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Temporal Aggregation of Garch Processes
Abstract
The authors derive low frequency, say weekly, models implied by high frequency, say daily, ARMA models with symmetric GARCH errors. They show that low frequency models exhibit conditional heteroskedasticity of the GARCH form as well. The parameters in the conditional variance equation of the low frequency model depend upon mean, variance, and kurtosis parameters of the corresponding high frequency model. Moreover, strongly consistent estimators of the parameters in the high frequency model can be derived from low frequency data. The common assumption in applications that rescaled innovations are independent is disputable, since it depends upon the available data frequency. Copyright 1993 by The Econometric Society.
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Cited by
TEMPORAL AGGREGATION OF UNIVARIATE AND MULTIVARIATE TIME SERIES MODELS: A SURVEY
Silvestrini, A., & Veredas, D. (2008) · Journal of Economic Surveys · doi:10.1111/j.1467-6419.2007.00538.x · Cited by 348
Metadata
- Authors
- Drost, F. C., & Nijman, T. E.
- Venue
- Econometrica
- Publisher
- JSTOR
- Published
- 1993-07
- Volume
- 61
- Issue
- 4
- Pages
- 909
- ISSN
- 0012-9682 (ISSN-L)
- OpenAlex
- W3124263521
- Cited by
- 676
- References
- 0
- FWCI
- 25.05
- Type
- journal article
- Language
- en
- Open access
- closed
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- Drost, F. C., & Nijman, T. E. (1993). Temporal Aggregation of Garch Processes. Econometrica. https://doi.org/10.2307/2951767
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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.
