The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Evidence of Nonlinearity in Daily Stock Returns
Abstract
This article applies a newly developed statistical technique to time series of daily rates of return of 15 common stocks. The technique involves estimating the bispectrum of the observed time series. The bispectrum is defined as the double Fourier transform of the third-order cumulant function. If the process generating rates of return is linear with independent innovations, then the skewness of the bispectrum will be constant. The article describes a test that can detect nonconstant skewness in the bispectrum. Hence if the test rejects constant skewness, a nonlinear process is implied. As a consequence, the test can distinguish between white noise and purely random noise. The results suggest that daily stock returns are generated by a nonlinear process.
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Cites
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Fama, E. F. (1965) · The Journal of Business · doi:10.1086/294743 · Cited by 8,664
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Cited by
Bispectrum estimation: A digital signal processing framework
Nikias, C. L., & Raghuveer, M. R. (1987) · Proceedings of the IEEE · doi:10.1109/proc.1987.13824 · Cited by 1,377
Chaos and Nonlinear Dynamics: Application to Financial Markets
HSIEH, D. A. (1991) · The Journal of Finance · doi:10.1111/j.1540-6261.1991.tb04646.x · Cited by 1,046
The evolution of stock market efficiency over time: A survey of the empirical literature
Lim, K.-P., & Brooks, R. (2011) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2009.00611.x · Cited by 410
Metadata
- Authors
- Hinich, M. J., & Patterson, D. M.
- Venue
- Journal of Business & Economic Statistics
- Publisher
- Informa UK Limited
- Published
- 1985-01
- Volume
- 3
- Issue
- 1
- Pages
- 69-77
- ISSN
- 0735-0015 (ISSN-L)
- 1537-2707
- OpenAlex
- W2036193989
- Cited by
- 288
- References
- 19
- FWCI
- 18.60
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
