Economics at your fingertips  

Realised variance forecasting under Box-Cox transformations

Nick Taylor ()

International Journal of Forecasting, 2017, vol. 33, issue 4, 770-785

Abstract: This paper assesses the benefits of modeling Box-Cox transformed realised variance data. In particular, it examines the quality of realised variance forecasts with and without this transformation applied in an out-of-sample forecasting competition. Using various realised variance measures, data transformations, volatility models and assessment methods, and controlling for data mining issues, the results indicate that data transformations can be economically and statistically significant. Moreover, the quartic root transformation appears to be the most effective in this regard. The conditions under which the use of transformed data is effective are identified.

Keywords: Volatility; Risk; Forecasting competitions; Loss function; Reality check (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

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:

DOI: 10.1016/j.ijforecast.2017.04.001

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2020-03-29
Handle: RePEc:eee:intfor:v:33:y:2017:i:4:p:770-785