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On the Number of State Variables in Options Pricing

Author

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  • Gang Li

    (Hong Kong Baptist University, Kowloon Tong, Hong Kong; and Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

  • Chu Zhang

    (Hong Kong Baptist University, Kowloon Tong, Hong Kong; and Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong)

Abstract

In this paper, we investigate the methodological issue of determining the number of state variables required for options pricing. After showing the inadequacy of the principal component analysis approach, which is commonly used in the literature, we adopt a nonparametric regression technique with nonlinear principal components extracted from the implied volatilities of various moneyness and maturities as proxies for the transformed state variables. The methodology is applied to the prices of S& P 500 index options from the period 1996-2005. We find that, in addition to the index value itself, two state variables, approximated by the first two nonlinear principal components, are adequate for pricing the index options and fitting the data in both time series and cross sections.

Suggested Citation

  • Gang Li & Chu Zhang, 2010. "On the Number of State Variables in Options Pricing," Management Science, INFORMS, vol. 56(11), pages 2058-2075, November.
    • Handle: RePEc:inm:ormnsc:v:56:y:2010:i:11:p:2058-2075
    DOI: 10.1287/mnsc.1100.1222
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    References listed on IDEAS

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    3. Yu-Hua Zeng & Shou-Lei Wang & Yu-Fei Yang, 2014. "Calibration of the Volatility in Option Pricing Using the Total Variation Regularization," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-9, March.
    4. Majewski, A. A. & Bormetti, G. & Corsi, F., 2013. "Smile from the Past: A general option pricing framework with multiple volatility and leverage components," Working Papers 13/11, Department of Economics, City University London.
    5. Pun, Chi Seng & Chung, Shing Fung & Wong, Hoi Ying, 2015. "Variance swap with mean reversion, multifactor stochastic volatility and jumps," European Journal of Operational Research, Elsevier, vol. 245(2), pages 571-580.
    6. Madan, Dilip B. & Schoutens, Wim, 2013. "Systemic risk tradeoffs and option prices," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 222-230.
    7. Majewski, Adam A. & Bormetti, Giacomo & Corsi, Fulvio, 2015. "Smile from the past: A general option pricing framework with multiple volatility and leverage components," Journal of Econometrics, Elsevier, vol. 187(2), pages 521-531.
    8. Mehrdoust, Farshid & Noorani, Idin & Hamdi, Abdelouahed, 2023. "Two-factor Heston model equipped with regime-switching: American option pricing and model calibration by Levenberg–Marquardt optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 660-678.
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    10. Ben-Zhang Yang & Xiaoping Lu & Guiyuan Ma & Song-Ping Zhu, 2019. "Robust portfolio optimization with multi-factor stochastic volatility," Papers 1910.06872, arXiv.org, revised Jun 2020.
    11. Li, Gang & Zhang, Chu, 2016. "On the relationship between conditional jump intensity and diffusive volatility," Journal of Empirical Finance, Elsevier, vol. 37(C), pages 196-213.
    12. Adam Aleksander Majewski & Giacomo Bormetti & Fulvio Corsi, 2014. "Smile from the Past: A general option pricing framework with multiple volatility and leverage components," Papers 1404.3555, arXiv.org.
    13. Biao Guo & Qian Han & Doojin Ryu, 2013. "The Number of State Variables for CDS Pricing," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
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