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

Benedikt Schamberger, Lutz F. Gruber and Claudia Czado
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Benedikt Schamberger: Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
Lutz F. Gruber: Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
Claudia Czado: Department of Mathematics, Technical University of Munich, 85748 Garching, Germany

Econometrics, 2017, vol. 5, issue 2, 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).

Keywords: Bayesian inference; dependence modeling; factor copulas; factor models; factor analysis; latent variables; MCMC; portfolio risk; value at risk; expected shortfall (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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