  Volatility forecasts play a central role among equity risk measures. Besides
traditional statistical models, modern forecasting techniques based on machine
learning can be employed when treating volatility as a univariate, daily
time-series. Moreover, econometric studies have shown that increasing the
number of daily observations with high-frequency intraday data helps to improve
volatility predictions. In this work, we propose DeepVol, a model based on
Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead
volatility. Our empirical findings demonstrate that dilated convolutional
filters are highly effective at extracting relevant information from intraday
financial time-series, proving that this architecture can effectively leverage
predictive information present in high-frequency data that would otherwise be
lost if realised measures were precomputed. Simultaneously, dilated
convolutional filters trained with intraday high-frequency data help us avoid
the limitations of models that use daily data, such as model misspecification
or manually designed handcrafted features, whose devise involves optimising the
trade-off between accuracy and computational efficiency and makes models prone
to lack of adaptation into changing circumstances. In our analysis, we use two
years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol.
Our empirical results suggest that the proposed deep learning-based approach
effectively learns global features from high-frequency data, resulting in more
accurate predictions compared to traditional methodologies and producing more
accurate risk measures.
