Semiparametric estimation of asset pricing kernel

This article empirically studies the pricing kernel implicit in option prices. Based on the cross-sectional fits alone, no significant difference can be detected between models with different factor dynamics. A cubic pricing kernel provides almost perfect fits in the sample. Nonlinearity in the pricing kernel is crucial for in-sample performance. Both excess kurtosis and skewness are very important. The claim-based market line sharply distinguishes various estimates of the pricing kernel and tracks the market sentiment. However, a well-specified factor dynamics model improves the out-of-sample pricing performance. With a well-specified factor dynamics model, the linear pricing kernel beats the other competitors at a 2-week horizon.