Hybrid quantile estimation for asymmetric power GARCH models

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

Journal of Econometrics, 2022, vol. 227, issue 1, 264-284

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.

Keywords: Asymmetric power GARCH; Asymmetry testing; Non-stationarity; Quantile estimation; Strict stationarity testing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:227:y:2022:i:1:p:264-284

DOI: 10.1016/j.jeconom.2020.05.005

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