Long Memory and Fractional Integration in High Frequency Financial Time Series
AbstractThis paper analyses the long-memory properties of high frequency financial time series. It focuses on temporal aggregation and the influence that this might have on 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. This holds for all four series examined, namely Open, High, Low and Last observations for the British pound/US dollar spot exchange rate.
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Bibliographic InfoPaper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 1016.
Length: 25 p.
Date of creation: 2010
Date of revision:
High frequency data; long memory; volatility persistence; structural breaks;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-06-18 (All new papers)
- NEP-CBA-2010-06-18 (Central Banking)
- NEP-ETS-2010-06-18 (Econometric Time Series)
- NEP-IFN-2010-06-18 (International Finance)
- NEP-MST-2010-06-18 (Market Microstructure)
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- Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, Elsevier, vol. 37(12), pages 4943-4957.
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