• Graduate program
    • Why Tinbergen Institute?
    • Program Structure
    • Courses
    • Course Registration
    • Facilities
    • Admissions
    • Recent PhD Placements
  • Research
  • News
  • Events
    • Summer School
      • Summer School
      • Behavioral Macro and Complexity
      • Climate Change
      • Econometrics and Data Science Methods for Business, Economics and Finance
      • Networks in Micro- and Macroeconomics
      • Business Data Science Summer School Program
    • Events Calendar
    • Events Archive
    • Tinbergen Institute Lectures
    • Conference: Consumer Search and Markets
    • Annual Tinbergen Institute Conference
  • Summer School
  • Alumni
  • Times

Boswijk, H. and Klaassen, F. (2012). Why frequency matters for unit root testing in financial time series Journal of Business and Economic Statistics, 30(3):351--357.


  • Journal
    Journal of Business and Economic Statistics

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.