Predicting financial volatility: High‐frequency time‐series forecasts vis‐à‐vis implied volatility

Martin Martens and Jason Zein

Journal of Futures Markets, 2004, vol. 24, issue 11, 1005-1028

Abstract: Recent evidence suggests option implied volatilities provide better forecasts of financial volatility than time‐series models based on historical daily returns. In this study both the measurement and the forecasting of financial volatility is improved using high‐frequency data and long memory modeling, the latest proposed method to model volatility. This is the first study to extract results for three separate asset classes, equity, foreign exchange, and commodities. The results for the S&P 500, YEN/USD, and Light, Sweet Crude Oil provide a robust indication that volatility forecasts based on historical intraday returns do provide good volatility forecasts that can compete with and even outperform implied volatility. © 2004 Wiley Periodicals, Inc. Jrl Fut Mark 24:1005–1028, 2004

Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (49)

Downloads: (external link)
http://hdl.handle.net/

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:wly:jfutmk:v:24:y:2004:i:11:p:1005-1028

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0270-7314

Access Statistics for this article

Journal of Futures Markets is currently edited by Robert I. Webb

More articles in Journal of Futures Markets from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().