Deep learning enhanced volatility modeling with covariates

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Exogenous information such as policy news and economic indicators can have the potential to trigger significant movements in financial asset volatility. This article presents a model, called the RECH-X model, that allows incorporating exogenous variables into a recurrent neural network for volatility modeling and forecasting. The RECH-X model can allow for abrupt changes in the volatility level and effectively capture the complex serial dependence structure in the volatility dynamics. We demonstrate in a wide range of applications that the RECH-X model consistently outperforms the benchmark models in terms of volatility modeling and forecasting.