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In this paper, a comparative analysis of traditional and hybrid root finding algorithms is performed in estimating implied volatility for Ethereum Options using the Black–Scholes model. Results indicate the efficiency of Newton–Raphson method in terms of algorithmic convergence as well as computational time. Since Newton–Raphson method may not always lead to convergence, the best approximation technique is chosen from the convergent bracketed methods. The hybrid Bisection–Regula Falsi method serves as the best choice for root estimation among the bracketed methods under consideration.

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Subject
Newton-Raphson method;
Algorithms;
Bisection;
Convergence;
Comparative analysis;
Computing time;
Volatility;
Estimation;
Futures;
Monte Carlo simulation;
Hedging;
Institutional investments;
Derivatives;
Digital currencies;
Securities prices;
Stochastic models;
Methods;
Financial analysis
Business indexing term
Title
Comparative Analysis of Root Finding Algorithms for Implied Volatility Estimation of Ethereum Options
Author
Sapna, S. 1 ; Mohan, Biju R. 1   VIAFID ORCID Logo 

1  National Institute of Technology Karnataka, Department of Information Technology, Surathkal, India (GRID:grid.444525.6) (ISNI:0000 0000 9398 3798) 
Publication title
Computational Economics; Dordrecht
Volume
64
Issue
1
Pages
515-550
Publication year
2024
Publication date
Jul 2024
Publisher
Springer Nature B.V.
Place of publication
Dordrecht
Country of publication
Netherlands
Publication subject
Business And Economics, Business And Economics--Computer Applications
ISSN
09277099
e-ISSN
15729974
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2023-08-18
Milestone dates
2023-07-28 (Registration); 2023-07-27 (Accepted)
Publication history
 
 
   First posting date
18 Aug 2023
DOI
https://doi.org/10.1007/s10614-023-10446-8
ProQuest document ID
3098443690
Document URL
https://www.proquest.com/scholarly-journals/comparative-analysis-root-finding-algorithms/docview/3098443690/se-2?accountid=208611
Copyright
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Last updated
2025-11-07
Database
ProQuest One Academic