Title(题名):  Modeling Dependence in High Dimensions With Factor Copulas
Authors(作者):  Dong Hwan Oh and Andrew J. Patton
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
Pages(页码):  p139-154
ISSN:  0735-0015
Abstract(摘要):  This article presents flexible new models for the dependence structure, or copula. Of economic variables based on a latent factor structure. The proposed models are particularly attractive for relatively high-dimensional applications, involving 50 0r more variables, and can be combined with semiparametric marginal distributions to obtain flexible multivariate distributions. Factor copulas generally lack a closed-form density, but we obtain analytical results for the implied tail dependence using extreme value theory and we verify that simulation-based estimation using rank statistics is reliable even in high dimensions. We consider “scree” plots to aid the choice of the number of factors in the model. The model is applied to daily returns on all 100 constituents of the S&P 100 index, and we find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. We also show that factor copula models provide superior estimates of some measures of systemic risk. Supplementary materials for this article are available online.
Key words(关键词):  Copulas; Correlation; Dependence: Systemic risk; Tail dependence.
Where(馆藏地):  304外文报刊阅览室
Available online
 2018/3/7 10:25:36
Click times(点击次数):  
地点:辽宁省大连市尖山街217号东北财经大学图书馆 邮编:116025
版权所有Copyright2010-2013 东北财经大学图书馆