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

Klaus Herrmann; Stefan Teis; Weijun Yu

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.

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Year of publication:	2014
Authors:	Herrmann, Klaus ; Teis, Stefan ; Yu, Weijun
Publisher:	Erlangen : Univ., Inst. für Wirtschaftspolitik und Quantitative Wirtschaftsforschung
Subject:	Volatility \| realized variance \| intraday seasonality \| volatility prediction \| high-frequency data \| tick data \| fractional integration \| sampling frequency \| Volatilität \| Zeitreihenanalyse \| Time series analysis \| Prognoseverfahren \| Forecasting model \| Stichprobenerhebung \| Sampling \| Deutschland \| Germany \| Schätzung \| Estimation \| Börsenkurs \| Share price \| ARCH-Modell \| ARCH model \| Index-Futures \| Index futures