Bootstrap for Value at Risk Prediction

Meriem Rjiba, Meriem and Tsagris, Michail and Mhalla, Hedi (2015): Bootstrap for Value at Risk Prediction. Published in: International Journal of Empirical Finance , Vol. 4, No. 6 (2015): pp. 263-371.

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 inefficient under moderate departures from normality and that the new method produces the most accurate forecasts of extreme losses.

• Item Type: MPRA Paper
• Language: English
• Keywords: Value at Risk, bootstrap, GARCH
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

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