Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/72627 
Year of Publication: 
2013
Series/Report no.: 
DIW Discussion Papers No. 1294
Publisher: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Abstract: 
This paper analyses the long-memory properties of a high-frequency financial time series dataset. It focuses on temporal aggregation and other features of the data, and how they might affect the degree of dependence of the series. Fractional integration or I(d) models are estimated with a variety of specifications for the error term. In brief, we find evidence that a lower degree of integration is associated with lower data frequencies. In particular, when the data are collected every 10 minutes there are several cases with values of d strictly smaller than 1, implying mean-reverting behaviour; however, for higher data frequencies the unit root null cannot be rejected. This holds for all four series examined, namely Open, High, Low and Last observations for the US dollar / British pound spot exchange rate and for different sample periods.
Subjects: 
high frequency data
long memory
volatility persistence
structural breaks
JEL: 
C22
F31
Document Type: 
Working Paper

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