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High-Frequency Tail Risk Premium and Stock Return Predictability

Published online by Cambridge University Press:  31 October 2023

Caio AlmeidaOpen the ORCID record for Caio Almeida[Opens in a new window],
Kym Ardison,
Gustavo Freire,
René Garcia and
Piotr Orłowski
Caio Almeida*
Affiliation:
Princeton University Department of Economics and Bendheim Center for Finance
Kym Ardison
Affiliation:
SPX Capital kymmarcel@gmail.com
Gustavo Freire
Affiliation:
Erasmus University Rotterdam, Tinbergen Institute and ERIM freire@ese.eur.nl
René Garcia
Affiliation:
Université de Montréal and Toulouse School of Economics rene.garcia@umontreal.ca
Piotr Orłowski
Affiliation:
Department of Finance, HEC Montréal piotr.orlowski@hec.ca
*
calmeida@princeton.edu (corresponding author)
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Abstract

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We propose a novel measure of the market return tail risk premium based on minimum-distance state price densities recovered from high-frequency data. The tail risk premium extracted from intra-day S&P 500 returns predicts the market equity and variance risk premiums and expected excess returns on a cross section of characteristics-sorted portfolios. Additionally, we describe the differential role of the quantity of tail risk, and of the tail premium, in shaping the future distribution of index returns. Our results are robust to controlling for established measures of variance and tail risk, and of risk premiums, in the predictive models.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 59 , Issue 8 , December 2024, pp. 3633 - 3670
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

Footnotes

We thank an anonymous referee, Thierry Foucault (the editor), Rodrigo Hizmeri, seminar participants at the Kellogg School of Management, and conference participants at the 2017 SoFiE Conference, the 2017 Vienna-Copenhagen Conference on Financial Econometrics, and the 2018 IAAE Meeting for useful comments and suggestions. Ardison acknowledges financial support from ANBIMA and FAPERJ. Garcia thanks the NSERC, the SSHRC, and the FQRSC research grant agencies for their financial support. He is a TSE associate faculty and a research Fellow of CIRANO and CIREQ.

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