ordinal Variables Can (Almost) Always be Treated as Continuous Variables: Clarifying Assumptions of Robust Continuous and Ordinal Factor Analysis Estimation Methods

“…If the ordinal nature of variables would be taken seriously, more sophisticated modeling strategies for factor analysis that try to estimate more flexible distributions are required (Foldnes & Grønneberg, 2019;Jin & Yang-Wallentin, 2017). A particularly attractive distribution class would be factor copula models (Ackerer & Vatter, 2017;Krupskii & Genton, 2018;Krupskii & Joe, 2013;Nikoloulopoulos & Joe, 2015). Copula models decompose a joint distribution for modeling into marginal distributions and modeling the dependency structure.…”