TY - JOUR
T1 - Option pricing with model-guided nonparametric methods
AU - Fan, Jianqing
AU - Mancini, Loriano
N1 - Funding Information:
Jianqing Fan is Frederick L. Moore’18 Professor of Finance, Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, and Honorary Professor, Department of Statistics, Shanghai University of Finance and Economics, Shanghai, China (E-mail: [email protected]). Loriano Mancini is Assistant Professor, Swiss Finance Institute at EPFL, CH-1015 Lausanne, Switzerland (E-mail: loriano.mancini@ epfl.ch). Fan’s research was supported by the National Science Foundation grants DMS-0532370 and DMS-0704337. Mancini’s research was supported by the University Research Priority Program “Finance and Financial Markets” (University of Zurich) and by the NCCR-FinRisk (Swiss National Science Foundation). This research was undertaken while Mancini visited the Department of Operations Research and Financial Engineering, Princeton University, whose hospitality is gratefully acknowledged. The authors thank the editor, the associate editor, three anonymous referees, Giovanni Barone-Adesi, Eric Ghy-sels, Rajna Gibson, Peter Gruber, and Claudia Ravanelli for their helpful comments.
PY - 2009/12
Y1 - 2009/12
N2 - 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.
AB - 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.
KW - Generalized likelihood ratio test
KW - Model misspecification
KW - Nonparametric regression
KW - Out-of-sample analysis
KW - State price distribution
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U2 - 10.1198/jasa.2009.ap08171
DO - 10.1198/jasa.2009.ap08171
M3 - Article
AN - SCOPUS:74049104435
SN - 0162-1459
VL - 104
SP - 1351
EP - 1372
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 488
ER -