Dr John Fry J.M.Fry@hull.ac.uk
Senior Lecturer in Applied Mathematics
A subjective probability argument suggests vote-share estimates from polling companies can be interpreted as market prices. The corresponding election constitutes the price at a known future date. This makes an options-pricing approach particularly attractive. In this setting vote-share estimates, the probability of winning the popular vote and the second-round qualification probability all have a convenient representation in terms of binary options prices. In this paper we develop options-pricing, vote-transfer and Monte Carlo methods to forecast the French presidential election. The approach fits well with the proportional and regimented two-stage nature of the French election but applies more broadly. Unusually for a French system characterised by uncertainty and constant flux the incumbent President Macron appears in a dominant position throughout the 2017 and 2022 elections albeit with no chance of an outright win in the first round.
Fry, J., Hastings, T., & Binner, J. (online). An options-pricing approach to forecasting the French presidential election. Journal of the Operational Research Society, https://doi.org/10.1080/01605682.2024.2334339
Journal Article Type | Article |
---|---|
Acceptance Date | Mar 11, 2024 |
Online Publication Date | Apr 8, 2024 |
Deposit Date | Mar 11, 2024 |
Publicly Available Date | Apr 9, 2025 |
Journal | Journal of the Operational Research Society |
Print ISSN | 0160-5682 |
Publisher | Taylor and Francis |
Peer Reviewed | Peer Reviewed |
DOI | https://doi.org/10.1080/01605682.2024.2334339 |
Keywords | Forecasting; Finance; OR in societal problem analysis; Options Pricing; Politics |
Public URL | https://hull-repository.worktribe.com/output/4586538 |
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© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
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