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Generalized parameter functions for option pricing

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  • Andreou, Panayiotis C.
  • Charalambous, Chris
  • Martzoukos, Spiros H.
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    Abstract

    We extend the benchmark nonlinear deterministic volatility regression functions of Dumas et al. (1998) to provide a semi-parametric method where an enhancement of the implied parameter values is used in the parametric option pricing models. Besides volatility, skewness and kurtosis of the asset return distribution can also be enhanced. Empirical results, using closing prices of the S&P 500 index call options (in one day ahead out-of-sample pricing tests), strongly support our method that compares favorably with a model that admits stochastic volatility and random jumps. Moreover, it is found to be superior in various robustness tests. Our semi-parametric approach is an effective remedy to the curse of dimensionality presented in nonparametric estimation and its main advantage is that it delivers theoretically consistent option prices and hedging parameters. The economic significance of the approach is tested in terms of hedging, where the evaluation and estimation loss functions are aligned.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Banking & Finance.

    Volume (Year): 34 (2010)
    Issue (Month): 3 (March)
    Pages: 633-646

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    Handle: RePEc:eee:jbfina:v:34:y:2010:i:3:p:633-646

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    Web page: http://www.elsevier.com/locate/jbf

    Related research

    Keywords: Option pricing Implied volatilities Deterministic volatility functions Delta-hedging Semi-parametric approach;

    References

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    Cited by:
    1. Beliaeva, Natalia & Nawalkha, Sanjay, 2012. "Pricing American interest rate options under the jump-extended constant-elasticity-of-variance short rate models," Journal of Banking & Finance, Elsevier, Elsevier, vol. 36(1), pages 151-163.
    2. Câmara, António & Krehbiel, Tim & Li, Weiping, 2011. "Expected returns, risk premia, and volatility surfaces implicit in option market prices," Journal of Banking & Finance, Elsevier, Elsevier, vol. 35(1), pages 215-230, January.
    3. Dunis, Christian & Kellard, Neil M. & Snaith, Stuart, 2013. "Forecasting EUR–USD implied volatility: The case of intraday data," Journal of Banking & Finance, Elsevier, Elsevier, vol. 37(12), pages 4943-4957.
    4. Panayiotis Andreou & Chris Charalambous & Spiros Martzoukos, 2014. "Assessing the performance of symmetric and asymmetric implied volatility functions," Review of Quantitative Finance and Accounting, Springer, Springer, vol. 42(3), pages 373-397, April.

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