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The Dynamics of the S&P 500 Implied Volatility Surface

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  • George Skiadopoulos
  • Stewart Hodges
  • Les Clewlow

Abstract

This empirical study is motivated by the literature on “smile-consistent” arbitrage pricing with stochastic volatility. We investigate the number and shape of shocks that move implied volatility smiles and surfaces by applying Principal Components Analysis. Two components are identified under a variety of criteria. Subsequently, we develop a “Procrustes” type rotation in order to interpret the retained components. The results have implications for both option pricing and hedging and for the economics of option pricing. Copyright Kluwer Academic Publishers 2000

Suggested Citation

  • George Skiadopoulos & Stewart Hodges & Les Clewlow, 2000. "The Dynamics of the S&P 500 Implied Volatility Surface," Review of Derivatives Research, Springer, vol. 3(3), pages 263-282, October.
    • Handle: RePEc:kap:revdev:v:3:y:2000:i:3:p:263-282
    DOI: 10.1023/A:1009642705121
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    Cited by:

    1. Kearney, Fearghal & Shang, Han Lin & Sheenan, Lisa, 2019. "Implied volatility surface predictability: The case of commodity markets," Journal of Banking & Finance, Elsevier, vol. 108(C).
    2. Jianhui Li & Sebastian A. Gehricke & Jin E. Zhang, 2019. "How do US options traders “smirk” on China? Evidence from FXI options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(11), pages 1450-1470, November.
    3. Francesco Audrino & Dominik Colangelo, 2009. "Option trading strategies based on semi-parametric implied volatility surface prediction," University of St. Gallen Department of Economics working paper series 2009 2009-24, Department of Economics, University of St. Gallen.
    4. Fengler, Matthias R. & Wang, Qihua, 2003. "Fitting the Smile Revisited: A Least Squares Kernel Estimator for the Implied Volatility Surface," SFB 373 Discussion Papers 2003,25, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    5. Matthias Fengler & Wolfgang Härdle & Enno Mammen, 2005. "A Dynamic Semiparametric Factor Model for Implied Volatility String Dynamics," SFB 649 Discussion Papers SFB649DP2005-020, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    6. Chen, Si & Zhou, Zhen & Li, Shenghong, 2016. "An efficient estimate and forecast of the implied volatility surface: A nonlinear Kalman filter approach," Economic Modelling, Elsevier, vol. 58(C), pages 655-664.
    7. Zdeněk Drábek & Miloš Kopa & Matúš Maciak & Michal Pešta & Sebastiano Vitali, 2023. "Investment disputes and their explicit role in option market uncertainty and overall risk instability," Computational Management Science, Springer, vol. 20(1), pages 1-25, December.
    8. Szymon Borak & Matthias Fengler & Wolfgang Härdle, 2005. "DSFM fitting of Implied Volatility Surfaces," SFB 649 Discussion Papers SFB649DP2005-022, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Bernales, Alejandro & Guidolin, Massimo, 2014. "Can we forecast the implied volatility surface dynamics of equity options? Predictability and economic value tests," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 326-342.
    10. Joshua Rosenberg, 1999. "Implied Volatility Functions: A Reprise," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-027, New York University, Leonard N. Stern School of Business-.
    11. Bastien Baldacci, 2020. "High-frequency dynamics of the implied volatility surface," Papers 2012.10875, arXiv.org.
    12. Bernd Engelmann & Matthias Fengler & Morten Nalholm & Peter Schwendner, 2006. "Static versus dynamic hedges: an empirical comparison for barrier options," Review of Derivatives Research, Springer, vol. 9(3), pages 239-264, November.
    13. Fengler, Matthias R. & Härdle, Wolfgang & Mammen, Enno, 2003. "Implied volatility string dynamics," SFB 373 Discussion Papers 2003,54, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    14. Peter Christoffersen & Steven Heston & Kris Jacobs, 2009. "The Shape and Term Structure of the Index Option Smirk: Why Multifactor Stochastic Volatility Models Work So Well," Management Science, INFORMS, vol. 55(12), pages 1914-1932, December.
    15. Martin Magris & Perttu Barholm & Juho Kanniainen, 2017. "Implied volatility smile dynamics in the presence of jumps," Papers 1711.02925, arXiv.org, revised May 2020.
    16. Amine Bouden, 2008. "The Behavior Of The Implied Volatility Surface: Evidence From Crude Oil Futures Options," World Scientific Book Chapters, in: Mondher Bellalah & Jean-Luc Prigent & Jean-Michel Sahut & Georges Pariente & Olivier Levyne & Michel (ed.), Risk Management And Value Valuation and Asset Pricing, chapter 8, pages 151-175, World Scientific Publishing Co. Pte. Ltd..
    17. Lovreta, Lidija & Silaghi, Florina, 2020. "The surface of implied firm’s asset volatility," Journal of Banking & Finance, Elsevier, vol. 112(C).
    18. 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.
    19. George Skiadopoulos, 2004. "The Greek implied volatility index: construction and properties," Applied Financial Economics, Taylor & Francis Journals, vol. 14(16), pages 1187-1196.
    20. Andrew Carverhill & Terry Cheuk & Sigurd Dyrting, 2009. "The smirk in the S&P500 futures options prices: a linearized factor analysis," Review of Derivatives Research, Springer, vol. 12(2), pages 109-139, July.
    21. Sebastiano Vitali & Miloš Kopa & Gabriele Giana, 2023. "Implied volatility smoothing at COVID-19 times," Computational Management Science, Springer, vol. 20(1), pages 1-42, December.

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