Bootstrap for Value at Risk Prediction

Meriem Rjiba, Michail Tsagris and Hedi Mhalla

International Journal of Empirical Finance, 2015, vol. 4, issue 6, 362-371

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 in efficient 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)
Date: 2015
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Citations: View citations in EconPapers (1)

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Working Paper: Bootstrap for Value at Risk Prediction (2015)
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