{"id":{"repo_id":"humboldt-diss","oai_identifier":"oai:edoc.hu-berlin.de:18452/14819"},"canonical_url":"https://search.dev.ndltd.org/etd/humboldt-diss/oai:edoc.hu-berlin.de:18452/14819","repository":{"repo_id":"humboldt-diss","name":"Humboldt Universität zu Berlin","base_url":"https://edoc.hu-berlin.de/server/oai/request"},"display":{"title":"Nonparametric Estimate for Conditional Quantiles of Time Series","abstract":"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.","abstract_html":"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. 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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."]},{"key":"dc:title","label":"Title","values":["Nonparametric Estimate for Conditional Quantiles of Time Series"]}]}],"canonical_facts":{"dc:creator":["Balcau, Ioana"],"dc:date.accessioned":["2017-06-18T02:33:03Z"],"dc:date.available":["2017-06-18T02:33:03Z"],"dc:date.issued":["2012-06-12"],"dc:description.abstract":["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."],"dc:identifier.doi":["https://doi.org/10.18452/14167"],"dc:identifier.uri":["https://edoc.hu-berlin.de/18452/14819"],"dc:language.iso":["eng"],"dc:publisher":["Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Value at Risk","Nonparametric","Backtesting","Conditional Quantiles","Kernel Estimation"],"dc:title":["Nonparametric Estimate for Conditional Quantiles of Time Series"],"dc:type":["masterThesis"]},"updated_at":"2026-08-21T16:45:14Z"}