Quantile Sieve Estimates For Time Series
AbstractWe 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.
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Bibliographic InfoPaper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2007-005.
Length: 26 pages
Date of creation: Feb 2007
Date of revision:
Conditional Quantile; Time Series; Sieve Estimate; Neural Network; Qualitative Threshold Model; Uniform Consistency; Value at Risk;
Find related papers by 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-02-10 (All new papers)
- NEP-ECM-2007-02-10 (Econometrics)
- NEP-ETS-2007-02-10 (Econometric Time Series)
- NEP-RMG-2007-02-10 (Risk Management)
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