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A Reduced Form Framework for Modeling Volatility of Speculative Prices based on Realized Variation Measures

Torben Andersen, Tim Bollerslev () and Xin Huang ()
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Xin Huang: School of Economics and Management, University of Aarhus, Denmark and CREATES, Postal: 8000 Aarhus C, Denmark

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

Abstract: Building on realized variance and bi-power variation measures constructed from high-frequency financial prices, we propose a simple reduced form framework for effectively incorporating intraday data into the modeling of daily return volatility. We decompose the total daily return variability into the continuous sample path variance, the variation arising from discontinuous jumps that occur during the trading day, as well as the overnight return variance. Our empirical results, based on long samples of high-frequency equity and bond futures returns, suggest that the dynamic dependencies in the daily continuous sample path variability is well described by an approximate long-memory HAR-GARCH model, while the overnight returns may be modelled by an augmented GARCH type structure. The dynamic dependencies in the non-parametrically identified significant jumps appear to be well described by the combination of an ACH model for the time-varying jump intensities coupled with a relatively simple log-linear structure for the jump sizes. Lastly, we discuss how the resulting reduced form model structure for each of the three components may be used in the construction of out-of-sample forecasts for the total return volatility.

Keywords: Stochastic Volatility; Realized Variation; Bipower Variation; Jumps; Hazard Rates; Overnight Volatility (search for similar items in EconPapers)
JEL-codes: C1 C2 G1 (search for similar items in EconPapers)
Pages: 31
Date: 2007-08-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
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Journal Article: A reduced form framework for modeling volatility of speculative prices based on realized variation measures (2011) Downloads
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