A comparison of local constant and local linear regression quantile estimators
Computational Statistics & Data Analysis
On the use of the peaks over thresholds method for estimating out-of-sample quantiles
Computational Statistics & Data Analysis
A flexible extreme value mixture model
Computational Statistics & Data Analysis
Mean-CVaR portfolio selection: A nonparametric estimation framework
Computers and Operations Research
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A framework is introduced allowing us to apply nonparametric quantile regression to Value at Risk (VaR) prediction at any probability level of interest. A monotonized double kernel local linear estimator is used to estimate moderate (1%) conditional quantiles of index return distributions. For extreme (0.1%) quantiles, nonparametric quantile regression is combined with extreme value theory. The abilities of the proposed estimators to capture market risk are investigated in a VaR prediction study with empirical and simulated data. Possibly due to its flexibility, the out-of-sample forecasting performance of the new model turns out to be superior to competing models.