IDEAS home Printed from https://ideas.repec.org/p/rtv/ceisrp/626.html

Pricing options on illiquid assets using liquid market benchmarks: an application to energy markets

Author

Listed:

Abstract

The Gasoil options market is illiquid, making it difficult to construct its implied volatility surface directly. However, it is closely linked to the highly liquid Brent options market. In this paper, we jointly model Brent and Gasoil futures prices through a correlated Bachelier local volatility model: the Brent factor is described by a normal mixture diffusion model, while the Gasoil-Brent spot volatility spread is estimated using a data-driven procedure that identifies clusters of historical crack-spread levels and Gasoil-Brent volatility spreads. The resulting bivariate model allows us to compute an implied volatility correction that maps Brent implied volatilities to Gasoil implied volatilities without using illiquid Gasoil option prices as inputs. Monte Carlo simulations demonstrate that the resulting implied volatilities closely match observed Gasoil implied volatilities when benchmarked against more direct approaches. These results suggest that the proposed framework is well suited for modeling refined products and pricing the corresponding financial derivatives.

Suggested Citation

  • Federico Aluigi & Lucia Caramellino & Paolo Pigato & Edoardo Scrima, 2026. "Pricing options on illiquid assets using liquid market benchmarks: an application to energy markets," CEIS Research Paper 626, Tor Vergata University, CEIS, revised 05 Aug 2026.
    • Handle: RePEc:rtv:ceisrp:626
    as

    Download full text from publisher

    File URL: https://ceistorvergata.it/RePEc/rpaper/RP626.pdf
    File Function: Main text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mahringer, Steffen & Prokopczuk, Marcel, 2015. "An empirical model comparison for valuing crack spread options," Energy Economics, Elsevier, vol. 51(C), pages 177-187.
    2. Damiano Brigo & Fabio Mercurio & Giulio Sartorelli, 2003. "Alternative asset-price dynamics and volatility smile," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 173-183.
    3. Jim Gatheral & Antoine Jacquier, 2014. "Arbitrage-free SVI volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 59-71, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Federico Aluigi & Lucia Caramellino & Paolo Pigato & Edoardo Scrima, 2026. "Pricing options on illiquid assets using liquid market benchmarks: an application to energy markets," Papers 2607.19030, arXiv.org.
    2. Hans Buehler & Blanka Horvath & Anastasis Kratsios & Yannick Limmer & Raeid Saqur, 2026. "SANOS Smooth strictly Arbitrage-free Non-parametric Option Surfaces," Papers 2601.11209, arXiv.org, revised May 2026.
    3. Stefano De Marco, 2020. "On the harmonic mean representation of the implied volatility," Papers 2007.03585, arXiv.org.
    4. Ciprian Necula & Gabriel Drimus & Walter Farkas, 2019. "A general closed form option pricing formula," Review of Derivatives Research, Springer, vol. 22(1), pages 1-40, April.
    5. Sergey Badikov & Mark H. A. Davis & Antoine Jacquier, 2018. "Perturbation analysis of sub/super hedging problems," Papers 1806.03543, arXiv.org, revised May 2021.
    6. Benjamin Virrion, 2020. "Deep Importance Sampling," Papers 2007.02692, arXiv.org, revised Jul 2020.
    7. Emmanuel Gnabeyeu & Omar Karkar & Imad Idboufous, 2024. "Solving The Dynamic Volatility Fitting Problem: A Deep Reinforcement Learning Approach," Papers 2410.11789, arXiv.org.
    8. Jing Wang & Shuaiqiang Liu & Cornelis Vuik, 2025. "Controllable Generation of Implied Volatility Surfaces with Variational Autoencoders," Papers 2509.01743, arXiv.org.
    9. Lijie Ding & Egang Lu & Kin Cheung, 2025. "Deep Learning Option Pricing with Market Implied Volatility Surfaces," Papers 2509.05911, arXiv.org.
    10. Thaddeus Neururer & George Papadakis & Edward J. Riedl, 2016. "Tests of investor learning models using earnings innovations and implied volatilities," Review of Accounting Studies, Springer, vol. 21(2), pages 400-437, June.
    11. Zheng Cao & Xinhao Lin, 2024. "Theoretical and Empirical Validation of Heston Model," Papers 2409.12453, arXiv.org, revised Oct 2024.
    12. Mnacho Echenim & Emmanuel Gobet & Anne-Claire Maurice, 2022. "Unbiasing and robustifying implied volatility calibration in a cryptocurrency market with large bid-ask spreads and missing quotes," Papers 2207.02989, arXiv.org.
    13. Alessandro Ramponi, 2011. "Mixture Dynamics and Regime Switching Diffusions with Application to Option Pricing," Methodology and Computing in Applied Probability, Springer, vol. 13(2), pages 349-368, June.
    14. Miquel Noguer i Alonso, 2026. "The Mathematics of Volatility Surfaces," Papers 2608.05198, arXiv.org.
    15. Wenyong Zhang & Lingfei Li & Gongqiu Zhang, 2021. "A Two-Step Framework for Arbitrage-Free Prediction of the Implied Volatility Surface," Papers 2106.07177, arXiv.org, revised Jan 2022.
    16. Arianna Mingone, 2022. "Smiles in delta," Papers 2209.00406, arXiv.org.
    17. Sadanand Singh & Allam Reddy & Manan Chopra, 2026. "Beyond the Smile: A Hybrid Convolutional VAE for Crypto Volatility Surfaces," Papers 2606.16961, arXiv.org.
    18. Stefano De Marco & Caroline Hillairet & Antoine Jacquier, 2013. "Shapes of implied volatility with positive mass at zero," Papers 1310.1020, arXiv.org, revised May 2017.
    19. repec:hal:wpaper:hal-03715921 is not listed on IDEAS
    20. Achintya Gopal, 2024. "Filling in Missing FX Implied Volatilities with Uncertainties: Improving VAE-Based Volatility Imputation," Papers 2411.05998, arXiv.org.
    21. Wang, Yudong & Liu, Li & Ma, Feng & Wu, Chongfeng, 2016. "What the investors need to know about forecasting oil futures return volatility," Energy Economics, Elsevier, vol. 57(C), pages 128-139.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rtv:ceisrp:626. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Barbara Piazzi (email available below). General contact details of provider: https://edirc.repec.org/data/csrotit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.