论文标题
波动率启发的$σ$ -LSTM单元格
Volatility-inspired $σ$-LSTM cell
论文作者
论文摘要
价格波动的波动率模型在《计量经济学文献》中进行了充分的研究，其中有50多年的理论和经验发现。深度学习领域的神经网络（NN）的最新进步自然提供了新颖的计量经济学建模工具。但是，仍然缺乏关于使用神经网络进行波动性建模的解释性和程式化的知识。使用程式化的事实可以帮助提高NN的性能进行波动性预测任务。在本文中，我们研究了如何将有关波动率过程的“物理学”的知识用作设计或限制长期短期记忆（LSTM）细胞状态的感应偏见，以预测波动性。我们引入了一种带有随机处理层的新型$σ$ -LSTM单元格，设计其学习机制并显示出良好的样本外预测性能。
Volatility models of price fluctuations are well studied in the econometrics literature, with more than 50 years of theoretical and empirical findings. The recent advancements in neural networks (NN) in the deep learning field have naturally offered novel econometric modeling tools. However, there is still a lack of explainability and stylized knowledge about volatility modeling with neural networks; the use of stylized facts could help improve the performance of the NN for the volatility prediction task. In this paper, we investigate how the knowledge about the "physics" of the volatility process can be used as an inductive bias to design or constrain a cell state of long short-term memory (LSTM) for volatility forecasting. We introduce a new type of $σ$-LSTM cell with a stochastic processing layer, design its learning mechanism and show good out-of-sample forecasting performance.
