Why Frequency Matters for Unit Root Testing in Financial Time Series
证明，对于具有厚尾和波动率聚集特征的金融数据，采样频率会影响单位根检验的功效，频率越高，这些特征越强，从而带来检验功效的提升。
It is generally believed that the power of unit root tests is determined only by the time span of observations, not by their sampling frequency. We show that the sampling frequency does matter for stock data displaying fat tails and volatility clustering, such as financial time series. Our claim builds on recent work on unit root testing based on non-Gaussian GARCH-based likelihood functions. Such methods yield power gains in the presence of fat tails and volatility clustering, and the strength of these features increases with the sampling frequency. This is illustrated using local power calculations and an empirical application to real exchange rates.