When the Fed Speaks: Dynamics and Forecasts of the Volatility Surface

2026-08-11

AI Summary

This study investigates the dynamics of the implied volatility surface around Federal Reserve FOMC meeting dates, with a focus on how pre-meeting uncertainty affects short-dated out-of-the-money options in high-volatility regimes. To this end, the authors develop a machine learning framework based on a two-dimensional ConvLSTM architecture that, for the first time, directly forecasts the full implied volatility surface without dimensionality reduction, explicitly incorporating the FOMC calendar as an exogenous feature. Empirical results demonstrate that the model effectively captures anomalous volatility structures induced by monetary policy announcements and significantly outperforms a random walk benchmark in predictive accuracy. These findings confirm the practical utility of machine learning approaches for modeling volatility surface evolution under macroeconomic event-driven, extreme market conditions.

Abstract

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.

Problem

• implied volatility surface
• FOMC announcements
• volatility forecasting
• pre-announcement uncertainty
• machine learning

Innovation

• implied volatility surface
• machine learning
• FOMC announcements
• convolutional LSTM
• exogenous features