Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model
Abstract
This paper considers a semiparametric generalized autoregressive conditional heteroskedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type short run component by the quasi maximum likelihood estimator (QMLE). We show that the QMLE is asymptotically normal with the parametric convergence rate. Next, we construct a Lagrange multiplier test for linear parameter constraint and a portmanteau test for model checking, and obtain their asymptotic null distributions. Our entire statistical inference procedure works for the non-stationary data with two important features: first, our QMLE and two tests are adaptive to the unknown form of the long run component; second, our QMLE and two tests share the same efficiency and testing power as those in variance targeting method when the S-GARCH model is stationary.
Cite
@article{arxiv.1907.04147,
title = {Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model},
author = {Feiyu Jiang and Dong Li and Ke Zhu},
journal= {arXiv preprint arXiv:1907.04147},
year = {2020}
}