Hybrid quantile estimation for asymmetric power GARCH models

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

Research output: Contribution to journalArticles


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
JournalJournal of Econometrics
Early online dateAug 2020
Publication statusE-pub ahead of print - Aug 2020


Wang, G., Zhu, K., Li, G., & Li, W. K. (2020). Hybrid quantile estimation for asymmetric power GARCH models. Journal of Econometrics. Advance online publication. doi: 10.1016/j.jeconom.2020.05.005


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

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