Robust Test of Persistence Change in Heavy-Tailed Time Series Environment

In this paper, we study the persistence change problem of heavy-tailed observations with infinite variance by constructing a Ratio two-sided test statistic based on M-estimates. It is shown that the asymptotic distribution of the statistic under the null hypothesis is functional for Brownian motion, which is independent of the tail index, and its consistency is established under the alternative hypothesis. The Bootstrap sampling method is used to approximate the asymptotic distribution to obtain the accurate critical values. The numerical simulation results show that the Ratio test based on M-estimates has a satisfactory empirical size without significant distortion, and significantly improves empirical power compared to the test based on least squares estimate, particularly when the tail features of time series are thicker. Finally, a set of gold ETF volatility index data verifies the validity and feasibility of our proposed method.