Comparison of Methods for Estimating the Uncertainty of Value at Risk
Santiago Gamba (),
Oscar Fernando Jaulín Méndez (),
Luis Melo-Velandia and
Carlos Andrés Quicazán Moreno ()
Additional contact information
Oscar Fernando Jaulín Méndez: Banco de la República de Colombia
Carlos Andrés Quicazán Moreno: Banco de la República de Colombia
Borradores de Economia from Banco de la Republica de Colombia
Abstract:
Value at Risk (VaR) is a market risk measure widely used by risk managers and market regulatory authorities. There is a variety of methodologies proposed in the literature for the estimation of VaR. However, few of them get to say something about its distribution or its confidence intervals. This paper compares different methodologies for computing such intervals. Several methods, based on asymptotic normality, extreme value theory and subsample bootstrap, are used. Using Monte Carlo simulations, it is found that these approaches are only valid for high quantiles. In particular, there is a good performance for VaR (99%), in terms of coverage rates, and bad performance for VaR (95%) and VaR (90%). The results are confirmed by an empirical application for the stock market index returns of G7 countries.
Keywords: Value at Risk; confidence intervals; data tilting; subsample bootstrap. (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 G32 (search for similar items in EconPapers)
Pages: 22
Date: 2016-02
New Economics Papers: this item is included in nep-rmg and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
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https://doi.org/10.32468/be.927 (application/pdf)
Related works:
Journal Article: Comparison of methods for estimating the uncertainty of value at risk (2016) 
Working Paper: Comparison of Methods for Estimating the Uncertainty of Value at Risk (2016) 
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Persistent link: https://EconPapers.repec.org/RePEc:bdr:borrec:927
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