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

Sampid, Marius and Hasim, Haslifah (2019) 'Forecasting robust value-at-risk estimates: Evidence from UK banks.' Quantitative Finance. ISSN 1469-7688

Manuscript_acceptedversion(Feb2019).pdf - Accepted Version

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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 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.

Item Type: Article
Uncontrolled Keywords: Value-at-risk, Risk management, Extreme value theory, GARCH, Volatility model, Vine copulas
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Elements
Date Deposited: 06 Feb 2019 10:02
Last Modified: 08 Sep 2020 01:00

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