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

作者：Zhu, Xiaorui^*; Xie, Li

来源：Communications in Statistics - Theory and Methods, 2016, 45(20): 6102-6111.

DOI：10.1080/03610926.2014.957852

• Adaptive estimator
• GARCH model
• Heavy-tailed error
• Quasi likelihood
• C13
• C22

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 (f) 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.

• 出版日期2016
• 单位北京工业大学