Quantile sieve estimates for time series

Jürgen Franke, Jean-Pierre Stockis and Joseph Tadjuidje

No 2007-005, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: conditional quantile; time series; sieve estimate; neural network; qualitative threshold model; uniform consistency; value at risk (search for similar items in EconPapers)
JEL-codes: C14 C45 (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2007-005

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