Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/107915
Authors: 
Belomestny, Denis
Ma, Shujie
Härdle, Wolfgang Karl
Year of Publication: 
2014
Series/Report no.: 
SFB 649 Discussion Paper 2015-001
Abstract: 
We propose a new method to estimate the empirical pricing kernel based on option data. We estimate the pricing kernel nonparametrically by using the ratio of the risk-neutral density estimator and the subjective density estimator. The risk-neutral density is approximated by a weighted kernel density estimator with varying unknown weights for di erent observations, and the subjective density is approximated by a kernel density estimator with equal weights. We represent the European call option price function by the second order integration of the risk-neutral density, so that the unknown weights are obtained through one-step penalized least squares estimation with the Kullback-Leibler divergence as the penalty function. Asymptotic results of the resulting estimators are established. The performance of the proposed method is illustrated empirically by simulation and real data application studies.
Subjects: 
Empirical Pricing Kernel
Kernel
Kernel Density Estimation
Nonparametric Fitting
Kullback-Leibler Divergence
JEL: 
C00
C14
G12
Document Type: 
Working Paper

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