Consistent high-precision volatility from high-frequency data

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Consistent high-precision volatility from high-frequency data

Author Info

Fulvio Corsi (Olsen & Associates)
Gilles Zumbach (Olsen & Associates)
Ulrich Müller (Olsen & Associates)
Michel Dacorogna (Zurich Re)

Additional information is available for the following registered author(s):

Michel M Dacorogna

Abstract

Estimates of daily volatility are investigated. Realized volatility can be computed from returns observed over time intervals of different sizes. For simple statistical reasons, volatility estimators based on high-frequency returns have been proposed, but such estimators are found to be strongly biased as compared to volatilities of daily returns. This bias originates from microstructure effects in the price formation. For foreign exchange, the relevant microstructure effect is the incoherent price formation, which leads to a strong negative first-order auto- correlation for tick-by-tick returns and to the volatility bias. On the basis of a simple theoretical model for foreign exchange data, the incoherent term can be filtered away from the tick-by-tick price series. With filtered prices, the daily volatility can be estimated using the information contained in high-frequency data, providing a high-precision measure of volatility at any time interval.

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Paper provided by EconWPA in its series Finance with number 0407005.

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Length: 17 pages
Date of creation: 06 Jul 2004
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Handle: RePEc:wpa:wuwpfi:0407005

Note: Type of Document - pdf; pages: 17
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G - Financial Economics

This paper has been announced in the following NEP Reports:

NEP-ALL-2004-07-11 (All new papers)
NEP-ETS-2004-07-11 (Econometric Time Series)
NEP-FIN-2004-07-11 (Finance)

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T. Di Matteo & T. Aste & Michel M. Dacorogna, 2004. "Using the Scaling Analysis to Characterize Financial Markets," Finance 0402014, EconWPA. [Downloadable!]
Jonathan Dark, 2004. "Long memory in the volatility of the Australian All Ordinaries Index and the Share Price Index futures," Monash Econometrics and Business Statistics Working Papers 5/04, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
T. Di Matteo & T. Aste & Michel M. Dacorogna, 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Econometrics 0503004, EconWPA. [Downloadable!]
Other versions:
- Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April. [Downloadable!] (restricted)
Jeannette H.C. Woerner, 2002. "Variational Sums and Power Variation: a unifying approach to model selection and estimation in semimartingale models," OFRC Working Papers Series 2002mf05, Oxford Financial Research Centre. [Downloadable!]
Torben G. Andersen & Tim Bollerslev & Francis X. Diebold,, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," CFS Working Paper Series 2003/35, Center for Financial Studies. [Downloadable!]
Other versions:
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2003. "Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility," PIER Working Paper Archive 03-025, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Sep 2003. [Downloadable!]
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Other versions:
- Eugenie Hol & Siem Jan Koopman & Borus Jungbacker, 2004. "Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements," Computing in Economics and Finance 2004 342, Society for Computational Economics.
- Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June. [Downloadable!] (restricted)
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Ole E. Barndorff-Nielsen & Neil Shephard, 2001. "Realised power variation and stochastic volatility models," Economics Papers 2001-W18, Economics Group, Nuffield College, University of Oxford. [Downloadable!]
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Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," Center for Financial Institutions Working Papers 02-27, Wharton School Center for Financial Institutions, University of Pennsylvania. [Downloadable!]
Other versions:
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2002. "Parametric and Nonparametric Volatility Measurement," NBER Technical Working Papers 0279, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
Marcel Dettling & Peter Bühlmann, 2004. "Volatility and risk estimation with linear and nonlinear methods based on high frequency data," Applied Financial Economics, Taylor and Francis Journals, vol. 14(10), pages 717-729, June. [Downloadable!] (restricted)
Fulvio Corsi & Uta Kretschmer & Stefan Mittnik & Christian Pigorsch, 2005. "The Volatility of Realized Volatility," CFS Working Paper Series 2005/33, Center for Financial Studies. [Downloadable!]

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