Nonlinear Predictability of Stock Returns? Parametric Versus Nonparametric Inference in Predictive Regressions
Nonlinear Predictability of Stock Returns? Parametric Versus Nonparametric Inference in Predictive Regressions
复制标题
股票收益的非线性可预测性？
DOI：
10.1080/07350015.2020.1819821
复制
发表时间：
2020
影响因子：
3
通讯作者：
Benjamin M Hillmann
中科院分区：
文献类型：
--
作者：
M. Demetrescu;Benjamin M Hillmann
摘要预测回归中的非参数检验程序在低和高回归变量持续性下都具有有限的零分布，但与错误指定的线性预测回归相比，局部功效较低。我们认为，IV推理更适合（在本地电源）分析加性预测模型与不确定的预测持久性。然后，提出了一个用于样本外预测的两步程序。对于当前的估计窗口，首先测试可预测性;在拒绝的情况下，使用非线性回归模型进行预测，否则使用股票收益的历史平均值。在标准普尔500指数的伪样本外预测中，这种两步方法的表现优于竞争对手（尽管幅度不大）。
Abstract Nonparametric test procedures in predictive regressions have limiting null distributions under both low and high regressor persistence, but low local power compared to misspecified linear predictive regressions. We argue that IV inference is better suited (in terms of local power) for analyzing additive predictive models with uncertain predictor persistence. Then, a two-step procedure is proposed for out-of-sample predictions. For the current estimation window, one first tests for predictability; in case of a rejection, one predicts using a nonlinear regression model, otherwise the historic average of the stock returns is used. This two-step approach performs better than competitors (though not by a large margin) in a pseudo-out-of-sample prediction exercise for the S&P 500.