On regression-based tests for persistence in logarithmic volatility models
讨论如何用回归检验法检验对数GARCH和随机波动率模型中条件方差的非平稳性，该方法易于实现、大样本分布明确，且比基于拟极大似然估计的检验对结构变化更不敏感。
Building on the work of Pantula (1986), this paper discusses how the hypothesis of conditional variance nonstationarity in the logarithmic family of generalized autoregressive conditional heteroskedasticity (GARCH) and stochastic volatility processes may be tested using regression-based tests. The latter are easy to implement, have well-defined large-sample distributions, and are less sensitive to structural changes than tests based on the quasimaximum likelihood estimator.