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

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  • Torben G. Andersen
  • Tim Bollerslev
  • Xin Huang

    ()
    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

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.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2007-14.

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Length: 31
Date of creation: 16 Aug 2007
Date of revision:
Handle: RePEc:aah:create:2007-14

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Stochastic Volatility; Realized Variation; Bipower Variation; Jumps; Hazard Rates; Overnight Volatility;

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Citations

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Cited by:
  1. Mihaela Craioveanu & Eric Hillebrand, 2012. "Why It Is Ok To Use The Har-Rv(1,5,21) Model," Working Papers, University of Central Missouri, Department of Economics & Finance 1201, University of Central Missouri, Department of Economics & Finance, revised Aug 2012.
  2. Stefano Grassi & Nima Nonejad & Paolo Santucci de Magistris, 2014. "Forecasting with the Standardized Self-Perturbed Kalman Filter," Studies in Economics, Department of Economics, University of Kent 1405, Department of Economics, University of Kent.
  3. Fulvio Corsi & Francesco Audrino, 2008. "Modeling Tick-by-Tick Realized Correlations," University of St. Gallen Department of Economics working paper series 2008, Department of Economics, University of St. Gallen 2008-05, Department of Economics, University of St. Gallen.
  4. Jozef Barunik & Lukas Vacha, 2012. "Realized wavelet-based estimation of integrated variance and jumps in the presence of noise," Papers 1202.1854, arXiv.org, revised Feb 2013.
  5. Jozef Barunik & Lukas Vacha, 2012. "Modeling and forecasting exchange rate volatility in time-frequency domain," Papers 1204.1452, arXiv.org, revised Aug 2013.
  6. Patton, Andrew J., 2011. "Data-based ranking of realised volatility estimators," Journal of Econometrics, Elsevier, Elsevier, vol. 161(2), pages 284-303, April.
  7. Chun Liu & John M Maheu, 2007. "Are there Structural Breaks in Realized Volatility?," Working Papers, University of Toronto, Department of Economics tecipa-304, University of Toronto, Department of Economics.
  8. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2014. "Volatility jumps and their economic determinants," CREATES Research Papers, School of Economics and Management, University of Aarhus 2014-27, School of Economics and Management, University of Aarhus.
  9. Julien Chevallier & Benoît Sévi, 2011. "On the volatility-volume relationship in energy futures markets using intraday data," EconomiX Working Papers, University of Paris West - Nanterre la Défense, EconomiX 2011-16, University of Paris West - Nanterre la Défense, EconomiX.
  10. Corsi, Fulvio & Fusari, Nicola & La Vecchia, Davide, 2013. "Realizing smiles: Options pricing with realized volatility," Journal of Financial Economics, Elsevier, Elsevier, vol. 107(2), pages 284-304.
  11. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, Elsevier, vol. 29(1), pages 28-42.
  12. Liao, Yin, 2013. "The benefit of modeling jumps in realized volatility for risk prediction: Evidence from Chinese mainland stocks," Pacific-Basin Finance Journal, Elsevier, Elsevier, vol. 23(C), pages 25-48.
  13. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers, School of Economics and Management, University of Aarhus 2013-03, School of Economics and Management, University of Aarhus.
  14. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, Elsevier, vol. 37(11), pages 4381-4403.
  15. Golosnoy, Vasyl & Hamid, Alain & Okhrin, Yarema, 2014. "The empirical similarity approach for volatility prediction," Journal of Banking & Finance, Elsevier, Elsevier, vol. 40(C), pages 321-329.

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