The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Long memory in continuous‐time stochastic volatility models
Abstract
This paper studies a classical extension of the Black and Scholes model for option pricing, often known as the Hull and White model. Our specification is that the volatility process is assumed not only to be stochastic, but also to have long‐memory features and properties. We study here the implications of this continuous‐time long‐memory model, both for the volatility process itself as well as for the global asset price process. We also compare our model with some discrete time approximations. Then the issue of option pricing is addressed by looking at theoretical formulas and properties of the implicit volatilities as well as statistical inference tractability. Lastly, we provide a few simulation experiments to illustrate our results.
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
Cites
The pricing of options and corporate liabilities
Black, F., & Scholes, M. (1973) · Journal of Political Economy · doi:10.1086/260062 · Cited by 29,074
Cited by
The Distribution of Realized Exchange Rate Volatility
Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001) · Journal of the American Statistical Association · doi:10.1198/016214501750332965 · Cited by 2,104
Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007) · Review of Economics and Statistics · doi:10.1162/rest.89.4.701 · Cited by 1,445
(2008) · Econometrica · doi:10.3982/ecta6495 · Cited by 1,228
Long memory and regime switching
Diebold, F. X., & Inoue, A. (2001) · Journal of Econometrics · doi:10.1016/s0304-4076(01)00073-2 · Cited by 1,152
Barndorff-Nielsen, O. E., & Shephard, N. (2004) · Econometrica · doi:10.1111/j.1468-0262.2004.00515.x · Cited by 905
Metadata
- Authors
- Comte, F., & Renault, E.
- Venue
- Mathematical Finance
- Publisher
- Wiley
- Published
- 1998-10
- Volume
- 8
- Issue
- 4
- Pages
- 291-323
- ISSN
- 0960-1627 (ISSN-L)
- 1467-9965
- OpenAlex
- W2013134717
- Cited by
- 750
- References
- 1
- FWCI
- 12.56
- 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: Long memory in continuous‐time stochastic volatility models. https://app.cochat.ai/papers/10.1111/1467-9965.00057
- The paper
- Comte, F., & Renault, E. (1998). Long memory in continuous‐time stochastic volatility models. Mathematical Finance. https://doi.org/10.1111/1467-9965.00057
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.