Bootstrap for Value at Risk Prediction

Meriem Meriem Rjiba, Michail Tsagris and Hedi Mhalla

MPRA Paper from University Library of Munich, Germany

Abstract: We evaluate the predictive performance of a variety of value-at-risk (VaR) models for a portfolio consisting of five assets. Traditional VaR models such as historical simulation with bootstrap and filtered historical simulation methods are considered. We suggest a new method for estimating Value at Risk: the filtered historical simulation GJR-GARCH method based on bootstrapping the standardized GJR-GARCH residuals. The predictive performance is evaluated in terms of three criteria, the test of unconditional coverage, independence and conditional coverage and the quadratic loss function suggested. The results show that classical methods are inefficient under moderate departures from normality and that the new method produces the most accurate forecasts of extreme losses.

Keywords: Value at Risk; bootstrap; GARCH (search for similar items in EconPapers)
JEL-codes: C15 G17 (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Citations: View citations in EconPapers (1)

Published in International Journal of Empirical Finance 6.4(2015): pp. 263-371

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Journal Article: Bootstrap for Value at Risk Prediction (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:68842

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