Econ Papers Daily / 期刊论文
非平稳波动环境下股票收益可预测性的实时监测
Real-time monitoring for stock return predictability in nonstationary volatility environments
摘要
Stock return predictability, should it exist, is likely to be episodic in nature. In order to exploit such pockets of predictability it is essential that they are rapidly detected, in real-time, as the nascent predictive regime emerges. This will typically entail the repeated (sequential) application of one-shot end-of-sample predictability statistics, updated as new data become available. Consequently, in addition to dealing with the usual inference problems caused by unknown regressor persistence and predictive regression endogeneity, one must also account for the multiple testing issues inherent in such monitoring procedures. In addition, stock returns and/or the predictors commonly used typically exhibit time-varying volatility and it is known that ignoring such data features can result in the spurious detection of predictability. We propose real-time monitoring procedures which take account of these issues. Our preferred monitoring strategy uses a CUSUM-type procedure based on a specific moment condition related to the predictive power of the putative predictor. We implement nonparametric adjustment methods to allow for the possibility of time-varying volatility which do not require the practitioner to assume any particular parametric model for volatility. Monte Carlo simulations confirm that our proposed detection procedures display well-controlled false positive rates across a range of feasible volatility paths coupled with good power to rapidly detect an emerging predictive episode. The empirical relevance of our proposed monitoring strategy is illustrated in a pseudo real-time monitoring exercise using a well-known dataset of S&P 500 returns.
中文摘要
股票收益的可预测性，如果存在的话，很可能是偶发性的。为了利用这些可预测性的片段，至关重要的是在新生的预测机制出现时，能够实时、迅速地检测到它们。这通常需要对单次样本末端可预测性统计量进行重复（序贯）应用，并随着新数据的可用而更新。因此，除了要处理由未知回归量持续性和预测回归内生性所引起的通常推断问题外，还必须考虑此类监测程序中固有的多重检验问题。此外，股票收益和/或常用的预测变量通常表现出时变波动性，而众所周知，忽略此类数据特征可能导致对可预测性的虚假检测。我们提出了考虑这些问题的实时监测程序。我们首选的监测策略使用基于与假定预测变量预测能力相关的特定矩条件的CUSUM型程序。我们实施非参数调整方法，以允许时变波动性的可能性，而不要求实践者对波动性假设任何特定的参数模型。蒙特卡洛模拟证实，我们提出的检测程序在一系列可行的波动性路径上显示出良好控制的假阳性率，同时具有良好的功效，能够迅速检测到新出现的预测期。我们提出的监测策略的经验相关性通过使用著名的S&P 500收益数据集的伪实时监测练习进行了说明。