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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
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...     »
Stichworte:
BIC, Model selection, Sparsity, Value-at-Risk, Vine copula
Dewey Dezimalklassifikation:
510 Mathematik
Zeitschriftentitel:
Journal of Multivariate Analysis
Jahr:
2019
Band / Volume:
172
Jahr / Monat:
2019-07
Quartal:
3. Quartal
Monat:
Jul
Seitenangaben Beitrag:
180-192
Sprache:
en
Volltext / DOI:
doi:10.1016/j.jmva.2019.03.004
Verlag / Institution:
Elsevier BV
E-ISSN:
0047-259X
Publikationsdatum:
01.07.2019
Semester:
SS 19
TUM Einrichtung:
Professur für Angewandte Mathematische Statistik
Format:
Text
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