Dynamic D-Vine Copula Model with Applications to Value-at-Risk (VaR)

Tófoli Paula V. (), Ziegelmann Flávio A., Osvaldo Candido and Pedro Valls Pereira
Additional contact information
Tófoli Paula V.: Graduate Program in Economics, Catholic University of Brasilia, SGAN 916, Module B, Office A-120, Asa Norte, Brasilia, DF 70790-160, Brazil
Ziegelmann Flávio A.: Department of Statistics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

Journal of Time Series Econometrics, 2019, vol. 11, issue 2, 34

Abstract: Vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a restricted ARMA(1, m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets. We also investigate the dynamic D-vine copula in a simulation study and the overall results of the Monte Carlo experiments are quite favorable to the dynamic D-vine copula in comparison with a static D-vine copula.

Keywords: regular vine; pair-copula constructions; time-varying copulas; value-at-risk (search for similar items in EconPapers)
JEL-codes: C13 C51 C52 C58 G15 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1515/jtse-2017-0016 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Dynamic D-Vine copula model with applications to Value-at-Risk (VaR) (2016)
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:bpj:jtsmet:v:11:y:2019:i:2:p:34:n:2

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jtse/html

DOI: 10.1515/jtse-2017-0016

Access Statistics for this article

Journal of Time Series Econometrics is currently edited by Javier Hidalgo

More articles in Journal of Time Series Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().