论文标题
部分可观测时空混沌系统的无模型预测
A penalized two-pass regression to predict stock returns with time-varying risk premia
论文作者
论文摘要
我们通过随时间变化的因子负载来开发受惩罚的两次回归。第一遍中的惩罚对时间变化驱动器的稀疏性进行了稀疏性，同时还通过规范适当的系数组来维持与无度关键限制的兼容性。第二次通过提供了风险溢价估计，以预测权益超额回报。我们的蒙特卡洛结果以及对大量横截面数据集的个人股票集的经验结果表明，如果不进行分组的惩罚可能会屈服于几乎所有估计的时变模型，违反了无标准限制。此外，我们的结果表明，与惩罚方法相比，所提出的方法在没有适当分组或时间不变的因子模型的情况下减少了预测错误。
We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no-arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no-arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.
