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Semiparametric estimation of asset pricing kernel

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  • Jun Yang

Abstract

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.

Suggested Citation

  • Jun Yang, 2009. "Semiparametric estimation of asset pricing kernel," Applied Financial Economics, Taylor & Francis Journals, vol. 19(4), pages 257-272.
    • Handle: RePEc:taf:apfiec:v:19:y:2009:i:4:p:257-272
    DOI: 10.1080/09603100802314492
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    Cited by:

    1. Audrino, Francesco & Meier, Pirmin, 2012. "Empirical pricing kernel estimation using a functional gradient descent algorithm based on splines," Economics Working Paper Series 1210, University of St. Gallen, School of Economics and Political Science.
    2. Horatio Cuesdeanu & Jens Carsten Jackwerth, 2018. "The pricing kernel puzzle: survey and outlook," Annals of Finance, Springer, vol. 14(3), pages 289-329, August.

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