AStA Advances in Statistical Analysis

, Volume 101, Issue 3, pp 289–308 | Cite as

Fourier methods for analyzing piecewise constant volatilities

  • Max Wornowizki
  • Roland Fried
  • Simos G. Meintanis
Original Paper

Abstract

We develop procedures for testing whether a sequence of independent random variables has constant variance. If this is fulfilled, the modulus of a Fourier-type transformation of the volatility process is identically equal to one. Our approach takes advantage of this property considering a canonical estimator for the modulus under the assumption of piecewise identically distributed zero mean observations. Using blockwise variance estimation, we introduce several test statistics resulting from different weight functions. All of them are given by simple explicit formulae. We prove the consistency of the corresponding tests and compare them to alternative procedures on extensive Monte Carlo experiments. According to the results, our proposals offer fairly high power, particularly in the case of multiple structural breaks. They also allow for an adequate estimation of the change point positions. We apply our procedure to gold mining data and also briefly discuss how it can be modified to test for the stationarity of other distributional parameters.

Keywords

Change point analysis Variance Piecewise identical distribution Independence Weight function 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Max Wornowizki
    • 1
  • Roland Fried
    • 1
  • Simos G. Meintanis
    • 2
    • 3
  1. 1.Department of StatisticsTU Dortmund UniversityDortmundGermany
  2. 2.Department of EconomicsNational and Kapodistrian University of AthensAthensGreece
  3. 3.Unit for Business Mathematics and InformaticsNorth-West UniversityPotchefstroomSouth Africa

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