论文标题
通过机器学习和日内通用性预测波动性
Volatility forecasting with machine learning and intraday commonality
论文作者
论文摘要
我们将机器学习模型应用于预测盘中实现的波动性（RV），通过将库存数据合并在一起，并通过纳入市场波动率的代理来利用日内波动性中的通用性。神经网络在性能方面主导了线性回归和基于树的模型，因为它们能够发现和建模变量之间的复杂潜在相互作用。当我们将经过训练的模型应用于培训集中尚未包括的新股票时，我们的发现仍然很健壮，从而为股票之间的普遍波动机制提供了新的经验证据。最后，我们提出了一种新的方法，以使用过去的盘中RV作为预测指标来预测一日的RV，并突出显示有助于预测机制的有趣时间效果。结果表明，所提出的方法比仅依靠过去每日RV的强大传统基准相比，产生了卓越的样本外预测。
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs.
