Weighted type of quantile regression and its application

Xuejun Jiang*, Tian Xia, Dejun Xie

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


In this paper we introduce a weighted composite quantile regression (CQR) estimation approach and study its application in nonlinear models such as exponential models and ARCH type of models. The weighted CQR is augmented by using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the oracle maximum likelihood estimator(MLE) for a variety of error distributions including the normal, mixed-normal, Student's t, Cauchy distributions and etc, We also suggest an algorithm for fast implementation of the proposed methodology. Simulations are conducted to compare the performance of different estimators, and the proposed approach is used to analyze the daily S&P 500 Composite index, which endorse our theoretical results.

Original languageEnglish
JournalLecture Notes in Engineering and Computer Science
Issue numberJanuary
Publication statusPublished - 2014
Externally publishedYes
EventInternational MultiConference of Engineers and Computer Scientists, IMECS 2014 - Kowloon, Hong Kong
Duration: 12 Mar 201414 Mar 2014


  • Double threshold ARCH models
  • Extended interior algorithm
  • Oracle MLE
  • Weighted CQR


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