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Assessing the performance of symmetric and asymmetric implied volatility functions

Author

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  • Panayiotis Andreou

    ()

  • Chris Charalambous

    ()

  • Spiros Martzoukos

    ()

Abstract

This study examines several alternative symmetric and asymmetric model specifications of regression-based deterministic volatility models to identify the one that best characterizes the implied volatility functions of S&P 500 Index options in the period 1996–2009. We find that estimating the models with nonlinear least squares, instead of ordinary least squares, always results in lower pricing errors in both in- and out-of-sample comparisons. In-sample, asymmetric models of the moneyness ratio estimated separately on calls and puts provide the overall best performance. However, separating calls from puts violates the put-call-parity and leads to severe model mis-specification problems. Out-of-sample, symmetric models that use the logarithmic transformation of the strike price are the overall best ones. The lowest out-of-sample pricing errors are observed when implied volatility models are estimated consistently to the put-call-parity using the joint data set of out-of-the-money options. The out-of-sample pricing performance of the overall best model is shown to be resilient to extreme market conditions and compares quite favorably with continuous-time option pricing models that admit stochastic volatility and random jump risk factors. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Panayiotis Andreou & Chris Charalambous & Spiros Martzoukos, 2014. "Assessing the performance of symmetric and asymmetric implied volatility functions," Review of Quantitative Finance and Accounting, Springer, vol. 42(3), pages 373-397, April.
  • Handle: RePEc:kap:rqfnac:v:42:y:2014:i:3:p:373-397
    DOI: 10.1007/s11156-013-0346-z
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    References listed on IDEAS

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    Cited by:

    1. Itkin, Andrey, 2015. "To sigmoid-based functional description of the volatility smile," The North American Journal of Economics and Finance, Elsevier, vol. 31(C), pages 264-291.
    2. Liu, Xiaoquan & Cao, Yi & Ma, Chenghu & Shen, Liya, 2019. "Wavelet-based option pricing: An empirical study," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1132-1142.

    More about this item

    Keywords

    Option pricing; Deterministic volatility functions; Implied volatility forecasting; Model selection; Stochastic volatility; G13; G14;

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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