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- W1565487582 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 different 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." @default.
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- W1565487582 date "2015-01-01" @default.
- W1565487582 modified "2023-09-27" @default.
- W1565487582 title "Pricing Kernel Modeling" @default.
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- W1565487582 doi "https://doi.org/10.2139/ssrn.2630305" @default.
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