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Semiparametric ARCH Models

Engle, Robert F., Gonzalez-Rivera, Gloriá·1991·Journal of Business & Economic Statistics

Abstract

This article introduces a semiparametric autoregressive conditional heteroscedasticity (ARCH) model that has conditional first and second moments given by autoregressive moving average and ARCH parametric formulations but a conditional density that is assumed only to be sufficiently smooth to be approximated by a nonparametric density estimator. For several particular conditional densities, the relative efficiency of the quasi-maximum likelihood estimator is compared with maximum likelihood under correct specification. These potential efficiency gains for a fully adaptive procedure are compared in a Monte Carlo experiment with the observed gains from using the proposed semiparametric procedure, and it is found that the estimator captures a substantial proportion of the potential. The estimator is applied to daily stock returns from small firms that are found to exhibit conditional skewness and kurtosis and to the British pound to dollar exchange rate.

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Metadata

Authors
Engle, R. F., & Gonzalez-Rivera, G.
Venue
Journal of Business & Economic Statistics
Published
1991-10
Volume
9
Issue
4
Pages
345-359
ISSN
  • 0735-0015 (ISSN-L)
  • 1537-2707
OpenAlex
W2055193196
Cited by
470
References
2
FWCI
20.98
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Semiparametric ARCH Models. https://app.cochat.ai/papers/10.1080/07350015.1991.10509863
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Engle, R. F., & Gonzalez-Rivera, G. (1991). Semiparametric ARCH Models. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.1991.10509863

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