Estimating value-at-risk using a multivariate copula-based volatility model: Evidence from European banks

Marius Galabe Sampid, Haslifah M. Hasim

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This paper proposes a multivariate copula-based volatility model for estimating Value-at-Risk (VaR) in the banking sector of selected European countries by combining dynamic conditional correlation (DCC) multivariate GARCH (M-GARCH) volatility model and copula functions. Non-normality in multivariate models is associated with the joint probability of the univariate models' marginal probabilities –the joint probability of large market movements, referred to as tail dependence. In this paper, we use copula functions to model the tail dependence of large market movements and test the validity of our results by performing back-testing techniques. The results show that the copula-based approach provides better estimates than the common methods currently used and captures VaR reasonably well based on the differences in the numbers of exceptions produced during different observation periods at the same confidence level.

Original languageEnglish
Pages (from-to)175-192
Number of pages18
JournalInternational Economics
Volume156
Early online date9 Mar 2018
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Copulas
  • Dynamic conditional correlation
  • GARCH
  • Value-at-risk
  • Volatility

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics, Econometrics and Finance(all)

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