论文标题
LSTM的表现能超过波动性 - 经济学模型吗？
Can LSTM outperform volatility-econometric models?
论文作者
论文摘要
金融资产的波动性预测是了解财务风险和二次价格变化的基本问题之一。但是，尽管最近提出了许多新型的深度学习模型，但它们仍然“困难”超过强大的计量经济学波动率模型。为什么这样？波动性预测任务是由于噪声，市场微观结构，异性范围，新闻的外源性和不对称效应以及不同时间尺度的存在而具有非平凡的复杂性。在本文中，我们分析了长期记忆（LSTM）复发性神经网络的类别，以实现波动性预测的任务，并将其与强大的波动性 - 经济学模型进行了比较。
Volatility prediction for financial assets is one of the essential questions for understanding financial risks and quadratic price variation. However, although many novel deep learning models were recently proposed, they still have a "hard time" surpassing strong econometric volatility models. Why is this the case? The volatility prediction task is of non-trivial complexity due to noise, market microstructure, heteroscedasticity, exogenous and asymmetric effect of news, and the presence of different time scales, among others. In this paper, we analyze the class of long short-term memory (LSTM) recurrent neural networks for the task of volatility prediction and compare it with strong volatility-econometric models.
