A Mixed Historical Formula to forecast volatility

Roberto Ferulano ()
Additional contact information
Roberto Ferulano: Finance and Statistics, University of Perugia

Journal of Asset Management, 2009, vol. 10, issue 2, No 5, 124-136

Abstract: Abstract This study presents a new methodology for forecasting volatility. It relies on a weighted mean of short and long estimates of variance, based on a Moving Average framework. The quality of the predictions obtained with the proposed formula was checked with both simulated and real data. When applied to the analysis of simulated data, the new formula provides the least reliable forecast when a Random Walk is used as Data Generating Process (DGP) and the forecast variance is a simple Moving Average. This is also the case when the DGP belongs to the ARCH model family and the associated forecast formula is used. However, compared to existing approaches, the new methodology allows for the most reliable forecast on 5-day and 20-day horizons, when it is applied to Index, Fixed Income and Foreign Exchange data series.

Keywords: volatility forecasting; GARCH models; evaluating forecasts; non-parametric methods; exponential smoothing (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1057/jam.2009.2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:assmgt:v:10:y:2009:i:2:d:10.1057_jam.2009.2

Ordering information: This journal article can be ordered from
http://www.springer.com/finance/journal/41260

DOI: 10.1057/jam.2009.2

Access Statistics for this article

Journal of Asset Management is currently edited by Marielle de Jong and Dan diBartolomeo

More articles in Journal of Asset Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().