# Quantile Convolutional Neural Networks for Value at Risk Forecasting

arXiv: https://arxiv.org/abs/1908.07978 · HTML: https://mlsanity.com/paper/1908.07978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach](https://mlsanity.com/paper/2211.01498/) (2022)

## Abstract (arXiv)

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.