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.
Author: | Jürgen Franke, Peter Mwita |
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URN: | urn:nbn:de:hbz:386-kluedo-12743 |
Series (Serial Number): | Report in Wirtschaftsmathematik (WIMA Report) (87) |
Document Type: | Preprint |
Language of publication: | English |
Year of Completion: | 2003 |
Year of first Publication: | 2003 |
Publishing Institution: | Technische Universität Kaiserslautern |
Date of the Publication (Server): | 2003/11/19 |
Tag: | conditional quantiles; kernel estimate; quantile autoregression; time series; uniform consistency; value-at-risk |
Faculties / Organisational entities: | Kaiserslautern - Fachbereich Mathematik |
DDC-Cassification: | 5 Naturwissenschaften und Mathematik / 510 Mathematik |
Licence (German): | Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011 |