Quantile Convolutional Neural Networks for Value at Risk Forecasting
This addresses risk management in finance, but it is incremental as it adapts an existing method to a specific task.
The authors tackled forecasting Value at Risk by modifying convolutional neural networks to predict arbitrary quantiles from asset price histories, resulting in fairly accurate forecasts.
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.