Quantile Convolutional Neural Networks for Value at Risk Forecasting

Gábor Petneházi

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arXiv · 1908.07978

Quantile Convolutional Neural Networks for Value at Risk Forecasting

Gábor Petneházi

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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.

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BibTeX RIS

Gábor Petneházi. 2019-08-21. Quantile Convolutional Neural Networks for Value at Risk Forecasting. https://arxiv.org/abs/1908.07978

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