Quantile Regression Estimator for GARCH Models

Sangyeol Lee, Jungsik Noh

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In this article, we study the quantile regression estimator for GARCH models. We formulate the quantile regression problem by a reparametrization method and verify that the obtained quantile regression estimator is strongly consistent and asymptotically normal under certain regularity conditions. We also present our simulation results and a real data analysis for illustration.

Original languageEnglish (US)
Pages (from-to)2-20
Number of pages19
JournalScandinavian Journal of Statistics
Volume40
Issue number1
DOIs
StatePublished - Mar 2013

Keywords

  • Argmin sequence
  • Asymptotic normality
  • Bracketing method
  • GARCH models
  • Non-convex optimization
  • Quantile regression
  • Reparametrization method
  • Strong consistency
  • Value at risk

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Quantile Regression Estimator for GARCH Models'. Together they form a unique fingerprint.

Cite this