DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

Understand

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.