Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity

Jungsik Noh, Sangyeol Lee

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


This paper considers quantile regression for a wide class of time series models including autoregressive and moving average (ARMA) models with asymmetric generalized autoregressive conditional heteroscedasticity errors. The classical mean-variance models are reinterpreted as conditional location-scale models so that the quantile regression method can be naturally geared into the considered models. The consistency and asymptotic normality of the quantile regression estimator is established in location-scale time series models under mild conditions. In the application of this result to ARMA-generalized autoregressive conditional heteroscedasticity models, more primitive conditions are deduced to obtain the asymptotic properties. For illustration, a simulation study and a real data analysis are provided.

Original languageEnglish (US)
Pages (from-to)700-720
Number of pages21
JournalScandinavian Journal of Statistics
Issue number3
StatePublished - 2016


  • ARMA-AGARCH models, asymptotic normality, conditional autoregressive value-at-risk models, conditional location-scale time series models, identifiability condition, quantile regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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