Optimal Prediction Periods for New and Old Volatility Indexes in USA and German Markets
Javier Giner (),
Sandra Morini () and
Rafael Rosillo ()
Additional contact information
Javier Giner: University of La Laguna
Sandra Morini: University of La Laguna
Rafael Rosillo: University of León
Computational Economics, 2016, vol. 47, issue 4, No 2, 527-549
Abstract:
Abstract In 1993, the Chicago Board of Options Exchange (CBOE) introduced the VXO, a volatility index based on implied volatilities on S&P 100 index. In 2003, the CBOE changed their volatility index design and introduced the VIX in order to enhance its economic significance and to facilitate hedging. In this paper, using data from the USA and the German stock markets, we compare the forecasting capability of the volatility indexes with that of historical volatility and conditional volatility models. Following this analysis, we have studied whether it may be the case that volatility indexes forecast the realized volatilities more accurately for a different period to 30 (or 45) days, attempting to answer the question: what time horizon is the informational content of volatility indexes best adjusted for? The optimal prediction period of each volatility index (VXO, VIX, VDAX and V1X) in terms of coefficient of determination is analysed. The results identify a difference between the observed optimal forecasting period and the theoretical one. This could be explained from different perspectives such as the index’s design, investor cognitive bias or overreaction.
Keywords: VIX; VDAX; Forecasting; Realized volatility; Maturity (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s10614-015-9500-0 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:kap:compec:v:47:y:2016:i:4:d:10.1007_s10614-015-9500-0
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
DOI: 10.1007/s10614-015-9500-0
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().