Hybrid quantile estimation for asymmetric power GARCH models

Guochang WANG, Ke ZHU, Guodong LI, Wai Keung LI

Research output: Contribution to journalArticlespeer-review

3 Citations (Scopus)


Asymmetric power GARCH models have been widely used to study the higher order moments of financial returns, while their quantile estimation has been rarely investigated. This paper introduces a simple monotonic transformation on its conditional quantile function to make the quantile regression tractable. The asymptotic normality of the resulting quantile estimators is established under either stationarity or non-stationarity. Moreover, based on the estimation procedure, new tests for strict stationarity and asymmetry are also constructed. This is the first try of the quantile estimation for non-stationary ARCH-type models in the literature. The usefulness of the proposed methodology is illustrated by simulation results and real data analysis. Copyright © 2020 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)264-284
JournalJournal of Econometrics
Issue number1
Early online dateAug 2020
Publication statusPublished - Mar 2022


Wang, G., Zhu, K., Li, G., & Li, W. K. (2022). Hybrid quantile estimation for asymmetric power GARCH models. Journal of Econometrics, 277(1), 264-284. doi: 10.1016/j.jeconom.2020.05.005


  • Asymmetric power GARCH
  • Asymmetry testing
  • Non-stationarity
  • Quantile estimation
  • Strict stationarity testing


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