The DOI matches its Crossref record.
Measuring and forecasting S&P 500 index‐futures volatility using high‐frequency data
Abstract
In the 24‐hr foreign exchange market, Andersen and Bollerslev measure and forecast volatility using intraday returns rather than daily returns. Trading in equity markets only occurs during part of the day, and volatility during nontrading hours may differ from the volatility during trading hours. This paper compares various measures and forecasts of volatility in equity markets. In the absence of overnight trading it is shown that the daily volatility is best measured by the sum of intraday squared 5‐min returns, excluding the overnight return. In the absence of overnight trading, the best daily forecast of volatility is produced by modeling overnight volatility differently from intraday volatility. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:497–518, 2002
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A forecast comparison of volatility models: does anything beat a GARCH(1,1)?
Hansen, P. R., & Lunde, A. (2005) · Journal of Applied Econometrics · doi:10.1002/jae.800 · Cited by 1,773
Deep learning and time series-to-image encoding for financial forecasting
Barra, S., Carta, S. M., Corriga, A., Podda, A. S., & Recupero, D. R. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003132 · Cited by 267
Metadata
- Authors
- Martens, M.
- Venue
- Journal of Futures Markets
- Publisher
- Wiley
- Published
- 2002-04-03
- Volume
- 22
- Issue
- 6
- Pages
- 497-518
- ISSN
- 0270-7314 (ISSN-L)
- 1096-9934
- OpenAlex
- W2074310789
- Cited by
- 237
- References
- 0
- FWCI
- 10.38
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Measuring and forecasting S&P 500 index‐futures volatility using high‐frequency data. https://app.cochat.ai/papers/10.1002/fut.10016
- The paper
- Martens, M. (2002). Measuring and forecasting S&P 500 index‐futures volatility using high‐frequency data. Journal of Futures Markets. https://doi.org/10.1002/fut.10016
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
