@techreport{Franke2007Quantile,
abstract = {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 sieve estimates which are a nonparametric versions of the Koenker-Bassett regression quantiles and do not require the specification of the innovation law. We prove consistency of those estimates and illustrate their good performance for light- and heavy-tailed distributions of the innovations with a small simulation study. As an economic application, we use the estimates for calculating the value at risk of some stock price series.},
address = {Berlin},
author = {J\"{u}rgen Franke and Jean-Pierre Stockis and Joseph Tadjuidje},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {C14; C45; 330; conditional quantile; time series; sieve estimate; neural network; qualitative threshold model; uniform consistency; value at risk; Zeitreihenanalyse; Ma\ss{}zahl; Sch\"{a}tztheorie; Value at Risk; B\"{o}rsenkurs; Theorie},
language = {eng},
number = {2007,005},
publisher = {SFB 649, Economic Risk},
title = {Quantile sieve estimates for time series},
type = {SFB 649 discussion paper},
url = {http://hdl.handle.net/10419/25177},
year = {2007}
}
