Components of intraday volatility and their prediction at different sampling frequencies with application to DAX and BUND futures

Klaus Herrmann, Stefan Teis and Weijun Yu

No 15/2014, FAU Discussion Papers in Economics from Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics

Abstract: The adjusted measure of realized volatility suggested in [20] is applied to high- frequency orderbook and transaction data of DAX and BUND futures from EU- REX in order to identify the drivers of intraday volatility. Four components are identified to have predictive power: an auto-regressive pattern, a seasonal pattern, long-term memory and scheduled data releases. These components are analyzed in detail. Some evidence for two additional components, market microstrucuture events and unscheduled news, is given. Depending on the sampling frequency we estimate that between one and two thirds of the variation in realized volatility can be predicted by a simple linear model based on the components identified. It is shown how the predictive power of the different components depends on sampling frequencies.

Keywords: volatility; realized variance; intraday seasonality; volatility prediction; high-frequency data; tick data; fractional integration; sampling frequency (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-mst and nep-rmg
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:iwqwdp:152014

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