Testing for Long Memory in Potentially Nonstationary Perturbed Fractional Processes
提出了检验平稳和非平稳时间序列中长期记忆性的新方法，基于半参数估计量和自相似性，通过模拟和实证（汇率和股票波动率）验证了有效性。
In this article, we propose new tests for long memory in stationary and nonstationary time series possibly perturbed by short-run noise. The tests are all based on semiparametric estimators and exploit the self-similarity property of long memory processes. We offer simulation results that show good size properties of the tests, with power against spurious long memory. To improve the potential size distortion in small samples from using temporal aggregation we use a bootstrap procedure. An empirical study of daily log-squared returns series of exchange rates and DJIA30 stocks shows that indeed there is long memory in exchange rate volatility and stock return volatility.