论文标题
通过转移学习，通过一维CNN预测波动率
Volatility Forecasting with 1-dimensional CNNs via transfer learning
论文作者
论文摘要
波动率是金融的自然风险措施，因为它可以量化股票价格的变化。数学金融中经常考虑的问题是预测波动率的不同估计值。使人们承诺将深度学习方法用于预测波动率的原因是，股票价格回报满足了一些共同的特性，称为“风格化事实”。同样，使用的数据量可能很高，有利于神经网络的应用。我们为数百种经常交易的股票使用了10年的每日价格，并比较了不同的CNN架构：某些网络仅使用考虑的股票，但我们尝试了一种建筑，该建筑对于培训而言，它使用了更多的系列，但不使用所考虑的股票。本质上，这是转移学习的应用，其性能在预测错误方面要好得多。我们还使用自动模型选择程序将扩张的因果CNN与经典Arima方法进行了比较。
Volatility is a natural risk measure in finance as it quantifies the variation of stock prices. A frequently considered problem in mathematical finance is to forecast different estimates of volatility. What makes it promising to use deep learning methods for the prediction of volatility is the fact, that stock price returns satisfy some common properties, referred to as `stylized facts'. Also, the amount of data used can be high, favoring the application of neural networks. We used 10 years of daily prices for hundreds of frequently traded stocks, and compared different CNN architectures: some networks use only the considered stock, but we tried out a construction which, for training, uses much more series, but not the considered stocks. Essentially, this is an application of transfer learning, and its performance turns out to be much better in terms of prediction error. We also compare our dilated causal CNNs to the classical ARIMA method using an automatic model selection procedure.
