Consistent high-precision volatility from high-frequency data
Fulvio Corsi (),
Ulrich Müller and
Additional contact information
Gilles Zumbach: Olsen & Associates
Ulrich Müller: Olsen & Associates
Finance from University Library of Munich, Germany
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.
Keywords: volatility; high-frequency data; foreign exchange (search for similar items in EconPapers)
JEL-codes: G (search for similar items in EconPapers)
Pages: 17 pages
New Economics Papers: this item is included in nep-ets and nep-fin
Note: Type of Document - pdf; pages: 17
References: Add references at CitEc
Citations: View citations in EconPapers (21) Track citations by RSS feed
Downloads: (external link)
Journal Article: Consistent High-precision Volatility from High-frequency Data (2001)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:wpa:wuwpfi:0407005
Access Statistics for this paper
More papers in Finance from University Library of Munich, Germany
Bibliographic data for series maintained by EconWPA ().