Forecasting realized volatility with the implied volatility surface: an image-based approach

Jinting Yang; Wenjing Xia; Wuyi Ye

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A data transformation framework that converts the implied volatility (IV) surface into standardized matrices suitable for image-based deep learning models is developed.
We use convolutional neural networks (CNNs) to extract RV predictive factors from the IV matrices at three forecasting horizons, which we denote as CNN-IVS factors.
We evaluate the in-sample and out-of-sample RV forecasting performance of several HAR-type models augmented with CNN-IVS factors and conduct a range of robustness checks.
A series of numerical and visual interpretive analyses shows that CNNs extract surface-based features related to jump risk and downside risk, while also providing incremental predictive information beyond standard risk measures.

Abstract

This paper explores how to extract information about an asset’s future risk from the entire implied volatility surface (IVS).We convert the IVS into a standardized matrix and model it with an image-based approach – namely, a convolutional neural network (CNN) – thereby establishing a direct link between the IVS and future realized volatility (RV). Moreover, the forecasts generated by the CNN model, designated the CNN-IVS factor, are integrated into a heterogeneous autoregressive model of realized volatility (HAR-RV) framework. The experimental results demonstrate that the extended HAR model with the CNN-IVS factor significantly enhances out-of-sample performance for RV forecasting. Further interpretive analyses show that the CNN model automatically extracts predictive information from the overall structure of the IVS, captures economically meaningful signals relating to jump risk and downside risk, and provides unique incremental information for volatility forecasting.

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