arbiter
JournalsPricingAccount

Modelling options on football players using individual rankings and club market value: evidence from Italy

Marco Cucculelli et al.

International Journal of Financial Markets and Derivatives2024https://doi.org/10.1504/ijfmd.2024.140655article
ABDC C
Weight
0.30

What the paper says

The paper develops a theoretical model for the valuation of options on players using individual player performance and club market value as value drivers. The paper also provides a test of the model by using data on individual rankings (from Squawka) to determine the value of players. For the team, the market value of listed clubs net of fixed assets is used. To account for the influence of player age on the ranking, the value is modelled using a mean reversion process. The model provides estimates that are consistent with the actual values on the transfer market in the case of three Italian football players. The empirical analysis is complemented by a predictive approach to check the robustness, generalisation and consistency of the proposed model.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijfmd.2024.140655

Or copy a formatted citation

@article{marco2024,
  title        = {{Modelling options on football players using individual rankings and club market value: evidence from Italy}},
  author       = {Marco Cucculelli et al.},
  journal      = {International Journal of Financial Markets and Derivatives},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1504/ijfmd.2024.140655},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Modelling options on football players using individual rankings and club market value: evidence from Italy

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.