We introduce a fast, closed-form, simulation-free method to model and forecast multiple asset returns and employ it to investigate the optimal ensemble of features to include when jointly predicting monthly stock and bond excess returns. Our approach builds on the Bayesian dynamic linear models of West and Harrison (Bayesian Forecasting and Dynamic Models (1997) Springer), and it can objectively determine, through a fully automated procedure, both the optimal set of regressors to include in the predictive system and the degree to which the model coefficients, volatilities and covariances should vary over time. When applied to a portfolio of five stock and bond returns, we find that our method leads to large forecast gains, both in statistical and economic terms. In particular, we find that relative to a standard no-predictability benchmark, the optimal combination of predictors, stochastic volatility and time-varying covariances increases the annualized certainty equivalent returns of a leverage-constrained power utility investor by more than 500 basis points.

中文翻译：

---

多元贝叶斯动态线性模型的最优资产配置

我们引入了一种快速，封闭式，无模拟的方法来对多种资产收益进行建模和预测，并使用它来研究联合预测月度股票和债券超额收益时要包括的最佳特征集合。我们的方法基于West和Harrison的贝叶斯动态线性模型（贝叶斯预测和动态模型）（1997年，Springer），它可以通过全自动程序客观地确定要纳入预测系统的最佳回归变量集，以及模型系数，波动率和协方差随时间变化的程度。当将其应用于包含五种股票和债券收益的投资组合时，我们发现我们的方法在统计和经济方面均带来了可观的预测收益。特别是，我们发现，相对于标准的不可预测性基准，预测器，随机波动率和时变协方差的最佳组合将杠杆约束的电力公司的年度确定性当量收益提高了500个基点以上。