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On the construction of low-parametric families of min-stable multivariate exponential distributions in large dimensions

On the construction of low-parametric families of min-stable multivariate exponential... AbstractMin-stable multivariate exponential (MSMVE) distributions constitute an important family of distributions,among others due to their relation to extreme-value distributions. Being true multivariate exponentialmodels, they also represent a natural choicewhen modeling default times in credit portfolios. Despitebeing well-studied on an abstract level, the number of known parametric families is small. Furthermore, formost families only implicit stochastic representations are known. The present paper develops new parametricfamilies of MSMVE distributions in arbitrary dimensions. Furthermore, a convenient stochastic representationis stated for such models, which is helpful with regard to sampling strategies. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Dependence Modeling de Gruyter

On the construction of low-parametric families of min-stable multivariate exponential distributions in large dimensions

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Publisher
de Gruyter
Copyright
© 2015 German Bernhart et al.
ISSN
2300-2298
eISSN
2300-2298
DOI
10.1515/demo-2015-0003
Publisher site
See Article on Publisher Site

Abstract

AbstractMin-stable multivariate exponential (MSMVE) distributions constitute an important family of distributions,among others due to their relation to extreme-value distributions. Being true multivariate exponentialmodels, they also represent a natural choicewhen modeling default times in credit portfolios. Despitebeing well-studied on an abstract level, the number of known parametric families is small. Furthermore, formost families only implicit stochastic representations are known. The present paper develops new parametricfamilies of MSMVE distributions in arbitrary dimensions. Furthermore, a convenient stochastic representationis stated for such models, which is helpful with regard to sampling strategies.

Journal

Dependence Modelingde Gruyter

Published: May 22, 2015

References