Option pricing with model-guided nonparametric methods

Jianqing Fan, Loriano Mancini

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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 non parametric learning approaches that learn and correct empirically the pricing errors. In this article we propose a new non parametric 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 non parametrically 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 non parametrically. This model - guided method, called automatic correction of errors (ACE), estimates the state price distribution non parametrically. The method is easy to implement and can be combined with any model - based pricing formula to correct the systematic biases of pricing errors. We also develop a non parametric test based on the generalized likelihood ratio to document the efficacy of the ACE method. Empirical studies based on S&P 500 index options show that our method outperforms several competing pricing models in terms of predictive and hedging abilities.

Original languageEnglish (US)
Pages (from-to)1351-1372
Number of pages22
JournalJournal of the American Statistical Association
Issue number488
StatePublished - Dec 2009

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Generalized likelihood ratio test
  • Model misspecification
  • Nonparametric regression
  • Out-of-sample analysis
  • State price distribution


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