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Quantile sieve estimates for time series

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  • Franke, Jürgen
  • Stockis, Jean-Pierre
  • Tadjuidje, Joseph

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.

Suggested Citation

  • Franke, Jürgen & Stockis, Jean-Pierre & Tadjuidje, Joseph, 2007. "Quantile sieve estimates for time series," SFB 649 Discussion Papers 2007-005, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2007-005
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    References listed on IDEAS

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    More about this item

    Keywords

    conditional quantile; time series; sieve estimate; neural network; qualitative threshold model; uniform consistency; value at risk;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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