Quantile Convolutional Neural Networks for Value at Risk Forecasting
Machine Learning
2020-10-01 v4 Computational Finance
Machine Learning
Abstract
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.
Keywords
Cite
@article{arxiv.1908.07978,
title = {Quantile Convolutional Neural Networks for Value at Risk Forecasting},
author = {Gábor Petneházi},
journal= {arXiv preprint arXiv:1908.07978},
year = {2020}
}