Nonparametric stochastic discount factor decomposition

Abstract

We introduce econometric methods to perform estimation and inference on the permanent and transitory components of the stochastic discount factor (SDF) in dynamic Markov environments. The approach is nonparametric in that it does not impose parametric restrictions on the law of motion of the state process. We propose sieve estimators of the eigenvalue-eigenfunction pair which are used to decompose the SDF into its permanent and transitory components, as well as estimators of the long-run yield and the entropy of the permanent component of the SDF, allowing for a wide variety of empirically relevant setups. Consistency and convergence rates are established. The estimators of the eigenvalue, yield and entropy are shown to be asymptotically normal and semiparametrically efficient when the SDF is observable. We also introduce nonparametric estimators of the continuation value under Epstein-Zin preferences, thereby extending the scope of our estimators to an important class of recursive preferences. The estimators are simple to implement, perform favorably in simulations, and may be used to numerically compute the eigenfunction and its eigenvalue in fully specified models when analytical solutions are not available.

(Almost) Model‐Free Recovery

“…Chabi‐Yo, Bakshi, and Gao () test the path‐dependence assumption in the bond market, while Qin and Linetsky () and Qin and Linetsky () give extended recovery theorems to general recurrent Markovian processes and general semimartingale state dynamics, respectively. Christensen () introduces a general nonparametric method for estimating the long‐term factorization of the pricing kernel in dynamic Markov environments.…”