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When Are Stocks Less Volatile in the Long Run?

Published online by Cambridge University Press:  26 October 2020

Eric Jondeau ,
Qunzi Zhang  and
Xiaoneng Zhu Open the ORCID record for Xiaoneng Zhu [Opens in a new window]
Eric Jondeau
Affiliation:
Swiss Finance Institute and University of Lausanneeric.jondeau@unil.ch
Qunzi Zhang
Affiliation:
Shandong University School of Economicsqunzi.zhang@sdu.edu.cn
Xiaoneng Zhu*
Affiliation:
Shanghai University of Finance and Economics School of Finance and Shanghai Institute of International Finance and Economicsxiaonengz@gmail.com
*
xiaonengz@gmail.com (corresponding author)
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Abstract

Pástor and Stambaugh (2012) find that from a forward-looking perspective, stocks are more volatile in the long run than they are in the short run. We demonstrate that when the nonnegative equity premium (NEP) condition is imposed on predictive regressions, stocks are in fact less volatile in the long run, even after taking estimation risk and uncertainties into account. The reason is that the NEP provides an additional parameter identification condition and prior information for future returns. Combined with the mean reversion of stock returns, this condition substantially reduces uncertainty on future returns and leads to lower long-run predictive variance.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 56 , Issue 4 , June 2021 , pp. 1228 - 1258
Copyright
© THE AUTHOR(S), 2020. PUBLISHED BY CAMBRIDGE UNIVERSITY PRESS ON BEHALF OF THE MICHAEL G. FOSTER SCHOOL OF BUSINESS, UNIVERSITY OF WASHINGTON

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Footnotes

Zhang acknowledges financial support from the Natural Science Foundation of China (No. 71903112, 71873079), National Social Science Fund (No. 18ZDA078), China Postdoctoral Science Foundation (No. 2020T130380), and Shandong University Research Fund (No. IFYT19001). Zhu acknowledges financial support from the Natural Science Foundation of China (No. 71473281), Shanghai University of Finance and Economics (SUFE) (No. 2018110698), and Shanghai Institute of International Finance and Economics (SIIFE) (No. 2018110262). The order of authors’ names is alphabetical.

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