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Cited by 499

The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada

Karolyi, G. Andrew·1995·Journal of Business & Economic Statistics

Abstract

This study examines the short-run dynamics of returns and volatility for stocks traded on the New York and Toronto stock exchanges. The main finding is that inferences about the magnitude and persistence of return innovations that originate in either market and that transmit to the other market depend importantly on how the cross-market dynamics in volatility are modeled. Moreover, much weaker cross-market dynamics in returns and volatility prevail during later subperiods and especially for Canadian stocks with shares dually listed in New York. Implications for international asset pricing, hedging strategies, and regulatory policy are discussed.

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Crossref · 10.1080/07350015.1995.10524575

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Metadata

Authors
Karolyi, G. A.
Venue
Journal of Business & Economic Statistics
Published
1995-01
Volume
13
Issue
1
Pages
11-25
ISSN
  • 0735-0015 (ISSN-L)
  • 1537-2707
OpenAlex
W2131426928
Cited by
499
References
31
FWCI
9.76
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada. https://app.cochat.ai/papers/10.1080/07350015.1995.10524575
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Karolyi, G. A. (1995). A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada. Journal of Business & Economic Statistics. https://doi.org/10.1080/07350015.1995.10524575

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.