Mancini, Loriano Trojani, Fabio 2010-09-10 This paper proposes a robust semiparametric bootstrap method to estimate predictive distributions of GARCH-type models. The method is based on a robust estimation of parametric GARCH models and a robustified resampling scheme for GARCH residuals that controls bootstrap instability due to outlying observations. A Monte Carlo simulation shows that our robust method provides more accurate VaR forecasts than classical methods, often by a large extent, especially for several days ahead horizons and/or in presence of outlying observations. An empirical application confirms the simulation results. The robust procedure outperforms in backtesting several other VaR prediction methods, such as RiskMetrics, CAViaR, Historical Simulation, and classical Filtered Historical Simulation methods. We show empirically that robust estimation reduces tail estimation risk, providing more accurate and more stable VaR prediction intervals over time. application/pdf https://localhost:5000/ark:/12658/srd1318249 https://susi.usi.ch/global/documents/318249 https://susi.usi.ch/documents/318249/files/trojani_JFE_2011.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1093/jjfinec/nbq035 info:eu-repo/semantics/altIdentifier/ark/12658/srd1318249 info:eu-repo/semantics/openAccess License undefined Journal of financial econometrics. - Oxford University Press. - 2011, vol. 9, no. 2, p. 281-313 M-estimator extreme value theory breakdown point backtesting info:eu-repo/classification/udc/33 Robust value at risk prediction http://purl.org/coar/resource_type/c_6501