Abstract
Forecasting the option implied volatility (IV) surface is difficult with standard time-series models because of its time-varying granularity. We propose a new two-step real-time sequential forecasting framework. The first step fits the daily surface and can accommodate any underlying specification for option prices or IVs, including dynamic option-pricing models, nonparametric methods, and machine-learning techniques. In the second step, we sequentially estimate a dynamic IV model using an updating rule. Our framework can accommodate large datasets and high data frequencies. An empirical application on S&P 500 IV surfaces shows that our approach significantly outperforms random-walk forecasts.
