Hybrid quantile estimation for asymmetric power GARCH models

TL;DR

This paper develops a novel quantile estimation method for asymmetric power GARCH models, including non-stationary cases, with theoretical properties and practical tests demonstrated through simulations and real data.

Contribution

It introduces a new monotonic transformation for quantile regression in asymmetric power GARCH models, including the first approach for non-stationary ARCH-type models.

Findings

Quantile estimators are asymptotically normal under stationarity and non-stationarity.

New tests for strict stationarity and asymmetry are proposed.

Method demonstrated effective through simulations and real data analysis.

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.