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dc.contributor.authorWornowizki, Max
dc.contributor.authorMeintanis, Simos G.
dc.contributor.authorFried, Roland
dc.date.accessioned2017-03-06T13:29:16Z
dc.date.available2017-03-06T13:29:16Z
dc.date.issued2017
dc.identifier.citationWornowizki, M. et al. 2017. Fourier methods for analyzing piecewise constant volatilities. AStA advances in statistical analysis, 101(3):289-308. [https://doi.org/10.1007/s10182-017-0288-1]en_US
dc.identifier.issn1863-8171
dc.identifier.issn1863-818X (Online)
dc.identifier.urihttp://hdl.handle.net/10394/20720
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs10182-017-0288-1
dc.identifier.urihttps://doi.org/10.1007/s10182-017-0288-1
dc.description.abstractWe 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 parametersen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectChange point analysisen_US
dc.subjectVarianceen_US
dc.subjectPiecewise identical distributionen_US
dc.subjectIndependenceen_US
dc.subjectWeight functionen_US
dc.titleFourier methods for analyzing piecewise constant volatilitiesen_US
dc.typeArticleen_US
dc.contributor.researchID21262977 - Meintanis, Simos George


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