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Hybrid quantile estimation for asymmetric power GARCH models

Author

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  • Wang, Guochang
  • Zhu, Ke
  • Li, Guodong
  • Li, Wai Keung

Abstract

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.

Suggested Citation

  • Wang, Guochang & Zhu, Ke & Li, Guodong & Li, Wai Keung, 2022. "Hybrid quantile estimation for asymmetric power GARCH models," Journal of Econometrics, Elsevier, vol. 227(1), pages 264-284.
    • Handle: RePEc:eee:econom:v:227:y:2022:i:1:p:264-284
    DOI: 10.1016/j.jeconom.2020.05.005
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    1. Zhu, Ke, 2023. "A new generalized exponentially weighted moving average quantile model and its statistical inference," Journal of Econometrics, Elsevier, vol. 237(1).

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