Sieve bootstrap prediction intervals for contaminated non-linear processes

Gustavo Ulloa, Héctor Allende-Cid, Héctor Allende

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Recently, the sieve bootstrap method has been successfully used in prediction of nonlinear time series. In this work we study the performance of the prediction intervals based on the sieve bootstrap technique, which does not require the distributional assumption of normality as most techniques that are found in the literature. The construction of prediction intervals in the presence of patchy outliers are not robust from a distributional point of view, leading to an undesirable increase in the length of the prediction intervals. In the analysis of financial time series it is common to have irregular observations that have different types of outliers, isolated and in groups. For this reason we propose the construction of prediction intervals for returns based in the winsorized residual and bootstrap techniques for financial time series. We propose a novel, simple, efficient and distribution free resampling technique for developing robust prediction intervals for returns and volatilities for TGARCH models. The proposed procedure is illustrated by an application to known synthetic time series.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 18th Iberoamerican Congress, CIARP 2013, Proceedings
Number of pages8
EditionPART 1
StatePublished - 2013
Externally publishedYes
Event18th Iberoamerican Congress on Pattern Recognition, CIARP 2013 - Havana, Cuba
Duration: 20 Nov 201323 Nov 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume8258 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th Iberoamerican Congress on Pattern Recognition, CIARP 2013


  • Financial prediction intervals
  • Forecasting in time series
  • Sieve bootstrap
  • TGARCH models
  • Time series
  • Volatility
  • Winsorized filter


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