Nonparametric long term prediction of stock returns with generated bond yields

Michael Scholz, Stefan Sperlich () and Jens Perch Nielsen

Insurance: Mathematics and Economics, 2016, vol. 69, issue C, 82-96

Abstract: Recent empirical approaches in forecasting equity returns or premiums found that dynamic interactions among the stock and bond are relevant for long term pension products. Automatic procedures to upgrade or downgrade risk exposure could potentially improve long term performance for such products. The risk and return of bonds is more easy to predict than the risk and return of stocks. This and the well known stock-bond correlation motivates the inclusion of the current bond yield in a model for the prediction of excess stock returns. Here, we take the actuarial long term view using yearly data, and focus on nonlinear relationships between a set of covariates. We employ fully nonparametric models and apply for estimation a local-linear kernel smoother. Since the current bond yield is not known, it is predicted in a prior step. The structure imposed this way in the final estimation process helps to circumvent the curse of dimensionality and reduces bias in the estimation of excess stock returns. Our validated stock prediction results show that predicted bond returns improve stock prediction significantly.

Keywords: Prediction; Stock returns; Bond yield; Cross validation; Generated regressors (search for similar items in EconPapers)
JEL-codes: C14 C53 C58 G17 G22 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:69:y:2016:i:c:p:82-96

DOI: 10.1016/j.insmatheco.2016.04.007

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