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arXiv · 1907.04147

Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model

Feiyu Jiang·Dong Li·Ke Zhu
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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.

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Feiyu Jiang, Dong Li, Ke Zhu. 2019-07-09. Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model. https://arxiv.org/abs/1907.04147

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