AI 中文总结
该研究聚焦美联储FOMC会议对IV曲面的影响，采用卷积二维LSTM模型直接预测IV曲面，验证了ML模型可在异常日有效预测该曲面，同时发现其优势受IV曲面噪声特征限制。
AI 中文摘要
我们的主要目标是预测并实证检验隐含波动率（IV）曲面的演变，尤其聚焦于联邦公开市场委员会（FOMC）的定期会议日期。首先，我们检验IV是否会在公告前上升，且在高波动 regime 下，短期、虚值（OTM）期权的这一效应是否更强。第二部分，我们转而聚焦于验证机器学习（ML）框架能否在预测该效应时击败基准随机游走。模型中加入了与FOMC定期会议日期相关的特征，这使我们能够探究其是否能学习到公告前不确定性升高的效应。我们的贡献主要在于对公告前效应的定量预测，以及在用于IV曲面预测的ML框架中纳入外生信息；同时，这也是首批直接将ML模型应用于IV曲面、不依赖降维的尝试之一。为实现这一点，我们采用了卷积二维LSTM模型，该模型能够学习曲面中的时空信号。我们的分析显示，由于IV曲面的噪声特征，ML框架的优势可能有限。不过，我们的研究进一步印证了这一观点：ML模型也能在异常日有效预测IV曲面。
英文摘要
Our primary goal is to forecast and empirically examine the evolution of the implied volatility (IV) surface, with particular focus on the dates of scheduled meetings of the Federal Open Market Committee (FOMC). Firstly, we check if IV increases before the announcement and if thes effect is stronger for short-dated, out-the-money (OTM) options in high volatility regimes. In the second part, we turn the focus to verifying if the ML framework can beat the benchmark random walk in forecasting this effect. A feature related to dates of scheduled FOMC meetings augments the model, which allows us to discover if it can learn the effect of elevated pre-announcement uncertainty. Our contribution relies mainly on the quantitative prediction of the pre-announcement effect and the inclusion of exogenous information inside the ML framework used for the IV surface forecasting. It is also on of the first attempts to apply ML models directly on the IV surface without relying on dimensionality reduction. To achieve this, we employ a convolutional two-dimensional LSTM model, which is capable of learning spatio-temporal signals in the surface. Our analysis reveals that the edge of the ML framework can be limited due to the noisy characteristics of the IV surface. Nevertheless, our study reinforces the perspective that ML models can effectively forecast the IV surface also during abnormal days.
