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Option pricing with model-guided nonparametric methods

Jianqing Fan Loriano Mancini

Statistics Theory and Methods mathscidoc:1912.43350

Journal of the American Statistical Association, 104, (488), 1351-1372, 2009.12
Parametric option pricing models are widely used in finance. These models capture several features of asset price dynamics; however, their pricing performance can be significantly enhanced when they are combined with nonparametric learning approaches that learn and correct empirically the pricing errors. In this article we propose a new nonparametric method for pricing derivatives assets. Our method relies on the state price distribution instead of the state price density, because the former is easier to estimate nonparametrically than the latter. A parametric model is used as an initial estimate of the state price distribution. Then the pricing errors induced by the parametric model are fitted nonparametrically. This model-guided method, called automatic correction of errors (ACE), estimates the state price distribution nonparametrically. The method is easy to implement and can be combined with any model-based
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@inproceedings{jianqing2009option,
  title={Option pricing with model-guided nonparametric methods},
  author={Jianqing Fan, and Loriano Mancini},
  url={http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113856912184910},
  booktitle={Journal of the American Statistical Association},
  volume={104},
  number={488},
  pages={1351-1372},
  year={2009},
}
Jianqing Fan, and Loriano Mancini. Option pricing with model-guided nonparametric methods. 2009. Vol. 104. In Journal of the American Statistical Association. pp.1351-1372. http://archive.ymsc.tsinghua.edu.cn/pacm_paperurl/20191221113856912184910.
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