Pricing kernel modeling

Denis Belomestny, Shujie Ma and Wolfgang Härdle

No 2015-001, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: Empirical Pricing Kernel; Kernel; Kernel Density Estimation; Nonparametric Fitting; Kullback-Leibler Divergence (search for similar items in EconPapers)
JEL-codes: C00 C14 G12 (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2015-001

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