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.

Export metadata

  • Export Bibtex
  • Export RIS

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Jürgen Franke, Peter Mwita
URN (permanent link):urn:nbn:de:hbz:386-kluedo-12743
Serie (Series number):Report in Wirtschaftsmathematik (WIMA Report) (87)
Document Type:Preprint
Language of publication:English
Year of Completion:2003
Year of Publication:2003
Publishing Institute:Technische Universität Kaiserslautern
Tag:conditional quantiles; kernel estimate; quantile autoregression; time series; uniform consistency; value-at-risk
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:510 Mathematik

$Rev: 12793 $