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Pricing Cryptocurrency Options With Volatility of Volatility

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  • Lingshan Du
  • Ji Shen

Abstract

We propose a novel option pricing model that explicitly incorporates volatility‐of‐volatility (VOV) dynamics and its associated risk premium. Our framework integrates realized variance and realized quarticity to capture latent VOV dynamics, addressing key challenges in cryptocurrency option pricing. Using Fourier inversion methods, we derive a closed‐form pricing formula for European‐style options. Empirical analysis with high‐frequency cryptocurrency option data shows that our model improves pricing accuracy, reducing implied volatility errors by 8.55% compared to benchmark models. The model outperforms benchmarks across all moneyness levels, remains robust for both short‐ and long‐maturity contracts, and maintains accuracy under high volatility. This study contributes to the literature by introducing a tractable and empirically validated approach to cryptocurrency option pricing through explicit VOV modeling.

Suggested Citation

  • Lingshan Du & Ji Shen, 2025. "Pricing Cryptocurrency Options With Volatility of Volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 45(11), pages 2066-2091, November.
  • Handle: RePEc:wly:jfutmk:v:45:y:2025:i:11:p:2066-2091
    DOI: 10.1002/fut.70029
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    References listed on IDEAS

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    1. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
    2. Ding, Yashuang (Dexter), 2023. "A simple joint model for returns, volatility and volatility of volatility," Journal of Econometrics, Elsevier, vol. 232(2), pages 521-543.
    3. Anders B. Trolle & Eduardo S. Schwartz, 2009. "Unspanned Stochastic Volatility and the Pricing of Commodity Derivatives," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4423-4461, November.
    4. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2005. "Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities," Econometrica, Econometric Society, vol. 73(1), pages 279-296, January.
    5. Christoffersen, Peter & Feunou, Bruno & Jeon, Yoontae, 2015. "Option valuation with observable volatility and jump dynamics," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 101-120.
    6. Oomen, Roel C.A., 2006. "Properties of Realized Variance Under Alternative Sampling Schemes," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 219-237, April.
    7. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    8. Ole E. Barndorff-Nielsen & Almut E. D. Veraart, 2012. "Stochastic Volatility of Volatility and Variance Risk Premia," Journal of Financial Econometrics, Oxford University Press, vol. 11(1), pages 1-46, December.
    9. Daniel Ellsberg, 1961. "Risk, Ambiguity, and the Savage Axioms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 75(4), pages 643-669.
    10. F. M. Bandi & J. R. Russell, 2008. "Microstructure Noise, Realized Variance, and Optimal Sampling," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 75(2), pages 339-369.
    11. Fang Liang & Lingshan Du, 2024. "Option pricing with dynamic conditional skewness," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(7), pages 1154-1188, July.
    12. Engle, Robert F. & Gallo, Giampiero M., 2006. "A multiple indicators model for volatility using intra-daily data," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 3-27.
    13. Carol Alexander & Ding Chen & Arben Imeraj, 2023. "Crypto quanto and inverse options," Mathematical Finance, Wiley Blackwell, vol. 33(4), pages 1005-1043, October.
    14. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    15. V. Lucic & A. Sepp, 2024. "Valuation and hedging of cryptocurrency inverse options," Quantitative Finance, Taylor & Francis Journals, vol. 24(7), pages 851-869, July.
    16. Janine Balter, 2015. "Quarticity Estimation on ohlc Data," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 505-519.
    17. Ole E. Barndorff-Nielsen & Neil Shephard, 2002. "Estimating quadratic variation using realized variance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 457-477.
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