Quantile autoregressive conditional heteroscedasticity

doi:10.1093/jrsssb/qkad068

Quantile autoregressive conditional heteroscedasticity

2023-07-19

1

OA

AI

DOI：10.1093/jrsssb/qkad068

Original

Original request for help

Save

Abstract

En 中文

This article proposes a novel conditional heteroscedastic time series model by applying the framework of quantile regression processes to the ARCH(& INFIN;) form of the GARCH model. This model can provide varying structures for conditional quantiles of the time series across different quantile levels, while including the commonly used GARCH model as a special case. The strict stationarity of the model is discussed. For robustness against heavy-tailed distributions, a self-weighted quantile regression (QR) estimator is proposed. While QR performs satisfactorily at intermediate quantile levels, its accuracy deteriorates at high quantile levels due to data scarcity. As a remedy, a self-weighted composite quantile regression estimator is further introduced and, based on an approximate GARCH model with a flexible Tukey-lambda distribution for the innovations, we can extrapolate the high quantile levels by borrowing information from intermediate ones. Asymptotic properties for the proposed estimators are established. Simulation experiments are carried out to access the finite sample performance of the proposed methods, and an empirical example is presented to illustrate the usefulness of the new model.

Keywords:

composite quantile regression

conditional quantile estimation

GARCH model

strict stationarity

Tukey-lambda distribution

Quantile autoregressive conditional heteroscedasticity

Abstract

Journal

Organization