  This article presents a new method for forecasting Value at Risk.
Convolutional neural networks can do time series forecasting, since they can
learn local patterns in time. A simple modification enables them to forecast
not the mean, but arbitrary quantiles of the distribution, and thus allows them
to be applied to VaR-forecasting. The proposed model can learn from the price
history of different assets, and it seems to produce fairly accurate forecasts.
