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- W3176183587 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." @default.
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- W3176183587 date "2021-12-01" @default.
- W3176183587 modified "2023-09-29" @default.
- W3176183587 title "Quantile convolutional neural networks for Value at Risk forecasting" @default.
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- W3176183587 doi "https://doi.org/10.1016/j.mlwa.2021.100096" @default.
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