Nonparametric long term prediction of stock returns with generated bond yields

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• 作者：Michael Scholz^a ; ^{michael.scholz@uni-graz.at" class="auth_mail" title="E-mail the corresponding author} ; Stefan Sperlich^b ; ^{stefan.sperlich@unige.ch" class="auth_mail" title="E-mail the corresponding author} ; Jens Perch Nielsen^c ; ^{Jens.Nielsen.1@city.ac.uk" class="auth_mail" title="E-mail the corresponding author}
• 关键词：C14 ; C53 ; C58 ; G17 ; G22
• 刊名：Insurance: Mathematics and Economics
• 出版年：2016
• 出版时间：July 2016
• 年：2016
• 卷：69
• 期：Complete
• 页码：82-96
• 全文大小：820 K

文摘

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.