Nonparametric estimation and testing of stochastic discount factor
This paper attempts to estimate stochastic discount factor (SDF) proxies nonparametrically using the conditional Hansen–Jagannathan distance. Nonparametric estimation can not only avoid misspecification when dealing with nonlinearity in the model but also provide more precise information about the local properties of the estimators. Empirical studies show that our method performs better than the alternative parametric polynomial models, and furthermore, we find that the return on aggregate wealth can sufficiently explain the SDF proxies when one deals with nonlinearity appropriately.
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- Ravi Jagannathan & Zhenyu Wang, 1996.
"The conditional CAPM and the cross-section of expected returns,"
208, Federal Reserve Bank of Minneapolis.
- Jagannathan, Ravi & Wang, Zhenyu, 1996. " The Conditional CAPM and the Cross-Section of Expected Returns," Journal of Finance, American Finance Association, vol. 51(1), pages 3-53, March.
- Lars Peter Hansen & Ravi Jagannathan, 1994.
"Assessing Specification Errors in Stochastic Discount Factor Models,"
NBER Technical Working Papers
0153, National Bureau of Economic Research, Inc.
- Hansen, Lars Peter & Jagannathan, Ravi, 1997. " Assessing Specification Errors in Stochastic Discount Factor Models," Journal of Finance, American Finance Association, vol. 52(2), pages 557-90, June.
- Lars Peter Hansen & Ravi Jagannathan, 1994. "Assessing specification errors in stochastic discount factor models," Staff Report 167, Federal Reserve Bank of Minneapolis.
- Robert F. Dittmar, 2002. "Nonlinear Pricing Kernels, Kurtosis Preference, and Evidence from the Cross Section of Equity Returns," Journal of Finance, American Finance Association, vol. 57(1), pages 369-403, 02.
- Cai, Zongwu, 2007. "Trending time-varying coefficient time series models with serially correlated errors," Journal of Econometrics, Elsevier, vol. 136(1), pages 163-188, January.
- Lewbel, Arthur, 2007. "A local generalized method of moments estimator," Economics Letters, Elsevier, vol. 94(1), pages 124-128, January.
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