## Nonparametric Estimates for Conditional Quantiles of Time Series

• We consider the problem of estimating the conditional quantile of a time series at time t given observations of the same and perhaps other time series available at time t-1. We discuss an estimate which we get by inverting a kernel estimate of the conditional distribution function, and prove its asymptotic normality and uniform strong consistency. We illustrate the good performance of the estimate for light and heavy-tailed distributions of the innovations with a small simulation study.

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Author: Jürgen Franke, Peter Mwita urn:nbn:de:hbz:386-kluedo-12743 Report in Wirtschaftsmathematik (WIMA Report) (87) Preprint English 2003 2003 Technische Universität Kaiserslautern 2003/11/19 conditional quantiles; kernel estimate; quantile autoregression; time series; uniform consistency; value-at-risk Fachbereich Mathematik 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011

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