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Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model

Feiyu Jiang, Dong Li, Ke Zhu

arXiv 9 Jul 2019 · Statistics — Methodology · publishedJournal of Econometrics (2020) · 3 citations (OpenAlex)

arXiv:1907.04147 · PDF · DOI · OpenAlex · Extracted main text

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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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Hafner, C.M., Linton, O (2010) Efficient estimation of a multivariate multiplicative volatility model1.000126100%
2Truquet, L (2017) Parameter stability and semiparametric inference in time varying auto-regressive conditional heteroscedasticity models1.000104100%
3Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity1.00064100%
4Francq, C., Horváth, L., Zakoïan, J.-M (2011) Merits and drawbacks of variance targeting in GARCH models1.00064100%
5Patilea, V., Raïssi, H (2014) Testing second-order dynamics for autoregressive processes in presence of time-varying variance1.00053100%
6Fryzlewicz, P., Sapatinas, T., Subba Rao, S (2008) Normalized least-squares estimation in time-varying ARCH models0.81142100%
7Hong, Y., Wang, X. and Wang, S (2017) Testing strict stationarity with applications to macroeconomic time series0.73732100%
8Francq, C., Zakoïan, J.-M (2004) Maximum likelihood estimation of pure GARCH and ARMA-GARCH processes0.6443267%
9Dahlhaus, R., Subba Rao, S (2006) Statistical inference for time-varying ARCH processes0.64422100%
10Engle, R.F (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation0.64422100%

Showing the top 10 of 44 scored citations.