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Title:

Model selection in sparse high-dimensional vine copula models with an application to portfolio risk

Document type:
Zeitschriftenaufsatz
Author(s):
Nagler, T., Bumann, C. and Czado, C.
Abstract:
Vine copulas allow the construction of flexible dependence models for an arbitrary number of variables using only bivariate building blocks. The number of parameters in a vine copula model increases quadratically with the dimension, which poses challenges in high-dimensional applications. To alleviate the computational burden and risk of overfitting, we propose a modified Bayesian information criterion (BIC) tailored to sparse vine copula models. We argue that this criterion can consistently dis...     »
Keywords:
BIC, Model selection, Sparsity, Value-at-Risk, Vine copula
Dewey Decimal Classification:
510 Mathematik
Journal title:
Journal of Multivariate Analysis
Year:
2019
Journal volume:
172
Year / month:
2019-07
Quarter:
3. Quartal
Month:
Jul
Pages contribution:
180-192
Language:
en
Fulltext / DOI:
doi:10.1016/j.jmva.2019.03.004
Publisher:
Elsevier BV
E-ISSN:
0047-259X
Date of publication:
01.07.2019
Semester:
SS 19
TUM Institution:
Professur für Angewandte Mathematische Statistik
Format:
Text
 BibTeX