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A closed-form approximation formula for pricing European options under a three-factor model

Published online by Cambridge University Press:  18 August 2021

Hye-mee Kil  and
Jeong-Hoon Kim Open the ORCID record for Jeong-Hoon Kim [Opens in a new window]
Hye-mee Kil
Affiliation:
School of Mathematics & Computing, Yonsei University, Seoul, Republic of Korea. E-mail: [email protected], [email protected]
Jeong-Hoon Kim
Affiliation:
School of Mathematics & Computing, Yonsei University, Seoul, Republic of Korea. E-mail: [email protected], [email protected]
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Abstract

The double-mean-reverting model, introduced by Gatheral [(2008). Consistent modeling of SPX and VIX options. In The Fifth World Congress of the Bachelier Finance Society London, July 18], is known to be a successful three-factor model that can be calibrated to both CBOE Volatility Index (VIX) and S&P 500 Index (SPX) options. However, the calibration of this model may be slow because there is no closed-form solution formula for European options. In this paper, we use a rescaled version of the model developed by Huh et al. [(2018). A scaled version of the double-mean-reverting model for VIX derivatives. Mathematics and Financial Economics 12: 495–515] and obtain explicitly a closed-form pricing formula for European option prices. Our formulas for the first and second-order approximations do not require any complicated calculation of integral. We demonstrate that a faster calibration result of the double-mean revering model is available and yet the practical implied volatility surface of SPX options can be produced. In particular, not only the usual convex behavior of the implied volatility surface but also the unusual concave down behavior as shown in the COVID-19 market can be captured by our formula.

Keywords

CalibrationDouble-mean-reverting modelEuropean optionImplied volatility
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 36 , Issue 4 , October 2022 , pp. 1214 - 1240
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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