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

Marius Galabe Sampid and Haslifah M. Hasim

Quantitative Finance, 2021, vol. 21, issue 11, 1955-1975

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.

Date: 2021
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DOI: 10.1080/14697688.2019.1579923

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