Nonparametric Estimate for Conditional Quantiles of Time Series

Ioana Balcau
This paper investigates a nonparametric approach for estimating conditional quantiles of time series for dependent data. The considered estimate is obtained by inverting a kernel estimate of the conditional distribution function. We implement the technique on four simulated samples with light and heavy-tailed distributions and on real financial data, by calculating VaR using the nonparametric procedure. The good performance of the estimator is illustrated with backtesting.
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