Parallel Bayesian Inference for High-Dimensional Dynamic Factor Copulas

Hoang Nguyen, M Concepción Ausín and Pedro Galeano

Journal of Financial Econometrics, 2019, vol. 17, issue 1, 118-151

Abstract: To account for asymmetric dependence in extreme events, we propose a dynamic generalized hyperbolic skew Student-t factor copula where the factor loadings follow generalized autoregressive score processes. Conditioning on the latent factor, the components of the return series become independent, which allows us to run Bayesian estimation in a parallel setting. Hence, Bayesian inference on different specifications of dynamic one factor copula models can be done in a few minutes. Finally, we illustrate the performance of our proposed models on the returns of 140 companies listed in the S&P500 index. We compare the prediction power of different competing models using value-at-risk (VaR), and conditional VaR (CVaR), and show how to obtain optimal portfolios in high dimensions based on minimum CVaR.

Keywords: Bayesian inference; factor copula models; GAS model; generalized hyperbolic skew Student-t factor copula; parallel estimation (search for similar items in EconPapers)
JEL-codes: C38 C58 G11 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://hdl.handle.net/10.1093/jjfinec/nby032 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:jfinec:v:17:y:2019:i:1:p:118-151.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Journal of Financial Econometrics is currently edited by Allan Timmermann and Fabio Trojani

More articles in Journal of Financial Econometrics from Oxford University Press Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().