 Convolutional neural networks can do time series forecasting . They can learn local patterns in time , and they can be modified to learn only from the history (ignoring the future) and to use a long look-back window when doing so. A further simple modification enables them to forecast not the mean, but arbitrary quantiles of the distribution . And one last thing to make this all work: the CNN forecaster's complexity and flexibility requires much data, that is, preferably multiple time series. When this is met, the proposed QCNN framework can be competitive. It is demonstrated on a financial problem of huge practical importance: Value at Risk forecasting. By contributing to the stability of financial systems, deep learning may find one further way to improve our lives .
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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This article presents a new method for forecasting Value at Risk.
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, and they can be modified to learn only from the history (ignoring the future) and to use a long look-back window when doing so. A further
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. And one last thing to make this all work: the CNN forecaster's complexity and flexibility requires much data, that is, preferably multiple time series. When this is met, the proposed QCNN framework can be competitive. It is demonstrated on a financial problem of huge practical importance: Value at Risk forecasting. By contributing to the stability of financial systems, deep learning may find one further way to improve our lives
, 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