Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171917
Authors: 
Schamberger, Benedikt
Gruber, Lutz F.
Czado, Claudia
Year of Publication: 
2017
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 5 [Year:] 2017 [Issue:] 2 [Pages:] 1-23
Abstract: 
Factor modeling is a popular strategy to induce sparsity in multivariate models as they scale to higher dimensions. We develop Bayesian inference for a recently proposed latent factor copula model, which utilizes a pair copula construction to couple the variables with the latent factor. We use adaptive rejection Metropolis sampling (ARMS) within Gibbs sampling for posterior simulation: Gibbs sampling enables application to Bayesian problems, while ARMS is an adaptive strategy that replaces traditional Metropolis-Hastings updates, which typically require careful tuning. Our simulation study shows favorable performance of our proposed approach both in terms of sampling efficiency and accuracy. We provide an extensive application example using historical data on European financial stocks that forecasts portfolio Value at Risk (VaR) and Expected Shortfall (ES).
Subjects: 
Bayesian inference
dependence modeling
factor copulas
factor models
factor analysis
latent variables
MCMC
portfolio risk
value at risk
expected shortfall
JEL: 
C11
C31
C38
C51
C58
C63
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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