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Copula-Based Factor Models for Multivariate Asset Returns
Open AccessArticle

Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting

Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Jean-David Fermanian
Econometrics 2017, 5(2), 21; https://doi.org/10.3390/econometrics5020021
Received: 25 November 2016 / Revised: 26 April 2017 / Accepted: 8 May 2017 / Published: 23 May 2017
(This article belongs to the Special Issue Recent Developments in Copula Models)
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). View Full-Text
Keywords: Bayesian inference; dependence modeling; factor copulas; factor models; factor analysis; latent variables; MCMC; portfolio risk; value at risk; expected shortfall Bayesian inference; dependence modeling; factor copulas; factor models; factor analysis; latent variables; MCMC; portfolio risk; value at risk; expected shortfall
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MDPI and ACS Style

Schamberger, B.; Gruber, L.F.; Czado, C. Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting. Econometrics 2017, 5, 21. https://doi.org/10.3390/econometrics5020021

AMA Style

Schamberger B, Gruber LF, Czado C. Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting. Econometrics. 2017; 5(2):21. https://doi.org/10.3390/econometrics5020021

Chicago/Turabian Style

Schamberger, Benedikt; Gruber, Lutz F.; Czado, Claudia. 2017. "Bayesian Inference for Latent Factor Copulas and Application to Financial Risk Forecasting" Econometrics 5, no. 2: 21. https://doi.org/10.3390/econometrics5020021

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