Title: Non-standard inference for augmented double autoregressive models with null volatility coefficients
Authors: Ke Zhu - University of Hong Kong (Hong Kong) [presenting]
Abstract: An augmented double autoregressive (DAR) model is considered, which allows null volatility coefficients to circumvent the over-identification problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical inference methods based on the Gaussian quasi-maximum likelihood estimation (GQMLE) become non-standard, and their asymptotics require the data to have a finite sixth moment, which narrows applicable scope in studying heavy-tailed data. To overcome this deficiency, a systematic statistical inference procedure is developed based on the self-weighted GQMLE for the augmented DAR model. The entire procedure is valid as long as the data is stationary, and its usefulness is illustrated by simulation studies and one real example.