The DOI matches its Crossref record.
Power and Bipower Variation with Stochastic Volatility and Jumps
Abstract
This article shows that realized power variation and its extension, realized bipower variation, which we introduce here, are somewhat robust to rare jumps. We demonstrate that in special cases, realized bipower variation estimates integrated variance in stochastic volatility models, thus providing a model-free and consistent alternative to realized variance. Its robustness property means that if we have a stochastic volatility plus infrequent jumps process, then the difference between realized variance and realized bipower variation estimates the quadratic variation of the jump component. This seems to be the first method that can separate quadratic variation into its continuous and jump components. Various extensions are given, together with proofs of special cases of these results. Detailed mathematical results are reported in Barndorff-Nielsen and Shephard (2003a).
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
BOLLERSLEV, T., & TODOROV, V. (2011) · The Journal of Finance · doi:10.1111/j.1540-6261.2011.01695.x · Cited by 804
Threshold bipower variation and the impact of jumps on volatility forecasting
Corsi, F., Pirino, D., & Renò, R. (2010) · Journal of Econometrics · doi:10.1016/j.jeconom.2010.07.008 · Cited by 561
Bollerslev, T., Li, S. Z., & Todorov, V. (2016) · Journal of Financial Economics · doi:10.1016/j.jfineco.2016.02.001 · Cited by 180
Cointegrated Portfolios and Volatility modeling in the Cryptocurrency Market
Gabriel, S., & Kunst, R. M. (2024) · doi:10.2139/ssrn.4756389 · Cited by 2
Metadata
- Authors
- Barndorff-Nielsen, O. E.
- Venue
- Journal of Financial Econometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2004-12-01
- Volume
- 2
- Issue
- 1
- Pages
- 1-37
- ISSN
- 1479-8409 (ISSN-L)
- 1479-8417
- OpenAlex
- W2051235503
- Cited by
- 2,023
- References
- 0
- FWCI
- 72.56
- 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: Power and Bipower Variation with Stochastic Volatility and Jumps. https://app.cochat.ai/papers/10.1093/jjfinec/nbh001
- The paper
- Barndorff-Nielsen, O. E. (2004). Power and Bipower Variation with Stochastic Volatility and Jumps. Journal of Financial Econometrics. https://doi.org/10.1093/jjfinec/nbh001
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.