Quantile Convolutional Neural Networks for Value at Risk Forecasting

G\'abor Petneh\'azi

Papers from arXiv.org

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.

Date: 2019-08, Revised 2020-09
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-for and nep-rmg
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