Adaptive quasi-maximum likelihood estimation of GARCH models with Student’s t likelihood

Xiaorui Zhu and Li Xie

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 20, 6102-6111

Abstract: This paper proposes an adaptive quasi-maximum likelihood estimation (QMLE) when forecasting the volatility of financial data with the generalized autoregressive conditional heteroscedasticity (GARCH) model. When the distribution of volatility data is unspecified or heavy-tailed, we worked out adaptive QMLE based on data by using the scale parameter ηf to identify the discrepancy between wrongly specified innovation density and the true innovation density. With only a few assumptions, this adaptive approach is consistent and asymptotically normal. Moreover, it gains better efficiency under the condition that innovation error is heavy-tailed. Finally, simulation studies and an application show its advantage.

Date: 2016
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DOI: 10.1080/03610926.2014.957852

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