Forecasting robust value-at-risk estimates: evidence from UK banks

Marius Galabe Sampid, Haslifah M. Hasim

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we present a novel approach for forecasting Value-at-Risk (VaR) by combining a Bayesian GARCH(1,1) model with Student's-t distribution for the underlying volatility models, vine copula functions to model dependence, and the peaks-over-threshold (POT) method of extreme value theory (EVT) to model the tail behaviour of asset returns. We further propose a new approach for threshold selection in extreme value analysis, which we call a hybrid method. The empirical results and back-testing analysis show that the model captures VaR quite well through periods of calmness and crisis; therefore, it is suitable for use as a measure of risk. Our results also suggest that with a correct implementation of the VaR model, Basel III is not needed.

Original languageEnglish
JournalQuantitative Finance
Early online date8 Mar 2019
DOIs
Publication statusE-pub ahead of print - 8 Mar 2019

Keywords

  • Extreme value theory
  • GARCH
  • Risk management
  • Value-at-risk
  • Vine copulas
  • Volatility model

ASJC Scopus subject areas

  • Finance
  • Economics, Econometrics and Finance(all)

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