The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Conditional Heteroskedasticity in Asset Returns: A New Approach
Abstract
This paper introduces an ARCH model (exponential ARCH) that (1) allows correlation between returns and volatility innovations (an important feature of stock market volatility changes), (2) eliminates the need for inequality constraints on parameters, and (3) allows for a straightforward interpretation of the "persistence" of shocks to volatility. In the above respects, it is an improvement over the widely-used GARCH model. The model is applied to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987. Copyright 1991 by The Econometric Society.
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
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Cited by
By Force of Habit: A Consumption‐Based Explanation of Aggregate Stock Market Behavior
Campbell, J. Y., & Cochrane, J. H. (1999) · Journal of Political Economy · doi:10.1086/250059 · Cited by 5,210
Generalized additive models for location, scale and shape.
Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228
Simple technical trading rules and the stochastic properties of stock returns
BROCK, W., LAKONISHOK, J., & LeBARON, B. (1992) · The Journal of Finance · doi:10.1111/j.1540-6261.1992.tb04681.x · Cited by 2,301
Forecast Methods for Time Series Data: A Survey
Liu, Z., Zhu, Z., Gao, J., & Xu, C. (2021) · IEEE Access · doi:10.1109/access.2021.3091162 · Cited by 247
Maximum entropy autoregressive conditional heteroskedasticity model
Park, S. Y., & Bera, A. K. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.12.014 · Cited by 218
Hanousek, J., Kočenda, E., & Kutan, A. M. (2009) · Journal of Financial Stability · doi:10.1016/j.jfs.2008.01.003 · Cited by 133
A generalized dynamic conditional correlation model for portfolio risk evaluation
Billio, M., & Caporin, M. (2009) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2008.12.011 · Cited by 77
Hasanov, A. S., Burkhanov, A. U., Usmonov, B., Khajimuratov, N. S., & Khurramova, M. M. Q. (2024) · Energy · doi:10.1016/j.energy.2024.130535 · Cited by 43
Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index
Vo, N., & Ślepaczuk, R. (2022) · Entropy · doi:10.3390/e24020158 · Cited by 39
Large-scale and rooftop solar generation in the NEM: A tale of two renewables strategies
Mwampashi, M. M., Nikitopoulos, C. S., Rai, A., & Konstandatos, O. (2022) · Energy Economics · doi:10.1016/j.eneco.2022.106372 · Cited by 26
An empirical examination of volatility spillover between the Indian and US swap markets
Bhargava, V., Malhotra, D. K., Russel, P., & Singh, R. (2012) · International Journal of Emerging Markets · doi:10.1108/17468801211237054 · Cited by 10
Parker, P. A., Holan, S. H., & Wills, S. A. (2021) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2021.1925279 · Cited by 9
A note on the determinants of non-fungible tokens returns
Panagiotidis, T., & Papapanagiotou, G. (2025) · International Journal of Finance & Economics · doi:10.1002/ijfe.3008 · Cited by 7
Cointegrated Portfolios and Volatility modeling in the Cryptocurrency Market
Gabriel, S., & Kunst, R. M. (2024) · doi:10.2139/ssrn.4756389 · Cited by 2
Unpacking leverage and lagged effects: do digitally mature and complex firms outpace their rivals?
Daruwala, Z., Khan, F., & Ullah Jan, S. (2025) · Cogent Economics & Finance · doi:10.1080/23322039.2025.2577909 · Cited by 1
Metadata
- Authors
- Nelson, D. B.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1991-03
- Volume
- 59
- Issue
- 2
- Pages
- 347
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W1999814123
- Cited by
- 10,236
- References
- 1
- FWCI
- 104.88
- 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
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.
