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|Title:||Generalized parameter functions for option pricing||Authors:||Andreou, Panayiotis
Martzoukos, Spiros H.
|Major Field of Science:||Social Sciences||Field Category:||Economics and Business||Keywords:||Semi-parametric approach;Delta-hedging;Deterministic volatility functions;Implied volatilities;Option pricing||Issue Date:||Mar-2010||Source:||Journal of Banking and Finance, 2010, vol. 34, no. 3, pp. 633-646||Volume:||34||Issue:||3||Start page:||633||End page:||646||Journal:||Journal of Banking & Finance||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.||ISSN:||0378-4266||DOI:||10.1016/j.jbankfin.2009.08.027||Rights:||© Elsevier||Type:||Article||Affiliation :||Durham University
University of Cyprus
Cyprus University of Technology
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