期刊
JOURNAL OF ECONOMETRICS
卷 202, 期 1, 页码 1-17出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2017.09.003
关键词
Conditional heteroscedasticity; GARCH model; Generalized quasi-maximum likelihood estimator; Heteroscedasticity; Portmanteau test; Stability test; Top Lyapunov exponent; Zero-drift GARCH model
资金
- Tsinghua University [53330230117]
- NSFC [11401337, 11401123, 11571348, 11371354, 11690014, 11731015, 71532013]
- Seed Fund for Basic Research [201611159233]
- Key Laboratory of RCSDS, Chinese Academy of Sciences
- Hong Kong Research Grants Commission [GRF 16500915, 16307516, 16500117]
This paper proposes a first-order zero-drift GARCH (ZD-GARCH(1, 1)) model to study conditional heteroscedasticity and heteroscedasticity together. Unlike the classical GARCH model, the ZD-GARCH(1, 1) model is always non-stationary regardless of the sign of the Lyapunov exponent gamma(0), but interestingly it is stable with its sample path oscillating randomly between zero and infinity over time when gamma(0) = 0. Furthermore, this paper studies the generalized quasi-maximum likelihood estimator (GQMLE) of the ZD-GARCH(1, 1) model, and establishes its strong consistency and asymptotic normality. Based on the GQMLE, an estimator for gamma(0), a t-test for stability, a unit root test for the absence of the drift term, and a portmanteau test for model checking are all constructed. Simulation studies are carried out to assess the finite sample performance of the proposed estimators and tests. Applications demonstrate that a stable ZD-GARCH(1, 1) model is more appropriate than a non-stationary GARCH(1, 1) model in fitting the KV-A stock returns in Francq and Zakoian (2012). (C) 2017 Elsevier B.V. All rights reserved.
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