Volatility Forecasts Embedded in the Prices of Crude-Oil Options


This paper evaluates the ability of alternative option-implied volatility measures to forecast crude-oil return volatility. We find that a corridor implied volatility measure that aggregates information from a narrow range of option contracts consistently outperforms forecasts obtained by the popular Black–Scholes and model-free volatility expectations, as well as those generated by a realized volatility model. This measure ranks favorably in regression-based tests, delivers the lowest forecast errors under different loss functions, and generates economically significant gains in volatility timing exercises. Our results also show that the Chicago Board Options Exchange's “oil-VIX” index performs poorly, as it routinely produces the least accurate forecasts.

Publication DOI: https://doi.org/10.1002/fut.22114
Divisions: College of Business and Social Sciences > Aston Business School > Economics, Finance & Entrepreneurship
College of Business and Social Sciences > Aston Business School
Additional Information: © 2020 The Authors. The Journal of Futures Markets published by Wiley Periodicals, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited
Uncontrolled Keywords: option-implied volatility,realized variance,volatility forecasting,Accounting,Business, Management and Accounting(all),Finance,Economics and Econometrics
Publication ISSN: 1096-9934
Last Modified: 11 Jun 2024 07:15
Date Deposited: 06 Dec 2019 11:54
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Related URLs: https://onlinel ... .1002/fut.22114 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-07-01
Published Online Date: 2020-04-13
Accepted Date: 2019-12-01
Authors: Tsiaras, Leonidas (ORCID Profile 0000-0002-4154-4210)
Gilder, Dudley



Version: Accepted Version

Access Restriction: Restricted to Repository staff only


Version: Published Version

License: Creative Commons Attribution

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