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MIDAS and Dividend Growth Predictability: Revisiting the Excess Volatility Puzzle

Quaye, Enoch, Tunaru, Radu, Voukelatos, Nikolaos (2024) MIDAS and Dividend Growth Predictability: Revisiting the Excess Volatility Puzzle. Journal of Financial Research, . ISSN 0270-2592. E-ISSN 1475-6803. (doi:10.1111/jfir.12403) (KAR id:105702)

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http://doi.org/10.1111/jfir.12403

Abstract

We examine dividend growth predictability and the excess volatility puzzle across a large sample of international equity markets, using a mixed frequency data sampling (MIDAS) regression approach. We find that accounting for dividend seasonality under the MIDAS framework significantly improves dividend growth predictability, compared to simple regressions with annually aggregated data. Moreover, variance bounds tests that allow for non-stationary dividends consistently fail to reject the hypothesis of market efficiency across all countries. Our findings suggest that the common rejection of market efficiency in the previous literature is most likely driven by the annual aggregation of dividend data as well as by the assumption of stationary dividends.

Item Type: Article
DOI/Identification number: 10.1111/jfir.12403
Subjects: H Social Sciences > HG Finance
Divisions: Divisions > Kent Business School - Division > Department of Accounting and Finance
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Nikolaos Voukelatos
Date Deposited: 20 Apr 2024 15:32 UTC
Last Modified: 15 May 2024 13:59 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/105702 (The current URI for this page, for reference purposes)

University of Kent Author Information

Voukelatos, Nikolaos.

Creator's ORCID: https://orcid.org/0000-0001-8272-2901
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