The economics of data: Using simple model-free volatility in a high-frequency world
John Garvey and
Liam Gallagher ()
The North American Journal of Economics and Finance, 2013, vol. 26, issue C, 370-379
This paper examines the practical implications of using high-frequency data in a fast and frugal manner. It recognises the continued widespread application of model free approaches within many trading and risk management functions. Our analysis of the relative characteristics of four model-free volatility estimates is framed around their relative long memory effects as measured by the feasible exact local Whittle estimator. For a cross-section of sixteen FTSE-100 stocks, for the period 1997–2007, we show that 5-min realized volatility exhibits a higher level of volatility persistence than approaches that use data in a sparse way (close-to-close volatility, high-low volatility and Yang & Zhang volatility). This observation is a useful decision-tool for a trading and risk management decisions that are undertaken in a time-constrained task environment. It recommends that the use of sparse data (open, high, low and closing price observations) requires trader intuition and judgement to build long-memory effects into their pricing.
Keywords: Economics of information; Model free volatility; High frequency data; Long memory effects (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
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:eee:ecofin:v:26:y:2013:i:c:p:370-379
Access Statistics for this article
The North American Journal of Economics and Finance is currently edited by Hamid Beladi
More articles in The North American Journal of Economics and Finance from Elsevier
Bibliographic data for series maintained by Haili He ().