Hybrid quantile estimation for asymmetric power GARCH models

11/21/2019
by   Guochang Wang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/19/2019

A new asymmetric ε-insensitive pinball loss function based support vector quantile regression model

In this paper, we propose a novel asymmetric ϵ-insensitive pinball loss ...
research
03/06/2023

Quantile-Quantile Methodology – Detailed Results

The linear quantile-quantile relationship provides an easy-to-implement ...
research
04/21/2015

The adaptable buffer algorithm for high quantile estimation in non-stationary data streams

The need to estimate a particular quantile of a distribution is an impor...
research
10/21/2019

A ν- support vector quantile regression model with automatic accuracy control

This paper proposes a novel 'ν-support vector quantile regression' (ν-SV...
research
01/12/2020

Bayesian Quantile and Expectile Optimisation

Bayesian optimisation is widely used to optimise stochastic black box fu...
research
09/26/2022

Inter-order relations between moments of a Student t distribution, with an application to L_p-quantiles

This paper introduces inter-order formulas for partial and complete mome...
research
12/20/2022

Probabilistic quantile factor analysis

This paper extends quantile factor analysis to a probabilistic variant t...

Please sign up or login with your details

Forgot password? Click here to reset